# Social Robots As Companions for Lonely Hearts: The Role of Anthropomorphism
and Robot Appearances

Yoonwon Jung1 and Sowon Hahn2 *This work was not supported by any
organization1Yoonwon Jung is with the Department of Psychology, Seoul National
University, Seoul 08826, South Korea<EMAIL_ADDRESS>Hahn is a Faculty
of the Department of Psychology, Seoul National University, Seoul 08826, South
Korea<EMAIL_ADDRESS>
###### Abstract

Loneliness is a distressing personal experience and a growing social issue.
Social robots could alleviate the pain of loneliness, particularly for those
who lack in-person interaction. This paper investigated how the effect of
loneliness on anthropomorphizing social robots differs by robot appearances,
and how it leads to the purchase intention of social robots. Participants
viewed a video of one of the three robots(machine-like, animal-like, and
human-like) moving and interacting with a human counterpart. The results
revealed that when individuals were lonelier, the tendency to anthropomorphize
human-like robots increased more than that of animal-like robots. The
moderating effect remained significant after covariates were included. The
increase in anthropomorphic tendency predicted the heightened purchase intent.
The findings imply that human-like robots induce lonely individuals’ desire to
replenish the sense of connectedness from robots more than animal-like robots,
and that anthropomorphic tendency reveals the potential of social robots as
real-life companions of lonely individuals.

##  I INTRODUCTION

The need to belong is fundamental to humans [1]. Its absence is felt as
loneliness, which arises when individuals feel their need for social
connection is not fulfilled to the desired level [2, 3, 4]. Loneliness
elevates stress hormone levels, increases depressive symptoms, and prompts
cognitive decline and dementia [5, 6, 7, 8]. It is also associated with higher
blood pressure, increased heart rate, and compounded immune system [9, 10, 11,
12]. These grave physical and mental declines lead to higher medical and
general practitioner costs [13, 14]. Given that loneliness is not only a
painful individual experience but an increasingly serious social problem,
tackling loneliness is critical.

Single-person households are chiefly at an increased risk of chronic
loneliness [15, 16]. Similarly, co-habitation and the quality of social
relationships had buffering effects on loneliness, suggesting that living
alone poses a significant risk [16]. When in-person communication is
restricted at home for individuals living alone, social robots that are
capable of socially meaningful interaction with humans could be a potential
source of connectedness [17, 18]. Social robot achieves meaningful
interactions with humans by imparting its own intentions and goals and
eliciting social responses, ranging from humans perceiving the robot as a
social agent to creating bonds with it [17, 18, 19]. Thus, social robots could
build companionship with humans, which could potentially have a positive
effect on lonely individuals who have an unmet need to feel a sense of
connectedness. Indeed, previous research has shown that social robots can
attenuate feelings of loneliness [20, 21, 22]

When humans interact with social robots, anthropomorphism plays an important
role in shaping positive experience. Anthropomorphism increases the human
counterparts’ likability and trust toward social robots [23, 24]. If a social
robot is more anthropomorphized by lonely individuals, it could induce a more
positive attitude toward social robots, which in turn influences not only the
likability and enjoyment of social robots but also the purchase intent.

Purchase intent is the individuals’ adoption intent of social robots in real-
life context. Previous research has found that anthropomorphism increases
customers’ probability of purchasing and that the likability or enjoyment
factor mediates this relationship [25, 26, 27, 28, 29]. However, such
associations were not tested on social robots. Investigating whether lonely
individuals’ anthropomorphic tendency predicts their purchasing intent
regarding social robots will weigh the potential of social robots as practical
companions for lonely individuals in need.

In light of the aforementioned reasons, investigating the impact of loneliness
on anthropomorphizing social robots carry both theoretical and practical
implications. Therefore, this paper investigated the effect of loneliness on
anthropomorphizing social robots, and how the anthropomorphizing tendency
predicts the purchase intent of social robots.

##  II Related Work

###  II-A Anthropomorphizing social robots

Humans anthropomorphize non-human agents by attributing minds to them [30].
Specifically, humans anthropomorphize non-human objects or agents by
attributing humanlike characteristics, motivations, intentions, and emotions
to them in response to their imagined or real behavior [31]. Previous research
defined anthropomorphism as mind perception or human essence attribution to
nonhuman entities [32, 33, 34, 35, 36, 37]. A group of scholars defined two
separate dimensions of humanness traits, which humans attribute to both humans
and nonhumans [32, 38, 39]. [38, 40] defined the two dimensions as human
nature(HN) and uniquely human nature(UHN). Characteristics of humans shared
with other animals are referred to as HN, while UHN is considered unique to
humans and not shared by any other species [38]. [41] also suggested two
dimensions of mind perception, which are agency and experience. Experience
represents bodily sensations and feelings, and agency refers to the ability to
intend and conduct actions. To a large extent, the notions of humanization and
mind perception resemble each other. Agency aligns with UHN dimensions,
whereas experience dovetails with the HN dimension [41, 42].

The two-dimensional model enables the investigation of the compartmentalized
features of anthropomorphism [43]. It has been reported that people saw a
typical human as high in both agency and experience, mammals as low in agency
but high in experience, and robots as high in agency but low in experience
[41, 42, 44]. It is also argued that attributing the perceived experience to
robots would help reduce their perceived machine-likeness, and give them a
more human-like bearing [36]. Therefore, this paper used perceived experience,
rather than perceived agency, as a human-likeness dimension that people
attribute to when anthropomorphizing social robots.

###  II-B The relationship between loneliness and anthropomorphizing social
robots

Previous research proposed a three-factor theory of anthropomorphism to
understand its mechanism from a psychological perspective, which are elicited
agent knowledge, effectance motivation, and sociality motivation [31, 45].
Among them, sociality motivation is the desire for social contact and
attachment, which implies that loneliness could influence the tendency to
anthropomorphize nonhuman agents. According to the theory, lonely individuals
compensate for unrealized connection needs by anthropomorphizing non-human
objects [46]. Indeed, a body of research showed that loneliness increases the
propensity to identify the human-like attributes of other entities [47, 46,
45, 34]. Lonely individuals generally held animals, technical gadgets, and
social robots to have more human-like mental states. Such a tendency was also
replicated in Korean samples [48, 49].

On the other hand, a recent study reported that trait loneliness lowered the
tendency to attribute humanness and the acceptance of human-like social robots
[36]. The authors suggested that social robots were possibly perceived as
unsettling by dispositionally lonely individuals, who are more prone to
interpret their social surroundings as threatening [50, 11, 36]. Also, when
the motivational factors of anthropomorphism are present, anthropomorphism is
facilitated; yet, when they are lacking, the tendency to anthropomorphize
nonhumans declines [30]. Given this, the authors also expected that lonely
participants were less motivated to efficiently engage or connect with others,
inhibiting the influence of the aforementioned two factors [36].

Therefore, there are conflicting findings on the association between
loneliness and the anthropomorphic tendency toward social robots. Although
lonely individuals generally anthropomorphized simple technical devices and
animals more [47, 46, 45, 48, 49], studies employing robots reported both
positive and negative associations between loneliness and anthropomorphism
[34, 36]. The inconsistency in the inclination to anthropomorphize social
robots regarding the impact of loneliness on anthropomorphism of robots needs
further investigation since relatively little research has been carried out on
robots.

###  II-C The role of robot appearances on the effect of loneliness in
anthropomorphizing social robots

The conflicting findings on the association between loneliness and the
anthropomorphic tendency toward social robots may, in fact, be natural. It is
worth noting that, embodied social robots can move and communicate in ways
that were previously reserved for live organisms only, unlike other non-human
devices or animals that lonely individuals showed increased anthropomorphic
tendency. Also, considering that social robots vary greatly in their physical
appearances, the tendency of lonely individuals to anthropomorphize social
robots may not be summarized into one unified inclination.

The appearance of robot is a perceptually salient feature that could affect
the anthropomorphism of robots. The research on anthropomorphizing social
robots has mostly used either machine-like robots or human-like robots [51,
34, 42, 36, 52, 53, 54, 43]. However, the purpose of anthropomorphism in
robotics is not to create artificial, ”human-like” agents, but rather to find
the best robotic form that could help facilitate human-robot interaction [55].
In such a perspective, more attention to animal-like robots is needed because
animal-like robots are widely being used as robotic pets, and robotic pets
have been reported to be effective in alleviating loneliness [20, 21, 22].

Therefore, we used three robots with varying appearances with human-like,
machine-like, and animal-like features to investigate whether the tendency of
lonely individuals to anthropomorphize social robots differs by the robot
appearances. Although the robot used in [36] had a head with two eyes and a
torso, the robot was generally more machine-like compared to that used in
[34]. This could indicate that machine-likeness decreases the lonely
individuals’ tendency to anthropomorphize. Moreover, since human-likeness is
more likely than animal-likeness to evoke anthropomorphism, we hypothesized
that lonely individuals would anthropomorphize animal-like robots less than
human-like ones. We predicted that the robots’ appearances will have a
moderating effect on this association with the following hypotheses.

> H1: The lonely individuals’ anthropomorphic tendency toward social robot
> will differ by social robots’ appearances.
> H1-1: Lonely individuals will show lower inclination to anthropomorphize
> machine-like robot than human-like robot.
> H1-2: Lonely individuals will show lower inclination to anthropomorphize
> animal-like robot than human-like robot.
>

###  II-D The effect of anthropomorphizing on the likability and purchase
intent of social robots

Social robots are reported to be effective in alleviating loneliness by
supplementing the social relationships that are otherwise perceived to be
lacking [20, 21, 22]. Such studies introduced robots in nursing homes or
shelters for the elderly and observed their effect on loneliness. However, for
social robots to be employed by lonely individuals in real-world settings, the
acceptance motivation of the user is a crucial factor to consider. Purchase
intention, in particular, goes beyond the mere potential to use them without
considering the practical conditions but indicates whether people are willing
to pay for the robots to be used in their real, daily lives. Thus,
investigating how anthropomorphizing social robots affect likability and
purchase intention will foreground the practical aspect of investigating the
relationship between loneliness and anthropomorphism.

Attributing human traits toward social robots has been reported to increase
the human counterparts’ likability and trust levels [24, 23, 56]. Moreover,
many prior studies revealed that anthropomorphism has significant effects on
behavioral intentions in a consumer context. Specifically, anthropomorphism is
reported to increase purchase intent [25, 27, 28, 29], and perceived sense of
enjoyment or likability mediates this relationship [25, 26]. However, the
association was tested on non-robotic anthropomorphic products, web design,
and disembodied chatbots. Therefore, further research is needed to examine
whether the relationship between anthropomorphism and purchase intent, along
with the mediation effect of likability, can also be applied to the embodied
social robot.

Also, whether lonely individuals’ greater anthropomorphizing of social robots
leads to increased purchase intent is yet to be tested. Although one study
examined the moderating effect of loneliness in anthropomorphism predicting
consumer attitudes [26], loneliness was not the main variable of interest.
Moreover, the anthropomorphized subjects used in the study were brands and
advertisements, not social robots. Therefore, there is a need to explore the
consequence of lonely individuals’ anthropomorphizing propensities regarding
social robots in the market context. If the effect of loneliness on
anthropomorphizing social robots varies by robot appearances, such differences
could eventually lead to differences in purchase intent toward robots with
various appearances. As a result of the aforementioned rationale, we propose
the following hypotheses.

> H2: Higher anthropomorphism will lead to increased purchase intent of social
> robots via increased likability of social robots.
> H3: Robot appearances will moderate the indirect effect of loneliness on the
> purchase intent of social robots through anthropomorphism.
>

##  III Study Design

We conducted an online-based study where the participants viewed human-robot
interaction videos of one of the three robots (human-like, machine-like,
animal-like) and rated their anthropomorphic perceptions, likability, and
purchase intent. Considering that social robots are robotic agents that can
communicate with humans, showing the interactive skills of the robots to
participants is crucial to measuring the users’ perception of social robots.
Moreover, users are more likely to see dynamic robots rather than static
robots in our daily life. Therefore, we used human-robot interaction video as
a stimuli.

###  III-A Participants

A total of 185 individuals participated. They were recruited using the
participant recruitment system of Seoul National University, as well as from
the students and alumni communities of both Seoul National University and
Korea University. This study design was approved by Seoul National University
Institutional Review Board. Data collection took place between January and May
of 2022.

Participants responded online through the survey built using Qualtrics.
Therefore, we conducted extensive quality-control to strictly judge the
quality of responses. Participants who failed one of the two attention-check
items (i.e. “For this question, please check ‘strongly agree’”) or completed
the survey too quickly were not considered to be fully engaged in the study
[47]. First, those who failed the attention check were not able to complete
the survey, because the experiment ended immediately if one had the wrong
response to it. Also, based on the average time needed to fully watch the
video and attentively respond to the questionnaires, which is calculated from
a pilot study, we concluded that a proper response needs at least 320 seconds.
Therefore, we excluded the responses that took less than 320 seconds to
complete.

###  III-B Materials

####  III-B1 Robots

We used DJI Robomaster S1, Softbank Robotics’ NAO, and SONY’s AIBO for robots
with machine-like, human-like, and animal-like appearances, respectively (Fig.
1). Robot stimuli were viewed as between-group designs by participants, where
participants were randomly assigned to view one of the three robots.

Figure 1: Robots of three robot appearance conditions

####  III-B2 Human-robot interaction videos

The interactions between each robot and a human counterpart were recorded as
videos. The robots engaged with their human counterparts according to the same
interaction scenario across all three conditions; robots learned a series of
new behavior, succeeded in memorizing them, and received positive feedback
(Fig. 2). Robots responded 1.8 seconds after the human voice, and the lengths
of videos were around 90 seconds.

Figure 2: Flow chart of human-robot interaction procedure and examples of
human-robot interaction

Three robots reflected different levels of lifelikeness during the
interaction. The machine-like robot turned its LED light on or off, emited a
basic alarm sound, rotated its upper body, or moved in any direction to
respond. The animal-like robot produced dog-like sounds and imitated the
movements of real dogs through dynamic motions which consisted of behaviors
like ear flopping, head movement, mouth opening, tail wagging, sitting, and
lowering its front paw. The human-like robot answered using spoken words and
conveyed nonverbal cues by moving its head or limbs. Specifically, in response
to the human counterparts’ praise, each robot was programmed to show distinct
positive responses. The machine-like robot blinked its LED light faster, made
an alarm-like sound twice, and quickly rotated left and right. The animal-like
robot wagged its tail barking and lowered its head and front paw to induce
petting from the human counterpart. Lastly, the human-like robot showed a
proud gesture and said ”thank you!”. Programming those behaviors, we enabled
the robots to be perceived as social entities with varying socially
interactive abilities.

We used Softbank Robotics’ programming tool Choreographe [57] and Robomaster
desktop application to program NAO and Robomaster S1, respectively. The basic
functions of AIBO were used without programming. For the final version of
videos, We filmed NAO and Robomaster executing the pre-programmed behaviors
without human commands and added Korean human speech afterwards. For AIBO, we
filmed the interaction using Japanese commands and replaced the commands with
Korean human speech.

####  III-B3 Self-reported measures

To assess loneliness, we used the Korean-validated version of the UCLA
loneliness scale version 3 [58, 58]. We used 4-point Likert scales, ranging
from ‘never’ to ‘often’, to indicate how often each statement explains their
feelings. The internal reliability was 0.94.

To assess the degree of anthropomorphism in each robot, we used a scale based
on the 2-dimensional model of mind perception [41]. A study suggested that
several items had less discernible loadings on both dimensions [59], and
another study used the reduced version of the scales consisting of items with
more distinguishable factor loadings on each dimension [60]. Therefore, we
left out a few items that were relatively vague in determining the two
dimensions, resulting in 6 items for each dimension. Seven-point scales
ranging from 1 to 7 was used to rate how much the robot appears to possess
various mental abilities. The internal reliability was 0.85 for perceived
experience and 0.75 for perceived agency.

To measure robot likability, 3-items differential scale was used [61]. Seven-
point scales ranging from -3 to 3 was used to rate which point among
adjectives at both extremes best represents the degree of characteristic felt
by the robot, such as ’unlikable - likable’. The internal reliability was 0.9.

Lastly, we used a single-item measure of purchase intent. Participants used
7-point scales ranging from 1 to 7 to rate how much do they want to purchase
the robot. We also measured variables that could affect the associations among
the main variables: age, gender, household income, and prior knowledge of
robots [62, 51, 63, 64, 65, 53].

###  III-C Procedure

Participants were recruited using the research title modified for blinding;
‘The Effect of Stress Level and Recall of Negative Experiences on Evaluation
of the Robot’. They completed questionnaires measuring loneliness and stress
as filler questions to match the blind research topic. Then each participant
was randomly assigned to one of three conditions that reflected three levels
of robot appearances. Participants viewed a video of a robot moving and
interacting with a human counterpart and completed questionnaires measuring
their perception of the robot. The study ended after responding to questions
measuring demographic variables, along with the questions asking about their
prior knowledge and familiarity with the robot, and participants who completed
the study were given the promised reward.

##  IV Results

Responses from 137 participants (${N}_{female}$=84, ${M}_{age}$=22.803,
${SD}_{age}$=3.788) were selected as final data for analyses. Participants
were randomly assigned to 3 robot appearance conditions; animal-like (_N_
=46), human-like (_N_ =45), and machine-like (_N_ =46) conditions. The average
loneliness score was 2.094 (_SD_ =0.584), and the average loneliness level of
each robot appearances condition was 2.013 (_SD_ =0.552) for human-like robot
condition, 2.214 (_SD_ =0.638) for machine-like robot condition, and 2.052
(_SD_ =0.550) for animal-like robot condition.

TABLE I: Bootstrapped regression results on robot appearances and loneliness as predictors for perceived experience | _$\beta$_ | _SE_ | 95% _BCa CI_
---|---|---|---
Intercept | -0.101 | 0.147 | [-0.341, 0.235]
Robot1(machine-like=1) | -0.147 | 0.185 | [-0.537, 0.200]
Robot2(animal-like=1) | 0.470* | 0.221 | [0.045, 0.896]
Loneliness | 0.197 | 0.161 | [-0.053, 0.587]
Robot1-loneliness interaction | -0.288 | 0.192 | [-0.699, 0.060]
Robot2-loneliness interaction | -0.500* | 0.260 | [-1.080, -0.056]
Note: Robot1 and Robot2 are contrasts of robot appearance levels (Robot1;
machine-like robot compared to human-like robot, Robot2;animal-like robot
compared to human-like robot).
All variables were standardized.
Bootstrapped _$R^{2}$_ value was 0.122 (95% _BCa CI_ = [0.025, 0.219]).
*= statistically significant at 95% confidence level TABLE II: Bootstrapped regression results on robot appearances and loneliness as predictors for perceived experience with covariates | _$\beta$_ | _SE_ | 95% _BCa CI_
---|---|---|---
Intercept | -0.060 | 0.166 | [-0.361, 0.287]
Age | 0.054 | 0.109 | [-0.152, 0.288]
Gender(male=1) | -0.169 | 0.177 | [-0.532, 0.176]
Income | -0.033 | 0.086 | [-0.215, 0.133]
Prior knowledge on robot | 0.175* | 0.092 | [0.013, 0.375]
Robot1(machine-like=1) | -0.095 | 0.166 | [-0.487, 0.291]
Robot2(animal-like=1) | 0.485* | -0.220 | [0.062, 0.938]
Loneliness | 0.167 | 0.160 | [-0.130, 0.521]
Robot1-loneliness interaction | -0.253 | 0.199 | [-0.653, 0.127]
Robot2-loneliness interaction | -0.496* | 0.249 | [-1.070, -0.060]
Note: Robot1 and Robot2 are contrasts of robot appearance levels (Robot1;
machine-like robot compared to human-like robot, Robot2;animal-like robot
compared to human-like robot).
All variables were standardized.
Bootstrapped _$R^{2}$_ value was 0.153 (95% _BCa CI_ = [0.054, 0.242]).
*= statistically significant at 95% confidence level Figure 3: The effect of loneliness on perceived experience by robot appearances Note: All variables were standardized.

Bootstrapped multiple regression analyses were conducted to verify the impact
of loneliness on robot anthropomorphism and the moderation effect of robot
appearances (Tab. I-II and Fig. 3). In R, CAR and boot package were used for
bootstrapping regression coefficients, R-squared values, and their BCa 95%
confidence intervals [66, 67]. Robot appearances were coded as two dummy
variables; robot1(machine-like robot compared to human-like robot) and
robot2(animal-like robot compared to human-like robot). We used 2000
iterations with seeds set as 123 for reproducibility.

There were no significant unique effect of loneliness (_$\beta$_ =.115, _SE_
=.180, _CI_ =[-.250, .476]), nor the significant moderating effects of robot
appearances on perceived agency (robot1-loneliness interaction: _$\beta$_
=-.170, _SE_ =.227, _CI_ =[-.599, .291]; robot2-loneliness interaction:
_$\beta$_ =-.303, _SE_ =.231, _CI_ =[-.758, .168]).

The unique effect of loneliness on perceived experience was not significant
(_$\beta$_ =.197, _SE_ =.161, _CI_ =[-.053, .587]). On animal-like robot,
loneliness was significantly more negatively correlated with perceived
experience than human-like robot (robot2-loneliness interaction: _$\beta$_
=-.500, _SE_ =.260, _CI_ =[-1.080, -.056]). The interaction effect was not
significant when machine-like robot was compared to human-like robot
(robot1-loneliness interaction: _$\beta$_ =-.288, _SE_ =.192, _CI_ =[-.699,
.060]). When covariates were introduced, the tendency of animal-like robots to
exhibit a stronger negative relationship between loneliness and perceived
experience than human-like robots persisted (robot2-loneliness interaction:
_$\beta$_ =-.496, _SE_ =.249, _CI_ =[-1.070, -.060]).

Also, we tested the indirect and direct effect of anthropomorphism on purchase
intent using PROCESS macro in R [68]. We included prior knowledge of robots,
gender, and income as covariates, which were the significant factors in Tab.
II. We used 1000 iterations with seeds set as 100. Both perceived experience
and perceived agency positively predicted the purchase intent via likability,
while only perceived experience had a positive direct effect on purchase
intent. See Tab. III for the detailed results.

TABLE III: Effects of anthropomorphism on purchase intent mediated by likability on social robot |  |  Indirect Effect | Direct Effect
---|---|---|---
Perceived Experience | _$\beta$_ |  0.135* | 0.250*
| _SE_ |  0.046 | 0.069
| 95% _BCa CI_ |  [0.043, 0.233] | [0.115, 0.386]
Perceived Agency | _$\beta$_ |  0.225* | 0.069
| _SE_ |  0.545 | 0.075
| 95% _BCa CI_ |  [0.119, 0.332] | [-0.081, 0.218]
Note: All variables were standardized. Prior knowledge of robots, age, gender,
and income were the covariates. Figure 4: Overview of the results of the
PROCESS model7 Note: Robot1 and Robot2 are contrasts of robot appearance
levels (Robot1; machine-like robot compared to human-like robot,
Robot2;animal-like robot compared to human-like robot.) Solid lines indicate
significant relationships, while dotted lines indicate non-significant
relationships. All variables were standardized. Prior knowledge of robots,
age, gender, and income were the covariates.

Lastly, we tested the final model using PROCESS macro in R [68]. We used 1000
iterations with seeds set as 100. Prior knowledge of robots, age, gender, and
income were included as covariates. Since the direct effect of
anthropomorphism on purchase intent was significant, likability was excluded
from the model for simplicity. Also, we included perceived experience, the
indicator of social robot anthropomorphism, as a mediator of the model. When
perceived experience was mediated, the conditional indirect effects were not
significant in any levels of robot appearances. The moderated mediation was
significant when animal-like robot was compared to human-like robot. See Tab.
IV and Fig. 4 for the moderated mediation results.

TABLE IV: Summary of the Results of the PROCESS model7 Moderated mediation test _$\beta$(SE)_, 95% _BCa CI_ |  Conditional Indirect Effect _$\beta$(SE)_, 95% _BCa CI_ |  Direct Effect _$\beta$(SE)_, 95% _BCa CI_
---|---|---
Robot1 | Robot2 | Human-like | Machine-like | Animal-like
-0.099(0.080), CI=[-0.277, 0.035] |  -0.194*(0.108), CI=[-0.445, -0.016] |  0.065(0.064), CI=[-0.045, 0.203] |  -0.034(0.045), CI=[-0.124, 0.057] |  -0.129(0.084), CI=[-0.308, 0.015] |  0.047(0.083), CI=[-0.117, 0.210]
Note: Prior knowledge of robots, age, gender, and income were the covariates
of the models.
All variables were standardized.
*= statistically significant at 95% confidence level

##  V Discussion

Lonely individuals showed lower tendency to anthropomorphize animal-like robot
than human-like robot, and such inclination remained significant after
covariates were introduced in the model. Thus, H1 and H1-2 was supported. The
rejection of H1-1 implies that the machine-like robot did not differ from
human-like robot in inducing lonely individuals to perceive it as human-like.
However, although not statistically significant, the regression coefficient
revealed that while loneliness and anthropomorphism were positively associated
in the human-like robot condition, the association was negative in the
machine-like robot condition. This implies that the degree of machine-likeness
could partly explain the inconsistency in lonely individuals’
anthropomorphizing tendency toward social robots [34, 36].

H1-2 being supported suggests that the animal-like robots’ mimicking of animal
behaviors less induces social motivation in lonely people and therefore reduce
the desire to anthropomorphize compared to human-like features of social
robots. Since AIBO, the animal-like robot used in this study, was designed to
be a ‘cute companion dog’ for the users by ’showing moves and gestures in
hundreds of adorable patterns’ [69], the robot mimics not only the appearances
of a real dog but also closely reflects the movements, sounds, and behaviors
of a dog longing to be petted. For lonely people who are hypervigilant to
social threats [50, 11], too much dog-like behaviors may have been perceived
as unsettling or even threatening [69].

Perceived experience increased the purchase intent of social robots, both with
and without the mediation of likability. The final analysis of PROCESS model7
integratively showed that although there was no conditional indirect impact of
loneliness on purchase intent, the mediated moderation index was significant
when animal-like robot was compared to human-like robot. The mediating effect
significantly decreased in animal-like robot than in human-like robot,
reflecting the H1-2 being supported. Therefore, H2 and H3 were supported. H2
and H3 being supported suggest that lonely individuals being motivated to seek
a sense of connectedness from social robots perceived social robots to be more
human-like, which in turn led to them viewing more value in the acquisition of
a human-like social robot [26].

##  VI Implications and Future Directions

Our work makes several theoretical contributions. Studies probing the
association between loneliness and anthropomorphism chiefly used human-like
robots, and recent versions of robots with more sophisticated appearances were
not tested [51, 34, 36]. This paper is the first to use animal-like and
machine-like robots, along with human-like robots, and compare the lonely
individuals’ anthropomorphizing tendencies across robot appearances. Moreover,
this paper revealed that lonely individuals’ inclination to anthropomorphize
social robots is not uniform across all robot appearances. This indicates that
the inclination of lonely individuals to attribute human-like qualities to
social robots should be evaluated in the context of specific agent
characteristics, rather than being simplified as a general inclination to
anthropomorphize all social robots. Lastly, our work extends the effect of
lonely individuals’ anthropomorphic tendencies on social robots to their
adoption intent as consumers. Combined with the moderating effect of robot
appearances, our results strengthen the implication of studying lonely
individuals’ anthropomorphic tendencies toward social robots.

This study also has practical implications. First, the results suggest the
potential of social robots, particularly human-like robots, as real-life
companions of lonely individuals. We made the case for a deeper examination of
anthropomorphic tendencies in lonely individuals, as lonely individuals
exhibited higher anthropomorphic tendencies and eventually higher purchase
intentions toward human-like robots than animal-like robots. The robots could
be helpful for people with lower in-person communication, where social robots
could be the potential source to replenish the need for social interactions.
Furthermore, our findings provide implications for social robot design. In
previous research, only [36] reported that lonely individuals demonstrated a
decreased anthropomorphic tendency. One possible explanation is that the
uncanny valley phenomenon may have occurred due to perceptual mismatch, which
happens when realism degrees of the humanlike and artificial elements are
discordant [70]. Moreover, although lonely individuals are more likely to seek
connectedness from nonhuman entities [46, 45, 31], it should also be noted
that lonely individuals are more susceptible to perceiving threats from social
environments [50, 11]. Thus, for robots to provide a potential source of
companionship for lonely individuals, they should be designed as evoking
anthropomorphism that is compatible with its artificialness, and non-
threatening. We suggest that social robots demonstrate suitable levels of
movement and interaction skills so they do not appear incongruous with their
look or overly mimic any existing beings.

Although the findings and implications of this study are valuable, they should
be regarded with caution due to some limitations. First, the responses were
collected using online studies due to COVID-19 safety concerns. Although we
conducted extensive quality control measures, the participants might still not
have been actively involved in watching the human-robot interaction video.
Moreover, the participants did not watch the human-robot interaction on-site.
Therefore, future studies could reexamine the hypotheses with on-site
experiments. With subjects physically interacting with the robot in laboratory
settings, the impact of loneliness on anthropomorphism, likability, and
purchase intent could be examined, and the increased effect of on-site
engagement with social robots could also be evaluated. Furthermore, the
relationships could be tested in an experimental field study, increasing the
results’ ecological validity.

## References

  * [1] R. Baumeister and M. Leary, “The need to belong: Desire for interpersonal attachments as a fundamental human motivation,” _Psychological Bulletin_ , vol. 117, no. 3, pp. 497–529, 1995.
  * [2] J. T. Cacioppo and W. Patrick, _Loneliness: Human Nature and the Need for Social Connection_. WW Norton & Company, 2008.
  * [3] D. Perlman and L. A. Peplau, “Toward a social psychology of loneliness,” _Personal Relationships_ , vol. 3, pp. 31–56, 1981.
  * [4] G. R. Perry, “Loneliness and coping among tertiary-level adult cancer patients in the home,” _Cancer Nursing_ , vol. 13, no. 5, pp. 293–302, 1990.
  * [5] E. K. Adam, L. C. Hawkley, B. M. Kudielka, and J. T. Cacioppo, “Day-to-day dynamics of experience–cortisol associations in a population-based sample of older adults,” _Proceedings of the National Academy of Sciences_ , vol. 103, no. 45, pp. 17 058–17 063, 2006.
  * [6] J. T. Cacioppo, L. C. Hawkley, J. M. Ernst, M. Burleson, G. G. Berntson, B. Nouriani, and D. Spiegel, “Loneliness within a nomological net: An evolutionary perspective,” _Journal of Research in Personality_ , vol. 40, no. 6, pp. 1054–1085, 2006.
  * [7] T. J. Holwerda, A. T. Beekman, D. J. Deeg, M. L. Stek, T. G. van Tilburg, P. J. Visser, B. Schmand, C. Jonker, and R. A. Schoevers, “Increased risk of mortality associated with social isolation in older men: only when feeling lonely? results from the amsterdam study of the elderly (amstel),” _Psychological Medicine_ , vol. 42, no. 4, pp. 843–853, 2012.
  * [8] R. J. McQuaid, S. M. L. Cox, A. Ogunlana, and N. Jaworska, “The burden of loneliness: Implications of the social determinants of health during covid-19,” _Psychiatry Research_ , vol. 296, p. 113648, 2021.
  * [9] J. Alun and B. Murphy, “Loneliness, social isolation and cardiovascular risk,” _British Journal of Cardiac Nursing_ , vol. 14, no. 10, pp. 1–8, 2019\.
  * [10] J. T. Cacioppo, L. C. Hawkley, and G. G. Berntson, “The anatomy of loneliness,” _Current directions in Psychological Science_ , vol. 12, no. 3, pp. 71–74, 2003.
  * [11] L. C. Hawkley and J. T. Cacioppo, “Loneliness matters: A theoretical and empirical review of consequences and mechanisms,” _Annals of Behavioral Medicine_ , vol. 40, no. 2, pp. 218–227, 2010.
  * [12] L. C. Hawkley, R. A. Thisted, C. M. Masi, and J. T. Cacioppo, “Loneliness predicts increased blood pressure: 5-year cross-lagged analyses in middle-aged and older adults,” _Psychology and Aging_ , vol. 25, no. 1, p. 132, 2010.
  * [13] T. L. Barnes, S. MacLeod, R. Tkatch, M. Ahuja, L. Albright, J. A. Schaeffer, and C. S. Yeh, “Cumulative effect of loneliness and social isolation on health outcomes among older adults,” _Aging & Mental Health_, pp. 1–8, 2021.
  * [14] R. Meisters, D. Westra, P. Putrik, H. Bosma, D. Ruwaard, and M. Jansen, “Does loneliness have a cost? a population-wide study of the association between loneliness and healthcare expenditure,” _International Journal of Public Health_ , vol. 66, 2021.
  * [15] J. M. Groarke, E. Berry, L. Graham-Wisener, P. E. McKenna-Plumley, E. McGlinchey, and C. Armour, “Loneliness in the uk during the covid-19 pandemic: Cross-sectional results from the covid-19 psychological wellbeing study,” _PloS One_ , vol. 15, no. 9, p. e0239698, 2020.
  * [16] M. Pinquart and S. Sörensen, _Risk Factors for Loneliness in Adulthood and Old Age–A Meta-Analysis_. Hauppauge, NY: Nova Science Publishers, 2003, vol. 19, pp. 111–143.
  * [17] C. Breazeal, “Toward sociable robots,” _Robotics and Autonomous Systems_ , vol. 42, no. 3-4, pp. 167–175, 2003.
  * [18] C. Breazeal and B. Scassellati, “How to build robots that make friends and influence people,” in _Proceedings 1999 IEEE/RSJ International Conference on Intelligent Robots and Systems. Human and Environment Friendly Robots with High Intelligence and Emotional Quotients (Cat. No.99CH36289)_ , vol. 2, 1999, pp. 858–863 vol.2.
  * [19] K. M. Lee, N. Park, and H. Song, “Can a robot be perceived as a developing creature? effects of a robot’s long‐term cognitive developments on its social presence and people’s social responses toward it,” _Human communication research_ , vol. 31, no. 4, pp. 538–563, 2005.
  * [20] M. R. Banks, L. M. Willoughby, and W. A. Banks, “Animal-assisted therapy and loneliness in nursing homes: Use of robotic versus living fogs,” _Journal of the American Medical Directors Association_ , vol. 9, no. 3, pp. 173–177, 2008.
  * [21] M. Kanamori, M. Suzuki, and M. Tanaka, “Maintenance and improvement of quality of life among elderly patients using a pet-type robot,” _Japanese Journal of Geriatrics_ , vol. 39, no. 2, pp. 214–218, 2002.
  * [22] H. Robinson, B. MacDonald, N. Kerse, and E. Broadbent, “The psychosocial effects of a companion robot: A randomized controlled trial,” _Journal of the American Medical Directors Association_ , vol. 14, no. 9, pp. 661–667, 2013\.
  * [23] F. Eyssel, F. Hegel, G. Horstmann, and C. Wagner, “Anthropomorphic inferences from emotional nonverbal cues: A case study,” in _19th International Symposium in Robot and Human Interactive Communication_ , 2010, pp. 646–651.
  * [24] E. J. De Visser, S. S. Monfort, R. McKendrick, M. A. Smith, P. E. McKnight, F. Krueger, and R. Parasuraman, “Almost human: Anthropomorphism increases trust resilience in cognitive agents,” _Journal of Experimental Psychology: Applied_ , vol. 22, no. 3, p. 331, 2016.
  * [25] M. C. Han, “The impact of anthropomorphism on consumers’ purchase decision in chatbot commerce,” _Journal of Internet Commerce_ , vol. 20, no. 1, pp. 46–65, 2021.
  * [26] P. Hart and M. B. Royne, “Being human: How anthropomorphic presentations can enhance advertising effectiveness,” _Journal of Current Issues & Research in Advertising_, vol. 38, no. 2, pp. 129–145, 2017.
  * [27] D. Laksmidewi, H. Susianto, and A. Z. Afiff, “Anthropomorphism in advertising: The effect of anthropomorphic product demonstration on consumer purchase intention,” _Asian Academy of Management Journal_ , vol. 22, no. 1, pp. 1–25, 2017.
  * [28] S. Wölfl, J. M. Feste, and L. D. K. Peters, “Is somebody there? anthropomorphic website design and intention to purchase from online stores,” in _AMCIS 2019 Proceedings_ , vol. 18, 2019.
  * [29] C. Yen and M.-C. Chiang, “Trust me, if you can: a study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments,” _Behaviour & Information Technology_, vol. 40, no. 11, pp. 1177–1194, 2021.
  * [30] N. Epley, J. Schroeder, and A. Waytz, _Motivated Mind Perception: Treating Pets as People and People as Animals_. New York, NY: Springer New York, 2013, pp. 127–152. [Online]. Available: https://doi.org/10.1007/978-1-4614-6959-9_6
  * [31] N. Epley, A. Waytz, and J. T. Cacioppo, “On seeing human: A three-factor theory of anthropomorphism,” _Psychological Review_ , vol. 114, no. 4, p. 864–886, 2007.
  * [32] F. Eyssel, D. Kuchenbrandt, and S. Bobinger, “Effects of anticipated human-robot interaction and predictability of robot behavior on perceptions of anthropomorphism,” in _Proceedings of the 6th International Conference on Human-Robot Interaction_ , ser. HRI ’11. New York, NY, USA: Association for Computing Machinery, 2011, p. 61–68. [Online]. Available: https://doi.org/10.1145/1957656.1957673
  * [33] F. Eyssel, D. Kuchenbrandt, F. Hegel, and L. de Ruiter, “Activating elicited agent knowledge: How robot and user features shape the perception of social robots,” in _2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication_ , 2012, pp. 851–857.
  * [34] F. Eyssel and N. Reich, “Loneliness makes the heart grow fonder (of robots): On the effects of loneliness on psychological anthropomorphism,” in _Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction_ , ser. HRI ’13. IEEE Press, 2013, p. 121–122.
  * [35] H. Kamide, F. Eyssel, and T. Arai, “Psychological anthropomorphism of robots,” in _Social Robotics_ , G. Herrmann, M. J. Pearson, A. Lenz, P. Bremner, A. Spiers, and U. Leonards, Eds. Cham: Springer International Publishing, 2013, pp. 199–208.
  * [36] S. Li, L. Xu, F. Yu, and K. Peng, “Does trait loneliness predict rejection of social robots? the role of reduced attributions of unique humanness (exploring the effect of trait loneliness on anthropomorphism and acceptance of social robots),” in _Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction_ , ser. HRI ’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 271–280. [Online]. Available: https://doi.org/10.1145/3319502.3374777
  * [37] X. Zhou, S. Kim, and L. Wang, “Money helps when money feels: Money anthropomorphism increases charitable giving,” _Journal of Consumer Research_ , vol. 45, no. 5, pp. 953–972, 2018.
  * [38] N. Haslam, S. Loughnan, Y. Kashima, and P. Bain, “Attributing and denying humanness to others,” _European Review of Social Psychology_ , vol. 19, no. 1, pp. 55–85, 2008.
  * [39] J. Złotowski, E. Strasser, and C. Bartneck, “Dimensions of anthropomorphism : From humanness to humanlikeness,” in _2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI)_ , 2014, pp. 66–73.
  * [40] S. Loughnan and N. Haslam, “Animals and androids: Implicit associations between social categories and nonhumans,” _Psychological Science_ , vol. 18, no. 2, pp. 116–121, 2007.
  * [41] H. M. Gray, K. Gray, and D. M. Wegner, “Dimensions of mind perception,” _Science_ , vol. 315, no. 5812, pp. 619–619, 2007.
  * [42] K. Gray and D. M. Wegner, “Feeling robots and human zombies: Mind perception and the uncanny valley,” _Cognition_ , vol. 125, no. 1, pp. 125–130, 2012\.
  * [43] J. Złotowski, H. Sumioka, C. Bartneck, S. Nishio, and H. Ishiguro, “Understanding anthropomorphism: Anthropomorphism is not a reverse process of dehumanization,” in _Social Robotics_ , A. Kheddar, E. Yoshida, S. S. Ge, K. Suzuki, J.-J. Cabibihan, F. Eyssel, and H. He, Eds. Cham: Springer International Publishing, 2017, pp. 618–627.
  * [44] T. S. Rai and D. Diermeier, “Corporations are cyborgs: Organizations elicit anger but not sympathy when they can think but cannot feel,” _Organizational Behavior and Human Decision Processes_ , vol. 126, pp. 18–26, 2015.
  * [45] N. Epley, A. Waytz, S. Akalis, and J. T. Cacioppo, “When we need a human: Motivational determinants of anthropomorphism,” _Social Cognition_ , vol. 26, no. 2, pp. 143–155, 2008.
  * [46] N. Epley, S. Akalis, A. Waytz, and J. T. Cacioppo, “Creating social connection through inferential reproduction: Loneliness and perceived agency in gadgets, gods, and greyhounds,” _Psychological Science_ , vol. 19, no. 2, pp. 114–120, 2008.
  * [47] J. A. Bartz, K. Tchalova, and C. Fenerci, “Reminders of social connection can attenuate anthropomorphism: A replication and extension of epley, akalis, waytz, and cacioppo (2008),” _Psychological Science_ , vol. 27, no. 12, pp. 1644–1650, 2016.
  * [48] H. I. Shin, “If i only have my cat in this world: Impacts of loneliness on anthropomorphism and dehumanization,” _Korean Journal of the Science of Emotion & Sensibility_, vol. 23, no. 2, pp. 23–34, 2020.
  * [49] H. I. Shin and J. Kim, “My computer is more thoughtful than you: Loneliness, anthropomorphism and dehumanization,” _Current Psychology_ , vol. 39, no. 2, pp. 445–453, 2020.
  * [50] J. T. Cacioppo and L. C. Hawkley, “Perceived social isolation and cognition,” _Trends in Cognitive Sciences_ , vol. 13, no. 10, pp. 447–454, 2009.
  * [51] C. R. Crowell, J. C. Deska, M. Villano, J. Zenk, and J. T. Roddy Jr, “Anthropomorphism of robots: study of appearance and agency,” _JMIR Human Factors_ , vol. 6, no. 2, p. e12629, 2019.
  * [52] I. Saltik, D. Erdil, and B. A. Urgen, _Mind Perception and Social Robots: The Role of Agent Appearance and Action Types_. Association for Computing Machinery, 2021, p. 210–214. [Online]. Available: https://doi.org/10.1145/3434074.3447161
  * [53] K. C. Yam, Y. E. Bigman, P. M. Tang, R. Ilies, D. De Cremer, H. Soh, and K. Gray, “Robots at work: People prefer—and forgive—service robots with perceived feelings,” _Journal of Applied Psychology_ , vol. 106, no. 10, pp. 1557–1572, 2020.
  * [54] J. A. Złotowski, H. Sumioka, S. Nishio, D. F. Glas, C. Bartneck, and H. Ishiguro, “Persistence of the uncanny valley: the influence of repeated interactions and a robot’s attitude on its perception,” _Frontiers in psychology_ , vol. 6, p. 883, 2015.
  * [55] J. Fink, “Anthropomorphism and human likeness in the design of robots and human-robot interaction,” in _Social Robotics_ , S. S. Ge, O. Khatib, J.-J. Cabibihan, R. Simmons, and M.-A. Williams, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 199–208.
  * [56] S. Kiesler and J. Goetz, “Mental models of robotic assistants,” in _CHI ’02 Extended Abstracts on Human Factors in Computing Systems_ , ser. CHI EA ’02. New York, NY, USA: Association for Computing Machinery, 2002, p. 576–577. [Online]. Available: https://doi.org/10.1145/506443.506491
  * [57] E. Pot, J. Monceaux, R. Gelin, and B. Maisonnier, “Choregraphe: a graphical tool for humanoid robot programming,” in _RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication_ , 2009, pp. 46–51.
  * [58] E. J. Jin and S. H. Hwang, “The validity of the korean-ucla loneliness scale version 3,” _Korean Journal of Youth Studies_ , vol. 26, no. 10, pp. 53–80, 2019.
  * [59] B. Malle, “How many dimensions of mind perception really are there?” in _Proceedings of the 41st Annual Meeting of the Cognitive Science Society_. Cognitive Science Society, 2019, pp. 2268–2274.
  * [60] H. I. Shin, “Who has a mind?: Mind perception and moral decision toward robots,” _Journal of Social Science_ , vol. 32, no. 1, pp. 195–213, 2021\.
  * [61] S. Brave, C. Nass, and K. Hutchinson, “Computers that care: Investigating the effects of orientation of emotion exhibited by an embodied computer agent,” _International Journal of Human-Computer Studies_ , vol. 62, no. 2, pp. 161–178, 2005.
  * [62] M. Blut, C. Wang, N. V. Wünderlich, and C. Brock, “Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other ai,” _Journal of the Academy of Marketing Science_ , vol. 49, no. 4, pp. 632–658, 2021.
  * [63] H. N. Green, M. M. Islam, S. Ali, and T. Iqbal, “Who’s laughing nao? examining perceptions of failure in a humorous robot partner,” in _Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction_ , ser. HRI ’22. IEEE Press, 2022, p. 313–322.
  * [64] D. Halpern and J. E. Katz, “Unveiling robotophobia and cyber-dystopianism: The role of gender, technology and religion on attitudes towards robots,” in _2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)_ , 2012, pp. 139–140.
  * [65] D. C. May, K. J. Holler, C. L. Bethel, L. Strawderman, D. W. Carruth, and J. M. Usher, “Survey of factors for the prediction of human comfort with a non-anthropomorphic robot in public spaces,” _International Journal of Social Robotics_ , vol. 9, no. 2, pp. 165–180, 2017.
  * [66] A. C. Davison and D. V. Hinkley, _Bootstrap Methods and their Application_. Cambridge University Press, 1997.
  * [67] J. Fox and S. Weisberg, _An R Companion to Applied Regression_ , 3rd ed. SAGE Publications, 2018.
  * [68] A. F. Hayes. (2022) The process macro for spss, sas, and r. [Online]. Available: https://www.processmacro.org/index.html
  * [69] SONY. (2022) Tricks and gestures. [Online]. Available: https://us.aibo.com/feature/feature1.html
  * [70] J. Kätsyri, K. Förger, M. Mäkäräinen, and T. Takala, “A review of empirical evidence on different uncanny valley hypotheses: support for perceptual mismatch as one road to the valley of eeriness,” _Frontiers in Psychology_ , p. 390, 2015.