Social robots should be encouraged and incorporated into daily life

Why Would I Use This in My Home? A Model of
Domestic Social Robot Acceptance

Maartje M. A. de Graaf,
1
Somaya Ben Allouch,

2
and Jan A. G. M. van Dijk

1

1
University of Twente, the Netherlands

2
Saxion University of Applied Science, the Netherlands

Many independent studies in social robotics and human–robot interaction
have gained knowledge on various factors that affect people’s perceptions
of and behaviors toward robots. However, only a few of those studies
aimed to develop models of social robot acceptance integrating a wider
range of such factors. With the rise of robotic technologies for everyday
environments, such comprehensive research on relevant acceptance fac-
tors is increasingly necessary. This article presents a conceptual model of
social robot acceptance with a strong theoretical base, which has been
tested among the general Dutch population (n = 1,168) using structural
equation modeling. The results show a strong role of normative believes
that both directly and indirectly affect the anticipated acceptance of social
robots for domestic purposes. Moreover, the data show that, at least at
this stage of diffusion within society, people seem somewhat reluctant to
accept social behaviors from robots. The current findings of our study

Maartje M.A. de Graaf ([email protected], https://robonarratives.wordpress.com) is a
behavioral scientist with an interest in people’s social, emotional, and cognitive responses to robots along
with the societal and ethical consequences of such responses. Currently she is a postdoctoral research
associate at the Department of Cognitive Linguistic and Psychological Sciences of Brown University.
Somaya Ben Allouch ([email protected], https://www.saxion.nl/gezondheidwelzijnentechnolo
gie/site/onderzoek/technologie/lector/lector/) is an Associate Professor with an interest in adoption
and acceptance of new technologies in everyday life. She is the chair of the Technology, Health & Care
research group at the Saxion University of Applied Science. Jan A. G. M. van Dijk (j.a.g.m.vandij-
[email protected], https://www.utwente.nl/bms/mco/en/emp/dijk/) is a social scientist with an interest
in the social aspects of new media, the network society, and the digital divide. He is Professor of
Communication Science and the of the Information Society, and director of the Center for
eGovernment Studies at the University of Twente.

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/HHCI.
© M. M. A. de Graaf, S. B. Allouch, and J. A. G. M. van Dijk
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCom-
mercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-
commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited,
and is not altered, transformed, or built upon in any way.

HUMAN–COMPUTER INTERACTION, 2019, Volume 34, pp. 115–173
Published with license by Taylor & Francis Group, LLC
ISSN: 0737-0024 print / 1532-7051 online
DOI: https://doi.org/10.1080/07370024.2017.1312406

115

https://robonarratives.wordpress.com

https://www.saxion.nl/gezondheidwelzijnentechnologie/site/onderzoek/technologie/lector/lector/

https://www.saxion.nl/gezondheidwelzijnentechnologie/site/onderzoek/technologie/lector/lector/

https://www.utwente.nl/bms/mco/en/emp/dijk/

http://www.tandfonline.com/HHCI

https://crossmark.crossref.org/dialog/?doi=10.1080/07370024.2017.1312406&domain=pdf&date_stamp=2019-01-08

and their implications serve to push the field of acceptable social robotics
forward. For the societal acceptance of social robots, it is vital to include
the opinions of future users at an early stage of development. This way
future designs can be better adapted to the preferences of potential users.

CONTENTS

1. INTRODUCTION
2. EVALUATING RELEVANT ACCEPTANCE MODELS

2.1. Reviewing Traditional Models of Technology Acceptance
2.2. Reviewing Existing Models for Social Robot Acceptance
2.3. Reviewing the Theory of Planned Behavior
2.4. Toward a Model of Social Robot Acceptance

3. INFLUENTIAL FACTORS FOR SOCIAL ROBOT ACCEPTANCE
3.1. Attitudinal Beliefs Structure
3.2. Normative Beliefs Structure
3.3. Control Beliefs Structure
3.4. The Conceptual Model

4. METHOD
4.1. Sampling of Participants
4.2. Design of the Questionnaire
4.3. The Measurement Model

Establishing the First-Order Factor Model
Establishing the Second-Order Factor Model

5. RESULTS
5.1. Interpreting the Effects of the Attitudinal Beliefs
5.2. Interpreting the Effects of the Normative Beliefs
5.3. Interpreting the Effects of the Control Beliefs

6. GENERAL DISCUSSION
6.1. Implications

Influential Factors for Social Robot Acceptance
The Unwanted Sociability of Robots
Practical Implications for the Development of Social Robots

6.2. Limitations
6.3. Conclusion

1. INTRODUCTION

The economic prospects of the robotics market are rapidly expanding. In
2013, approximately 4 million service robots for personal and domestic use were
sold worldwide, and this number is expected to increase to 31 million by the end
of 2017 (International Federation of Robotics, 2014). However, the increasing
presence of robots in our everyday lives will not simply be accepted unreservedly
by human users. Research in robotics suggests that the mere presence of robots

116 de Graaf et al.

in everyday life automatically increases neither their chances of being accepted nor
the willingness of users to interact with them (Bartneck et al., 2005), which is a
major challenge for the success of social robots. Although there have been many
studies in the field of social robotics regarding the various factors affecting
people’s perceptions of and behavior toward robots, only a few aimed to develop
models of social robot acceptance. As researchers focus more on developing
robotic technologies for everyday environments, more comprehensive studies on
the factors relating to their acceptance are increasingly necessary. Furthermore,
the inclusion of future users during the early stages of design is important for
developing socially robust, rather than merely acceptable, robotic technologies
(Sabanovic, 2010). Therefore, the goal of this article is to present a conceptual
model of social robot acceptance for domestic purposes and to test it using
structural equation modelling (SEM). We begin by evaluating the current accep-
tance model and then present the theoretical framework of our conceptual model
within the theory of planned behavior (TPB). Thereafter we describe several
influential factors for social robot acceptance in domestic environments, resulting
in our proposed conceptual model. We then outline our research methods,
including the establishment of the measurement model. Following this, we pre-
sent the test results of our conceptual model, along with its hypotheses. This
article concludes with the implications for social robot acceptance in domestic
environments and how our model could serve to advance the field of social
robotics.

2. EVALUATING RELEVANT ACCEPTANCE MODELS

Applying existing acceptance models from human–computer interaction to the
field of social robotics without modification is problematic, because robot technology
is far more complex than other technological devices (Flandorfer, 2012). With robots
recognizing our faces, making eye contact, and responding socially, they are pushing
our Darwinian buttons by displaying behavior associated with sentience, intentions,
and emotions (Turkle, 2011). Therefore, some researchers have argued that robots
should be regarded as a new technological genre (de Graaf, 2016; Kahn, Gary, &
Shen, 2013; Young, Hawkins, Sharlin, & Igarashi, 2007). In this section, we review
the most prominent models applied to technology acceptance in general, then
critically reflect on the few existing models developed specifically for social robot
acceptance. We later conclude that we need to deviate from these models in the
development of our conceptual model of social robot acceptance. As we argue in
what follows, we suggest building on the framework of TPB. Because we acknowl-
edge that TPB has its shortcomings, which we elaborate next, the final part of this
section provides suggestions for improvement on our conceptual model of social
robot acceptance.

Model of Domestic Social Robot Acceptance 117

2.1. Reviewing Traditional Models of Technology Acceptance

The technology acceptance model (TAM) developed by Davis (1989) is con-
sidered the most influential and commonly applied theory for describing an indivi-
dual’s acceptance of information systems (Y. Lee, Kozar, & Larsen, 2003). The
widespread popularity of TAM is broadly attributable to three factors. First, it is a
parsimonious and IT-specific model, designed to adequately explain and predict the
acceptance of a wide range of systems and technologies among a diverse population
of users across varying organizational and cultural contexts and expertise levels.
Second, the TAM model has a strong theoretical base and a well-researched, validated
inventory of psychometric measurement scales, which makes its use operationally
appealing. Third, the model has accumulated strong empirical support for its overall
explanatory power and has emerged as a preeminent model of users’ acceptance of
technology (Yousafzai, Foxall, & Pallister, 2007a). TAM views user acceptance as
being dependent upon the perceived usefulness of the technology and its perceived
ease of use. The model was first developed by Davis (1989) to provide validated
measurement scales for predicting the user acceptance of computers, as these
subjective measures were not yet validated and their relationships to systems use
unknown. The model adopts a causal chain of beliefs, attitudes, intention, and
behavior, introduced previously by social psychologists (Ajzen, 1991; Fishbein &
Ajzen, 1975). Based on certain beliefs, people form attitudes about a specific object,
the basis upon which they form an intention to behave regarding that object. Here,
the effects of the outcome variables end at intention to use, or even at attitude
toward use. In TAM, the only predictor of actual system use is behavioral intention.
Although TAM has been found to be a useful predictor of acceptance behavior in
numerous contexts, it does not provide a mechanism for the inclusion of other salient
beliefs (Benbasat & Barki, 2007). The literature suggests that other factors may play a
role in explaining use behavior, including expected outcomes and habits (LaRose &
Eastin, 1994), motives to use a technology (Katz, Blumler, & Gurevitch, 1973), or
environmental factors (Bandura, 1977). As a result, many recent studies focused on
the elaboration of the model, including those undertaken by Davis and his colleagues
(e.g., Davis, 1989; Venkatesh & Davis, 2000). A review of TAM-related research
shows that many determinants of perceived usefulness and perceived ease of use
have been discovered (Y. Lee et al., 2003). Therefore, the creators of TAM expanded
their original model, resulting in the introduction of a second edition of TAM
(Venkatesh & Davis, 2000) and later a third edition (Venkatesh & Bala, 2008).
However, even this third edition of their model is still somewhat limited.

TAM is a very economical model that does not specifically include other
external factors, besides usefulness and ease of use. Moreover, the model presumes
that all external factors are moderated by the evaluation of usefulness and ease of use.
However, many studies adopting the principles of TAM have demonstrated that
several other factors directly influence behavioral intentions and actual behavior (see
Y. Lee et al., 2003, for a summary). Indeed, the relation between perceived useful-
ness, perceived ease of use, and use behavior may be more complex and less linear

118 de Graaf et al.

than reflected by TAM. As depicted in TPB (Ajzen, 1991), social influence, facilitat-
ing conditions (Venkatesh, Morris, Davis, & Davis, 2003), and habitual use (Ouellette
& Wood, 1998; Triandis, 1979) have also been found to explain actual use directly,
and not, as the original TAM assumes, to only mediate it through usefulness and ease
of use. In addition, TAM assumes that technology use is directly accepted or not
accepted independently of other factors preventing individuals from using a technol-
ogy. However, many situational factors, such as lack of time, money, or experience,
can prevent individuals from using a technology (Mathieson, Peacock, & Chin, 2001).
Other researchers argue that the overly simple conceptualization and operationaliza-
tion of the constructs of usefulness and ease of use have prevented researchers from
understanding the internal workings of these central constructs within TAM (Benba-
sat & Barki, 2007). These examples indicate that the acceptance of domestic social
robots is more complex and less linear than the limited TAM model suggests, raising
objections against its applicability for the investigation of social robot acceptance in
domestic environments.

One of the most prominently applied models of technology acceptance is the
unified theory of acceptance and use of technology (UTAUT), developed by the
same researchers who worked on the TAM modifications (Venkatesh et al., 2003). In
developing UTAUT, the researchers reviewed and consolidated the constructs of
eight theoretical models, employed in previous research, to explain information
systems use behavior (i.e., theory of reasoned action [TRA], TAM, motivational
model, TPB, a combined TPB/TAM, model of personal computer use, diffusion
of innovations theory, and social cognitive theory). In building this eclectic model, the
researchers chose an empirical, rather than theoretical, approach. Of all these theore-
tical constructs, only those shown to have the highest significant effect in an
empirical study investigating the user acceptance of an information system were
picked for their model. UTAUT holds that performance expectancy, effort expec-
tancy, social influence, and facilitating conditions are direct determinants of use
intention and actual use. Gender, age, experience, and voluntariness of use are
posited to moderate the impact of these four key constructs on use intention and
actual use. The inclusion of moderators in the model is reminiscent of a social
psychological approach.

The effects of the independent variables thus do not spread beyond the user’s
intention to use, and the single predictor of actual system use is behavioral intention.
Obviously, there are both advantages and limitations to UTAUT’s utilization in
acceptance research. An advantage is its holistic approach to explaining many psy-
chological and social factors that impact technology acceptance, together with the
consistent validity and reliability of data collection through the instrument (Yoo, Han,
& Huang, 2012). However, despite being an eclectic model that combines highly
correlated variables to create an extremely high explained variance (Yoo et al., 2012),
UTAUT is criticized for not being parsimonious enough, because it requires several
variables to achieve a substantial level of explained variance (Straub & Burton-Jones,
2007). Parsimony, the goal of which is to identify factors accounting for the most
variation, is to be greatly valued (Burgoon & Buller, 1996), but not at the expense of

Model of Domestic Social Robot Acceptance 119

explanatory power. UTAUT does not explain the different underlying mechanisms,
although such an explanation would make the unified model more suitable for
explaining the user’s general opinions about expected use, rather than explaining
the user’s motivations relating to the continued and increased adoption of a particular
technology (Peters, 2011). Another disadvantage is that, even though the founders of
the model are working toward extending the original model to a second edition
(Venkatesh, Thong, & Xu, 2012), both measurements of social influence and facil-
itating conditions are not robustly constructed. These concepts are quite complex but
are measured with only two items. In addition, by adding social influence and
facilitating conditions to the original technology acceptance model, we are essentially
faced with a model that is not very different from the model of planned behavior
theory. The two constructs of social influence and facilitating conditions from
UTAUT overlap considerably with the constructs of subjective norm and perceived
behavioral control from TPB. Moreover, the original TAM and UTAUT constructs
were merely developed for utilitarian systems and were validated in a working
environment. The applicability of these models on hedonic systems or more plea-
sure-oriented systems is limited (van der Heijden, 2004). Yet the use of social robots
in domestic environments could result in an experience that goes beyond its utility.
These robotic systems have been observed to evoke a social reaction from its users
(Kahn, Friedman, Perez-Granados, & Freier, 2006; K. Lee, Park, & Song, 2005;
Reeves & Nass, 1996). In addition, the context in which these models have been
validated (i.e., in the working environment) is not congruent with our study’s
objective, which is social robot acceptance in domestic environments. This suggests
that other models may be more appropriate for the development of a model of
acceptance for domestic social robots.

2.2. Reviewing Existing Models for Social Robot Acceptance

To our knowledge, only two user acceptance models for social robots have been
proposed to date using SEM. The current most cited model of social robot accep-
tance is the Almere model of Heerink, Kröse, Evers, and Wielinga (2010). Shin and
Choo (2011) presented an alternative acceptance model for social robots. Although
these models offer useful insights into the factors influencing social robot acceptance,
they show some weakness regarding its general application in the domestic context.
First, both the Almere model (Heerink et al., 2010) and the acceptance model for
socially interactive robots (Shin & Choo, 2011) have their roots in UTAUT. As
previously indicated, UTAUT is not considered to be parsimonious (Straub &
Burton-Jones, 2007), and it is an eclectic model that combines highly correlated
variables to create an unnaturally high explained variance (Yoo et al., 2012). In what
follows, we argue that TPB offers a more suitable theoretical base for a model of
social robot acceptance that focuses on individual adoption behavior in a domestic
environment. Second, both models have been tested only on specific user groups.
The Almere model (Heerink et al., 2010) has been developed for the acceptance of

120 de Graaf et al.

socially interactive agents in the eldercare facilities context, and the acceptance model
for socially interactive robots (Shin & Choo, 2011) has been tested on a sample of
students. This limits the generalizability of these models to other user groups and
contexts. Our study focuses on the general population and social robot use within the
domestic context, for which the two existing models have not yet been validated.
Third, both models are based on grouped findings from previous research in human–
robot interaction (HRI) and human–computer interaction. They lack both a theore-
tical foundation and strong arguments for the inclusion of the chosen factors in the
model and the exclusion of other factors. Fourth, the SEM, used to test the Almere
model, was performed on a data set that consisted of a combined dataset from four
separate studies. Similarly, the acceptance model for socially assistive robots (Shin &
Choo, 2011) is based on different groups of participants, who used different types of
robots with varying functionalities. Neither of the two studies statistically confirmed
any similarities between the data sets to justify merging their samples into one data set
to test their models. A final shortcoming of the Almere model can be found in the
application of the model modification indices, which were accepted without any
theoretical support. Based on the deficiencies of both models, we decided to deviate
from these existing models by proposing a new model for social robot acceptance,
conceptualized within a strong theoretical foundation.

2.3. Reviewing the Theory of Planned Behavior

Because our focus is mainly on psychological aspects of individual users, we
have chosen to build on an existing theory from a psychological perspective. We use
the TPB (Ajzen, 1991) as a starting point in the development of our proposed model.
We chose TPB as a guiding framework because (a) it is particularly suitable for
explaining and predicting volitional behaviors, including technology acceptance
(Mathiesson, 1991; S. Taylor & Todd, 1995; Venkatesh & Brown, 2001); (b) it has
been successfully applied to explain a wide range of behaviors (Ajzen, 1991); and (c)
its origin invites researchers to extend the model to adapt to a specific behavior
(Ajzen, 1991). Moreover, when considering use intention as the main outcome
variable to explain future use of a new technology—in this case, social robots—the
explanatory power of TPB is greater than that of TAM and its successors, especially
when it is decomposed to a specific technology (S. Taylor & Todd, 1995). Therefore,
TPB provides a solid basis for the development of a conceptual model to investigate
social robot acceptance from an individual perspective.

TPB, which is an extension of TRA (Ajzen & Fishbein, 1980), has been one of
the most influential, well-researched theories in explaining and predicting behavior
across a variety of settings (Manstead & Parker, 1995). As a general model, it is
intended to provide a parsimonious explanation of informational and motivational
influences on most human behavior and can therefore be used to predict and
understand human behavior (Ajzen, 1991). The TPB approach is embedded in
expectancy-value models of attitudes and decision making, with an underlying logic

Model of Domestic Social Robot Acceptance 121

that the expected personal and social outcomes of a particular action influence the
intention to behave in a certain way (Manstead & Parker, 1995). According to TPB,
the main determinant of a behavior is a behavioral intention, which in turn is
determined by attitude, subjective norms, and perceived behavioral control. Attitude
captures an individual’s overall evaluation of performing the behavior, whereas
subjective norms refer to an individual’s perception of the expectations of important
others about the specific behavior. Because the achievement of behavioral goals is
not always completely under volitional control, Ajzen (1991) added a third concept to
the prediction of behavior, namely, perceived behavior control. Perceived behavioral
control is an individual’s perceived ease or difficulty in performing the behavior and is
conceptually related to Bandura’s (1977) self-efficacy. The concept of perceived
behavioral control may include both internal (e.g., skills, knowledge, adequate plan-
ning) and external (e.g., facilitating conditions, availability of resources) factors.

Despite its success in behavior research (Manstead & Parker, 1995), a flaw of
TPB’s original model and the hypothesized relations between its constructs is that
only moderate correlations exist between the global and belief measures of its
constructs (Benbasat & Barki, 2007). This means that these concepts are not strongly
related and other factors may influence the formation of people’s beliefs about a
certain behavior. Moreover, the model suggests correlations between attitudes, sub-
jective norms, and perceived behavioral control (Ajzen, 1991), which result in a lack
of knowledge regarding the precise nature of the relations between these concepts.
Meta-analytic reviews on TPB (e.g., Armitage & Conner, 2001; Sheppard, Hartwick,
& Warshaw, 1988) indicate that a substantial proportion of the variance of behavior
intention remains unexplained by the core variables of attitudinal beliefs, subjective
norms, and perceived behavior control. This has led some researchers to postulate
that other factors play a role in explaining and predicting human behavior (Bentler &
Speckart, 1981). TPB has also been challenged for its claim that attitude, subjective
norms, and perceived behavioral control are the sole antecedents of intentions.

The critics can be divided into four groups: (a) those who challenge the lack of
emotional components in the model, (b) those who criticize the sole focus on social
pressure in the social components in the model, (c) those who criticize the assump-
tion that all behaviors are consciously performed, and (d) those who argue that a lot
of behavior is a result of habitual routines. Next we focus on these criticisms and
explain how we address these shortcomings in our conceptual model of domestic
social robot acceptance.

First, TPB is challenged for the lack of emotional components in the model, as
it mainly focuses on cognitive or instrumental components and neglects affective
evaluations or emotional aspects of human behavior (Bagozzi et al., 2001). However,
although both concepts are highly correlated, they can be empirically discriminated
and have different functions in explaining or predicting human behavior (Breckler &
Wiggins, 1989; Greenwald, 1989). Human behavior is not purely rational. In fact,
emotions are intertwined in the determination of human behavioral reactions to
environmental and internal events that are very important to the needs and goals
of an individual (Izard, 1977). Many researchers believe that it is impossible for

122 de Graaf et al.

humans to act or think without the involvement of, at least subconsciously, our
emotions (Mehrabian & Russell, 1974). Indeed, rational evaluations and forming
expectations, as well as nonrational attitudes, feelings, and other affective or emo-
tional-related concepts have been acknowledged by researchers to influence human
behavior (Limayem & Hirt, 2003; Manstead & Parker, 1995; Richard, Plicht, & Vries,
1995; Sun & Zhang, 2006). If emotions affect human behavior in general, they might
be relevant for HRI research as well. Several studies have indicated that people react
emotionally when confronted with robots. People are more aroused after watching a
robot being tortured than when watching a robot being petted (Rosenthal–von der
Pütten et al., 2013). Moreover, people’s negative attitudes toward robots decreased
significantly after interacting with robots, which in turn explained the significant
variance in the overall rating of the robot (Stafford et al., 2010). As negative emotions
are naturally unpleasant, people tend to perform corrective behaviors or avoid bad
behaviors to mitigate them (Izard, 1977). This reasoning reflects the importance of
including emotions as a factor influencing human behavior. Therefore, in addition to
utilitarian attitudes that entail the more rational evaluation of the behavior, we include
hedonic attitudes that compose the emotional components of the behavior as
determinants of social robot acceptance in our model.

Second, TPB has a narrow conceptualization and focuses solely on social pressure
experienced when making decisions about human behavior (Rivis & Sheeran, 2003;
Sheeran & Orbell, 1999). Previous studies have largely used subjective norms to capture
the essence of social influence, but their inconsistent findings have led some researchers
to question whether these reflect the full extent of social influence (Y. Lee, Lee, & Lee,
2006). Therefore, the link between social influence and technology acceptance requires
further investigation (Karahanna & Limayem, 2000). Only a few empirical studies have
investigated the underlying components of normative beliefs (Fisher & Price, 1992), and
some researchers have suggested the introduction of further dimensions to TPB to tap
the complete function of normative beliefs in explaining human behavior (Fisher & Price,
1992; Sheeran & Orbell, 1999). Therefore, further exploration regarding additional
factors that better explain the normative component is needed. Our model of social
robot acceptance, which splits the normative component into a personal and a social
element, attempts to achieve this.

Third, although these additions and alterations to the theory provide greater
insights into the rational-based and deliberate nature of behavior, its assumption that
people consciously act in a certain way could be problematic. In general, psycholo-
gical research originates from goal-directed human behavior and relies on expectancy-
value models of attitudes and decision making, which are rooted in theories of
rational choice. TPB may be considered one of the most influential models in this
perspective (Aarts, Verplanken, & van Knippenberg, 1998). However, humans are the
only animal species with the ability for metacognition or to reflect on their actions
and their thoughts (Cartwright-Hatton & Wells, 1997). For example, when a ball is
thrown at someone, their reflex will most likely be to catch the ball without thinking
about the action. Similarly, our environment is capable of activating goal-directed
behavior automatically, without an individual’s awareness (Bargh & Gollwitzer, 1994).

Model of Domestic Social Robot Acceptance 123

Thus, not all human behavior is part of a conscious …