From “Made In” to Mukokuseki: Exploring the Visual Perception of
National Identity in Robots
KATIE SEABORN†
School of Engineering, Tokyo Institute of Technology, Tokyo, Japan, seaborn.k.aa@m.titech.ac.jp

HARUKI KOTANISchool of Engineering, Tokyo Institute of Technology, Tokyo, Japan, kotani.h.ab@m.titech.ac.jp

PETER PENNEFATHER

gDial, Inc., Toronto, Ontario, Canada, p.pennefather@gmail.com

People read human characteristics into the design of social robots, a visual process with socio-cultural implications. One
factor may be nationality, a complex social characteristic that is linked to ethnicity, culture, and other factors of identitythat can be embedded in the visual design of robots. Guided by social identity theory (SIT), we explored the notion of“mukokuseki,” a visual design characteristic deﬁned by the absence of visual cues to national and ethnic identity in Japanesecultural exports. In a two-phase categorization study (n=212), American (n=110) and Japanese (n=92) participants rated arandom selection of nine robot stimuli from America and Japan, plus multinational Pepper. We found evidence of made-in
and two kinds of mukokuseki eﬀects. We oﬀer suggestions for the visual design of mukokuseki robots that may interact
with people from diverse backgrounds. Our ﬁndings have implications for robots and social identity, the viability of robotic
exports, and the use of robots internationally.
CCS CONCEPTS • Human-centered computing → Human computer interaction (HCI) • Computer systems
organization → Embedded and cyber-physical systems; Robotics • Social and professional topics → User
characteristics; Race and ethnicity, Cultural characteristics.
KEYWORDS: Robots, Nationality, Ethnicity, Mukokuseki, Anthropomorphism, Social identity theory, Culture

1

INTRODUCTION

Robots are being taken up in a variety of contexts with a diverse array of people around the world. Robots
produced in one country are being exported to other countries at small and large scales. The collaboration
between Aldebaran in France and SoftBank in Japan, for instance, led to the development of Pepper, one of themost recognizable social robots used in research and daily life. Many robots, like Pepper, have a humanoid formor feature some level of anthropomorphism in their visual design. These visual cues may be “designed in” tothe robot’s appearance on purpose or without the intention (or realization) of the designer. Still, they areperceivable and interpreted by people in relation to human models and especially stereotypes, often
unconsciously but with implications for the interaction with the robot [22, 39, 43]. This highlights a modern
challenge in reconciling the increasing “entanglement” of social and material worlds [21], where we andpossibly our robotic creations are participating in social categorization, identification, and comparison
activities, i.e., social identity dynamics [27, 49]. Understanding whether and how these visual cues and resulting
categorizations influence people's attitudes and behaviour towards robots has become a key area of interest
within industry and the academy, especially in light of recent calls for action on designing for diversity withinrobotics and adjacent spaces [23, 47, 53, 62].
At a fundamental level, the visual design of robots, especially socially interactive, anthropomorphic ones,
may be understood through the socio-cultural context in which the robot was designed and wherein the person
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that
copies are not made or distributed for proﬁt or commercial advantage and that copies bear this notice and the full citation on the ﬁrst page.
Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permied. To copy
otherwise, or republish, to post on servers or to redistribute to lists, requires prior speciﬁc permission and/or a fee. Request permissions
from permissions@acm.org© 2024 Copyright held by the owner/author(s).
ACM 2573-9522/2024/8-ARThps://doi.org/10.1145/3681782

ACM Trans. Hum.-Robot Interact.

2

is interacting with the given robot. Visual cues to humanlikeness that are embedded, purposefully or not, in thephysical design of a robot may be read as human social characteristics [14, 54]. These may then be linked to
culturally-mediated models of human social groups, a phenomenon captured under the concept of social
identity theory (SIT) [27, 60, 61, 64]. Such social categories and characteristics include gender [9, 19, 41, 50],
race and ethnicity [7, 35, 52], nationality [19, 33, 54], emotional expression [20, 28, 58], nonverbal behaviours[13, 45, 65], and more. The degree to which people key into these visual cues as social categories and underwhat circumstances with robots is an ongoing question. Relatedly, the degree to which designers and roboticists
consciously embed these visual cues, as well as to what extent researchers, educators, and practitioners makeconscious choices about employing robots based on these visual cues, is typically unknown, with some evidence
to suggest this is unconsciously done or without awareness of the effects. A critical review of Pepper, forinstance, found a confusion of gender attributions by researchers and participants based on research reporting[50].
Another possibility is that these cues are vague by design. Mukokuseki is a Japanese concept about the visualdesign of characters and commodities whereby no trace of nationality, race, ethnicity, or culture is (meant tobe) perceivable: effectively, the appearance is “stateless,” a socio-cultural tabula rasa [1, 31, 32, 44]. A
mukokuseki approach may be purposefully employed, simply assumed, or an unconscious result of the design
process. Indeed, many products and cultural exports from Japan rely on the assumption of mukokuseki for
economic success internationally. Yet, it is not clear whether and to what degree this has been done or holds
true for robots, especially in the presence of intersecting factors, such as anthropomorphism. Moreover,mukokuseki has implications for diversity and representation, given that many robots are designed with forms
and shades that may be perceived as racial or ethnic cues [7, 53]. Even if designers purposefully employ amukokuseki approach or assume that a given robot is mukokuseki by design, this is not necessarily the casefor end-users. When it comes to user experience (UX), experts must ultimately accept that end-users may
interpret their creations differently than intended or expected [16]. The question of whether robots are truly
mukokuseki or possibly betray their “made in” origins [54] through visual cues un/knowingly embedded intheir morphologies is an open question.
To this end, we conducted a categorization study to evaluate whether and how robots were deemedmukokuseki based solely on their visual design, i.e., using photos of robots as visual stimuli. We asked: Does
the phenomenon of mukokuseki occur for robots? We recruited two cohorts from nations associated withrobots in industry and daily life—America and Japan—to rate a series of nine robots from each nation. Drawing
from SIT, we asked: RQ1: Do people interpret a robot’s appearance in line with its origins, i.e., are there
perceivable “made in” visual cues, or do they read in their own or no national, ethnic, and/or cultural
background, i.e., a mukokuseki effect? Nationality is a human concept and may be invoked by relatively
more humanlike designs. As such, we also varied the selection of robots by level of anthropomorphism, asking:RQ2: Does the degree of anthropomorphism influence the presence or absence of a mukokuseki effect?
Our main contributions are empirical, cross-cultural evidence of both mukokuseki and made-in effects thatwere not universal across the robots, national cohorts, or individuals, as well as the presence of two forms of
mukokuseki. A high degree of perceived anthropomorphism and individual perceptions of similarity appear toplay key roles. Our findings have implications for robotics research and practice, especially for visual designand cross-cultural initiatives.

2

BACKGROUND

2.1 Mukokuseki and Nationality, Race, Ethnicity, and Culture
People are diverse, and arguably our robotic creations should reflect that plurality. The challenge of designingsocial robots for diversity and a multiplicity of perspectives is grounded in the need to anticipate people’s
propensity to anthropomorphize machines and other objects. When people interact with non-human agents,
such as robots, that are designed to behave as if they can act with intelligence, foresight and intentions, they
will anthropomorphize the robot [6, 17, 39, 43]. We look for and interpret humanlike cues—not only visual, butalso auditory, gestural, and other modes of embodiment—as a means of understanding what the agent is andhow it may behave. There can be individual variability as well as group variability at all scales, and these may

ACM Trans. Hum.-Robot Interact.

3

intersect in complex and unexpected ways. Nevertheless, anthropomorphism is generally considered anaccessible strategy for understanding and anticipating socially intelligent robotic behaviour.
One aspect of anthropomorphism long overlooked relates to the robot’s “origins.” We capture this under theheading of “nationality,” a complex characteristic of people that refers to one’s legal and psycho-physicallocation/s within the human-occupied world of nations and boundaries, as well as cultural background, of
which there may be multiple, and racial and/or ethnic diversity in appearance, behaviour, attitudes, social
expression, social status, and more [3, 10, 12]. The degree to which nationality is tied to and intersects with
culture, race, and ethnicity can vary widely within and across nations as well as individual people. Notably,
these are all social constructs [52, 63], categories we have created to organize ourselves. For instance, critical
historians have argued that “race” was constructed to justify certain acts on and the removal of liberties forgroups of people who shared certain physical characteristics, cultures, and/or national origins [24]. More subtly,
we may assume that the more socially powerful groups represent everyone, i.e., white American men are the“default” [53, 56, 59], or attempt to evade the issue, despite un/known effects [5]. Growing recognition of social
power and representation as a matter of fairness has led to calls for critical reflection and engagement [47, 48,
53].
Mukokuseki offers another perspective. Iwabuchi [31] coined the term to describe the phenomena of thetransnational success of Japanese electronic consumer goods based on national “vagueness” in the design of thevisual appearance of the characters. “Mu” means absence, while “kokuseki” means nationality, i.e., the absenceof nationality. In short, mukokuseki is the notion that the visual designs of such cultural outputs have no cues
to nationality. This may be by design, i.e., a conscious design choice; by assumption, i.e., a premise of the design;
and/or a perception, i.e., a consequence of how the design is interpreted. According to Iwabuchi, mukokuseki
has two explicit premises: statelessness, or lacking cues to national identity, and trans-cultural adaptivity,
whereby anyone from any nation or culture can readily accept the goods as their own. This potential for aplurality, rather than an absence, of national identity prescriptions was raised by Adamowicz [1], whosuggested a complementary term: takokuseki, where instead of “mu” we have “ta” to mean “multiplicity.” As
implied by Iwabuchi, we cannot know whether mukokuseki is a conscious design choice or assumption without
asking the designers, and we typically do not have this information. Designers may also not employ
mukokuseki consciously. In any case, whether mukokuseki has been achieved—no cues to nationality plus
adoptability by anyone from anywhere—is ultimately the domain of end-user perceptions [16]. This is what
motivated us to start our exploration with a user perceptions study, rather than interviews with designers.
We must also consider a hidden premise about what “nationality” can mean: not only national identity, butalso cultural, racial, and/or ethnic identity. Japan, for instance, is not a mononational, monocultural, monoracial,or monoethnic nation. Ethnic groups with Japanese nationality include the Burakku, Indigenous Ainu, zainichi
Koreans, as well as immigrants from around the world. Yet, the national identity of Japan is largely constructedin a “mono” way around the majority ethnic population: the Yamato or Wajin [38]. This, alongside the current
global context of who has social power, notably histories of white Western imperialism and colonialism, andthe post-war influence of the West, especially the US, on Japan [46], raises questions about whether and forwhom mukokuseki occurs. Notably, while mukokuseki emerged from Japanese theorizing about Japanesecultural exports, it may be a general concept that has potential applications elsewhere, i.e., for robots and othernations.
Indeed, a small but growing body of work has discovered implications of nationality, culture, and race and/or
ethnicity for robots. Spatola et al. [54] primed people to key into human stereotypes about national identities
by labeling certain robots as having certain national identities. These stereotypes, such as about national
warmth, held true for robots, indicating that a “made in” effect could be induced simply by assigning national
identities to any robot. We go one step further by refraining from priming the national identities explicitly,
exploring whether robots, in the absence of primes, are “stateless” or mukokuseki. Marchesei, Roselli, andWykowska [36] considered how the national identity and cultural orientations towards collectivism influenced
perceptions of a robot, finding that individual differences favouring collectivism influenced acceptance of therobot as a member of the social group. We add on by considering individual and cultural-level possibilities.
Bartneck et al. [7] replicated the study by Eberhardt et al. [18] on race association with gun violence, which
involved participants indicating the moment that they could identify the content of an image—a gun—that was

ACM Trans. Hum.-Robot Interact.

4

slowly changing from blurry to clear. In the robot study, robots with darker “skins” experiencing a similar“shooter bias” as in the original study, where white participants more quickly identified the image as a gun
after being primed by a split-second image of a Black face, compared to a white face or an abstract shape. Thissuggests that visual design choices, such as apparent race, can have implications that match human models,
further raising doubts about the “mukokuseki-ness” of robots.

2.2 Mukokuseki and Social Identity Theory
Nationality, race, ethnicity, and culture are social characteristics as well as matters of social identity. We canrefer to the umbrella model of social identity theory (SIT) to help explain and predict responses to robots that
may have these characteristics. SIT is based on the idea of the social categorization of self and others, a dynamic
and process that is both personal and social. People tend to assign themselves to social groups (in-groups) andnot others (out-groups). SIT posits that such sociological characteristics as national identity will influence a
person’s affinity and willingness to interact with others [27, 60, 61, 64].
Given that national identity is a human phenomenon, the degree of humanlikeness of the robot, i.e., its levelof anthropomorphism, may impact the degree to which people pick up on and accept these social designations.
As such, we needed to select our visual robotic stimuli by degree of anthropomorphism. Following Spatola etal. [54], we selected robots that, based on their appearance, would fall into one of four distinct categories ofanthropomorphism offered by Duffy [17]: industrial, mechanical humanoid, iconic humanoid, and humanoid.
We then conducted a manipulation check of our selection to ensure that each robot would in fact be generallyprescribed by others as falling into each of these categories in the way that we expected (refer to 3.3.3).
We do not know whether and to what degree a mukokuseki effect exists for robots. The mukokuseki premiseis that robots will not have a perceivable national identity. Still, given that it is a human characteristic, we may
expect that robots with higher levels of perceived anthropomorphism, i.e., humanlike robots, would be viewed
as having some form of human nationality as a premise of “being human.” We thus hypothesized:
H1. Robots with a high degree of anthropomorphism, i.e., humanoid robots, will be perceived ashaving a human nationality, i.e., a made-in effect by anthropomorphism.
The other work reviewed, notably the report by the non-Japanese developers of the iconic humanoid“designed for Japan” robot Pepper [40], suggests that any robot, as a designed object, may have intentional orunintentional cues to national identity that could be perceived by people through its visual design, i.e., a madein effect. We thus hypothesized:
H2. Robots made in a certain nation will be more often assigned that nationality, i.e., a made-in effectby design.
Robots occupy a liminal space, where contradictions may be possible. Robots of any level ofanthropomorphism or with any visual cue to national identity may simply be perceived as a “made in” object:produced within a nation but not deemed to be “of” that nation like people are. We can explore this possibility
through similarity-to-self. SIT predicts that our perceived similarity to others is premised in social
characteristics like national identity. Thus, a robot deemed by a person to be similar to them may also be deemedby that person as having the same national identity, i.e., being part of the same social in-group. We thus
hypothesized:
H3. The perceived national identity of the robot will reflect that of the attributor when the attributor
also perceives that robot as similar to themselves, i.e., a made-in effect by similarity-to-self.

3

METHODS

We conducted an online, cross-cultural, within-subjects categorization study, following similar research survey
designs, e.g., [51, 54]. The within-subjects variable was the robot stimuli; all participants categorized all robots,
which were presented in a random order. The cross-cultural aspect refers to the participant pools (Americanand Japan-based respondents) and the national origin of the robot stimuli (robots built in America and Japan,
plus the Japan-France case of Pepper).

ACM Trans. Hum.-Robot Interact.

5

Table 1: Participant demographics.
Phase
1

NationUS

GenderAge
Nationality
33 men, 21 women,
11 aged 25-34, 21 aged 35-44, 11 aged 45-54, 6
54 Americans
and no others
aged 55-64, 4 aged 65-74, and one aged 75 orreportedolder
1JP
29 men, 26 women,
53 in their 20s, 2 in their 50s
55 Japaneseand no others
reported2US
28 men, 27 women
12 aged 18-14, 20 aged 25-34, 10 aged 35-44, 5
55 Americans(one trans*), and noaged 45-54, 7 aged 55-64, and one aged 65-74
others reported
2
JP
10 men, 27 women,
7 in their 20s, 13 in their 30s, 9 in their 40s, 6 in 35 Japanese, 5 having dual citizenshipand no others
their 50s, and 2 in their 60s or above(4 American, one N/A), and one otherreported
Demographic totals may not match overall totals due to unreported demographics. Age formats differ due to SurveyMonkey defaults foreach language.

Two phases were conducted. In the first phase, the US iconic humanoid robot (Bandit) was deemed lessanthropomorphic than expected and replaced by another robot (Octavia) in the second phase (refer to 3.3.3).
Since both robots received similar ratings in the end, we ultimately combined the data sets from both phases,
allowing us to double the number of samples. The basic protocol was registered at OSF before data collection
on Oct. 6th, 2021 1.

3.1 Participants

A total of 202 participants were involved over two phases (n=109, n=93; full details are in Table 1). Different
participants pools were used for each phase and national group. In the first phase, 54 Americans were recruited
through Amazon Mechanical Turk (AMT or mTurk) and 55 Japanese respondents were recruited through wordof-mouth sampling (n=109). In the second phase, 56 American and 37 Japanese respondents were recruited from
Prolific. To ensure response quality, we used the random code procedure by Nicoletti 2, which required
participants to complete the study and input a random code generated on the final page into the recruitment
system to receive credit. mTurk participants needed the Masters Qualification, which is granted by Amazon
when a worker has achieved high performance across a variety of tasks 3, and a HIT approval rate of over 95%.
With checks in place, mTurk and Prolific are expected to produce responses comparable to within 10% of
traditional survey panel methods [8]. Some respondents disclosed other national, ethnic, and cultural identities;
we accepted this as a characteristic of identity plurality, grouping these participants with the rest of theircohort. Participants were compensated roughly $4 USD or ￥300. Ethics approval was granted by theinstitutional ethics board at Tokyo Institute of Technology (approval #2023358).

3.2 Procedure
Participants were given a link to the English or Japanese version of the online questionnaire. The first page
provided study details and asked for consent. Participants were then presented with an overview of all robot
images in a random order. This was to create a baseline for relative comparisons even while evaluating robots
individually. Participants were then presented with each individual robot in a random order. The instruments(refer to 3.4) were folded into a larger study alongside items about gender and age as distractors [15]. Next,
participants were asked to write down the national origin of each robot, if they knew it. They then provided
demographics and collected the code for completion and compensation. The study took an average of 20
minutes.

1 hps://osf.io/ack48

2 hp://nicholasnicolei.com/survey-monkey-and-mechanical-turk-the-veriﬁcation-code
3 hps://www.mturk.com/worker/help

ACM Trans. Hum.-Robot Interact.

6

3.3 Materials and Stimuli
3.3.1 Questionnaires.
We used the multilingual SurveyMonkey online platform to deploy the questionnaires. We used the original
instruments or official translations where possible. Otherwise, a native Japanese speaker with advanced English
ability and an advanced Japanese speaker native in English translated and back-translated materials withadditional checks using Deepl 4, an imperfect but slightly higher quality tool than alternatives [26].

3.3.2 Stimuli Selection.
Nine images of robots were used as visual stimuli (Figure 1). Following previous research [51, 54], we used the
Duffy designations [17] to select the robots, aiming for those formally recognized in the IEEE Robots database 5,
and then conducted a manipulation check (refer to 3.3.3) to confirm our selections. We aimed to select similarlooking pairs of robots, one pair for each Duffy category, from the US and Japan. We were unable to do this forthe mechanical and mechanical humanoid robots, so we searched Japanese robotics company websites foralternatives, finally selecting duAro and T-HR3. Two researchers finalized the selection after several rounds of
discussion.
Five robots were made in America: Cody 6, CHARLI 7, Bandit 8, Octavia 9, and Diego-san 10. Three robots weremade in Japan: duAro 11, T-HR3 12, HRP-4C 13. We also included the special case of Pepper 14, an internationallyknown and nationally ambiguous robot created by a French company (Aldebaran Robotics) in collaboration
with a Japanese company (SoftBank Robotics) for Japanese and international markets. Pepper is often associatedwith Japan by way of this collaboration and SoftBank’s local marketing efforts, leading to high cultural
integration. Examples include the Pepper Parlor restaurant in Shibuya, Tokyo 15 and Pepper’s designation by theGovernment of Japan as an anti-COVID-19 “ambassador” 16 for the Japanese people. Yet, Pepper’s national
identity is a bit murky and possibly takokuseki. The official homepage for Pepper does not specify its origin
but implies a Japanese one by virtue of being on SoftBank’s website 17. The IEEE Robots database lists Pepper asa Japan-made robot 18. Wikipedia lists both France and Japan 19. The backstory of Pepper’s development asreported in IEEE Spectrum Magazine suggests that its engineering was largely a French effort 20. Still, adevelopment report by its designers describes how Pepper was purposefully designed to embody visual cues
4 hps://www.deepl.com
5 hps://robots.ieee.org

6 hps://robots.ieee.org/robots/cody

7 hps://robots.ieee.org/robots/charli

8 hps://robots.ieee.org/robots/bandit

9 hps://robots.ieee.org/robots/octavia

10 hps://robots.ieee.org/robots/diegosan

11 hps://kawasakirobotics.com/eu-africa/products-robots/duaro1
12 hps://global.toyota/jp/download/34530903
13 hps://robots.ieee.org/robots/hrp4c

14 hps://robots.ieee.org/robots/pepper
15 hps://www.pepperparlor.com/en/
16 hps://www.sobank.jp/en/robot/

17 hps://www.sobank.jp/en/robot/#action_pepper
18 hps://robotsguide.com/robots/pepper

19 hps://en.wikipedia.org/wiki/Pepper_(robot)

20 hps://spectrum.ieee.org/how-aldebaran-robotics-built-its-friendly-humanoid-robot-pepper

ACM Trans. Hum.-Robot Interact.

7

linked to Japanese culture, such as large “kawaii” (cute) eyes [40]. As such, Pepper may be considered amultinational, i.e., takokuseki, or nationally ambiguous robot. Still, given its international fame and framing asa “Japanese” robot, we may expect people to attribute a Japanese nationality to it. This complexity in where
Pepper was made, by whom and for whom, and how it has been framed in terms of its national identity presents
an intriguing case study and point for comparison against the other mononational and less-known robots. We
thus used Pepper to evaluate priming effects and perceptions of made-in and mukokuseki against actual
knowledge (refer to 3.4.4). Even so, given this complexity, we decided to separate our analyses of Pepper fromthe other robots.
Degree of
Anthropomorphism

Made in America

Made in Japan

Multinational

Industrial

Mechanical
Humanoid

IconicHumanoid

Humanoid

Figure 1: Robots used as visual stimuli by degree of anthropomorphism and national origin.

Most images were sourced from the IEEE Robots database 21, except for duAro and T-HR3, which weresourced from the creators directly. As in previous research [51, 54], we cropped all images to the profile of therobot for consistency.
3.3.3 Manipulation Check.
We conducted a manipulation check to confirm the level of anthropomorphism for each robot in both phases,
bundled together with the main questionnaire. We used the measure described in 3.4.1, analyzed as describedin 3.5. Most robots were ascribed the expected Duffy category. However, the US iconic humanoid robot, Bandit,
received lower anthropomorphism scores than expected. We replaced this robot with Octavia in the second
phase. Nevertheless, Octavia received similar anthropomorphism scores, leading us to accept uneven meandifferences between the Duffy categorizations. We report on the manipulation check results in full in 4.1.

3.4 Measures
3.4.1 Perceived Anthropomorphism.
We used the 5-point semantic differential anthropomorphism subscale from the Godspeed instrument [6]. The
Godspeed is widely used instrument that has been translated into many languages, including Japanese. We used
the anthropomorphism subscale to check our Duffy scale categorizations (Cronbach’s alpha = .92).
3.4.2 Perceived Nationality.
We created a custom two-part instrument, in the absence of validated instruments. The first comprised one
item to directly assess perceived nationality: “Nationality is a complex concept related to status as a member ofa nation and geographic origin, as well as race, ethnicity, and culture. Do you think this robot has a nationality?”
21 hps://robots.ieee.org

ACM Trans. Hum.-Robot Interact.

8

Respondents used a 3-point scale to indicate agreement and confidence (Yes, No, Unsure). We then asked
participants to indicate what nationality, if any, the robot had, allowing for multiple selections from an
expansive set of options to avoid bias and check the perceived multinationalism of Pepper. These included:
North American, including Canada and the USA; Latin American; European; Middle Eastern; African or
Caribbean; East Asian, including China and Japan; South Asian; a “robot” nationality, to account for liminal
and ambiguous perceptions of robots as human enough for some national identity but not to the extent of
having a human national identity; no nationality; and another nationality (please write).
3.4.3 Perceived Similarity in Nationality.
We created a custom two-part instrument reflecting direct and indirect measures of perceived national
similarity and ingroup identification. We captured and triangulated direct and indirect measures as a means ofrigour and quality assurance [37]. The direct measure used a 5-point Likert scale and asked: “Do you think thisrobot has the same nationality as you?” The indirect measure was placed within the Godspeed items as “Not
Similar to Me” and “Similar to Me” poles.
3.4.4 Made-In Knowledge.
We created a custom qualitative instrument to check if respondents knew which countries the robots weremade in, i.e., to account for priming effects, especially for Pepper. At the end of the survey, respondents wereasked to write the country or nation for each robot, if they knew. They were able to skip each robot or theentire question.
3.4.5 Demographics.
We collected gender, age, and nationality at the end of the survey, to avoid potential priming effects [25] orstereotype threats [57]. Our response options were chosen based on recent guidelines for participant research,e.g., [30, 55], as well as the defaults of the SurveyMonkey platform. Gender options included woman/feminine,
man/masculine, non-binary, gender fluid, trans*, another gender (please write), and prefer not to say. Age for
American respondents was collected in 5-year increments from 18 to 75+. For Japanese respondents, age wascollected by decade, e.g., 20s, 30s. Nationality options included USA, Japan, dual citizenship, and other (pleasewrite).

3.5 Data Analysis
Descriptive statistics were generated, including mean (M), standard deviation (SD), median (MD), interquartile
range (IQR), and counts with percentages where appropriate. Perceived anthropomorphism items wereaveraged together to create one score. Inferential statistics were conducted for each robot, each national groupof robots, and each national cohort, i.e., Japan and USA, according to the measures and hypotheses.
Nonparametric statistics were used when normality and other checks failed. Made-in nationality responses
were categorized and counted, and a grand score for the cohort was generated.

4

RESULTS

We now present our results, organized by RQ and hypothesis alongside manipulation checks and otherconfirmative analyses. Descriptive statistics are found in Table 2 and Table 3.

4.1 Anthropomorphism Manipulation Check
We first considered whether the robots mapped onto the Duffy categories as expected, based on perceived
anthropomorphism. In Phase 1, a t-test indicated a statistically significant difference between Japanese (M=2.0)
and American (M=1.6) perceptions of Bandit, the US iconic robot, t(106.6) = 2.6, p = .01, 95% CI [.1, .7]. Due tothis, we replaced Bandit with a different iconic robot made in the US but more like Pepper: Octavia. No
statistically significant differences were found in Phase 2, confirming the fix.
We then conducted t-tests to compare Phases 1 and 2, treating Bandit and Octavia as the US iconic robot
each time. We did not find a statistically significant difference, suggesting that, despite the Phase 1 results,
participants across phases perceived Bandit and Octavia as roughly equal in terms of anthropomorphism.
However, t-tests indicated a statistically significant difference for the US- and Japan-made humanoid robots,

ACM Trans. Hum.-Robot Interact.

9

Diego-san and HRP-4C, between Phase 1 (M=3.4 and M=3.6) and Phase 2 (M=3.0 and M=3.3), t(197) = 3.0, p =.003, 95% CI [.1, .7] and t(199.6) = 2.1, p = .04, 95% CI [.02, .5], indicating lower anthropomorphism ratings in
the second phase. Still, the means indicated high anthropomorphism overall. Moreover, Kruskal-Wallis tests
and follow-up Dunn’s tests indicated statistically significant differences among all US-made, χ2(3) = 338.0, p <.001, ηp2 = .42, and Japan-made, χ2(3) = 413.4, p < .001, ηp2 = .52, robots, indicating that all Duffy categories
remained distinct for all robots across both national cohorts and phases. Notably, robots higher on the Duffy
continuum were rated more human-like than those lower down (Figure 2).
Table 2: Descriptive statistics for anthropomorphism by study phase, robot origin, and Duﬀy category.
Robot Origin

Duffy Category

Phase

Perceived Anthropomorphism
M
SD
MD
IQR
US
Industrial
1
1.3
0.4
1
0.5
US
Mechanical Humanoid 1
1.8
0.8
1.5
1.2
US
Iconic Humanoid
1a
2.2
0.9
2
1.2
US
Humanoid
1b
3.4
0.9
3.5
1.2
US
Industrial
2
1.4
0.6
1.2
0.5
US
Mechanical Humanoid 2
1.8
0.8
1.5
1.2
US
Iconic Humanoid
2
2
0.7
2
1
US
Humanoid
2b
3
0.9
3.2
1.5
JP
Industrial
1
1.3
0.5
1
0.2
JP
Mechanical Humanoid 1
1.7
0.7
1.8
1.2
JP-FR
Iconic Humanoid
1
2.4
0.8
2.5
1.2
JP
Humanoid
1b
3.6
1
3.8
1.2
JP
Industrial
2
1.3
0.5
1
0.2
JP
Mechanical Humanoid 2
1.8
0.7
1.8
1.2
JP-FR
Iconic Humanoid
2
2.2
0.8
2
1.2
JP
Humanoid
2b
3.3
0.9
3.5
1.2
a Sig. diff. by respondent country, p < .05. b Sig. diff. by phase, p < .01 and p < .05.

Perceived Nationality
Matched“Robot”
8 (7%)
15 (14%)7 (6%)
18 (17%)21 (19%)19 (17%)69 (63%)
6 (5%)
4 (4%)
18 (19%)5 (5%)
25 (27%)
5 (5%)
28 (30%)44 (47%)
3 (3%)
7 (6%)
15 (14%)7 (6%)
19 (17%)23 (21%)20 (18%)88 (81%)4 (4%)1
19 (20%)5 (5%)
21 (23%)
6 (6%)
24 (26%)73 (78%)7 (8%)

Table 3: Summarized descriptive statistics for key variables presented by robot origin.
Robot
OriginDuffy
Category

Perceived
Anthropomorphism
M
SD
MD

Perceived Nationality
IQR

Matched“Robot”

US

Industrial

1.4

0.5

1

0.5

12 (6%)

33 (16%)US

Mech. Hu.

1.8

0.8

1.5

1.2

12 (6%)

43 (21%)

US

Iconic Hu.

2.1

0.8

2

1.2

26 (13%)47 (23%)

US

Humanoid

3.2

0.9

3.4

1.5

113 (56%)

9 (4%)

JP

Industrial

1.3

0.5

1

0.2

8 (4%)

34 (17%)JP

Mech. Hu.

1.8

0.7

1.8

1.2

12 (6%)

40 (20%)JP-FR

Iconic Hu.

2.3

0.8

2.2

1.2

29 (14%)44 (22%)JP

Humanoid

3.4

0.9

3.8

1

161 (80%)

11 (5%)

Mech.: Mechanical. Hu.: Humanoid.

Perceived Similarity:
Direct
M
SD
MD
IQR
1.
1
1.
2
1.
4
2.
1
1.
0
1.
3
1.
5
2.
0

1.1

1

2

Perceived Similarity:
Indirect
M
SD
M
IQR
D
1.2
0.6
1
0

1.2

1

2

1.5

0.8

1

1

1.3

1

2

1.7

0.8

1

1

1.5

2

2

2.6

1.2

3

2

1.1

1

2

1.1

0.6

1

0

1.2

1

2

1.5

0.8

1

1

1.5

1

3

1.8

0.9

2

1

1.5

2

2

3.0

1.2

3

2

We thus confirmed that our categorization of the robots used in our subsequent analyses generally matchedthose of participants. We could combine the data collected in each phase.

4.2 Nationality and Anthropomorphism (H1)
The distribution of attributions of nationality for the robots are presented in Table 4 and Figure 3. There were1863 attributions.

ACM Trans. Hum.-Robot Interact.

10

Figure 2. Perceived anthropomorphism for the US (le) and JP plus JP-FR Pepper (right) robots.

Figure 3. Frequency of aributed nationalities.

Participants tended to ascribe a nationality of some kind to the humanoid robots. Chi-squared tests indicatedstatistically significant differences in attributions of no nationality and some nationality (“robot” or human) to
US-industrial, χ2(1) = 33.32, p < .001, US-mechanical, χ2(1) = 12.16, p < .001, US-humanoid, χ2(1) = 128.47, p <.001, JP-industrial, χ2(1) = 57.00, p < .001, JP-mechanical, χ2(1) = 14.38, and JP-humanoid, p < .001, χ2(1) = 179.56,
p < .001. Descriptive statistics (Table 4) show fewer attributions of some nationality compared to no nationality
for the industrial and mechanical robots, while the opposite was true for the humanoids. The iconic robots had
roughly even attributions, with no statistically significant differences found for the US-iconic robot, p = 26, and
JP-FR Pepper, p = 10.

ACM Trans. Hum.-Robot Interact.

11

Table 4: Nationalities assigned to the robots by origin and mukokuseki, takokuseki, or made-in designation.
Rob.
Ori.
US
US

Duffy
Category

North
Amer.

Latin
Amer.

Europ.

MiddleEast.

Afric.

East
Asian

Sum of
Hu.
Nat.
35
47

“Robot”

None

Desig.

Industrial
16 (8%)
5 (2%)
7 (3%)
2 (1%)1
7 (3%)
33
154
Made-inMech.
23 (11%) 2 (1%)
17 (8%)
2 (1%)0
8 (4%)
43
143
Made-in
Hu.
US
Iconic18 (9%)
7 (3%)
8 (4%)
10 (5%)
8 (4%)
16 (8%)
60
47
124
Tako.
Hu.
US
Human.
75 (37%) 64 (32%) 31 (15%) 22 (11%) 7 (3%)
32 (16%)
209
9
37
Tako.
JP
Industrial
9 (4%)
2 (1%)
4 (2%)
3 (1%)
2 (1%)
5 (2%)
23
34
171
Muko.
JP
Mech.
19 (9%)1
11 (5%)
2 (1%)1
11 (5%)
47
40
146
Tako.
Hu.
JP-FR
Iconic15 (7%)1
9 (4%)
1
0
34 (17%)60
44
129
Made-in
Hu.
JP
Human.
4 (2%)1
4 (2%)
4 (2%)0184 (91%)
204
11
12
Made-in
Note that respondents could select multiple options. Rob. Ori.: Robot Origin. Mech.: Mechanical. Hu.: Humanoid. Human.: Humanoid.
Amer.: American. Europ.: European. East.: Eastern. Afric.: African. Hu.: Human. Nat.: Nationalities. Desig. Designation. Tako.:
Takokuseki. Muko.: Mukokuseki.

Participants also tended to ascribe a human, rather than “robot,” nationality to the humanoid robots. Chisquared tests indicated statistically significant differences in attributions of “robot” and human nationality to
the US-humanoid, χ2(1) = 183.49, p < .001, and JP-humanoid, χ2(1) = 173.25, p < .001. Descriptive statistics (Table4) show far more attributions of a human nationality for the humanoid robots, while the other types received
roughly even numbers of “robot” and human nationality attributions.
Perceived level of anthropomorphism also related to ascriptions of nationality. Positive, statisticallysignificant Kendall’s tau correlations were found between perceived anthropomorphism and attribution ofnationality for US-iconic, τb = .18, p = .003, US-humanoid, τb = .24, p < .001, JP-iconic, τb = .22, p < .001, and JPhumanoid, τb = .19, p = .001. Kruskal-Wallis tests indicated statistically significant differences for perceived
anthropomorphism by attribution of nationality for US-iconic, χ2(2) = 9.68, p = .008, ηp2 = .11, US-humanoid,
χ2(2) = 17.78, p < .001, ηp2 = .13, JP-industrial, χ2(2) = 8.34 p = .016, ηp2 = .14, and JP-humanoid, χ2(2) = 10.23, p= .006, ηp2 = .08. The results for the Japan-industrial robot appear to be an artifact of the small group size (“Yes”
n=8 and anth. M=1.7 v. “No” n=181 and anth. M=1.2).
We now consider the liminal case of JP-FR iconic humanoid Pepper. A McNemar test did not find astatistically significant difference between ascriptions of human (n=60) and “robot” (n=44) nationalities, χ2(1)= 3.7, p = .054. However, ones were found between human and none (n=129), χ2(1) = 21.89, p < .001, and "robot"
and none, χ2(1) = 41.76, p < .001. This indicates relatively similar levels of among the two kinds of nationality
ascriptions but significantly more ascriptions of no nationality to Pepper. For perceived anthropomorphism(M=2.3, SD=0.8, MD=2.2, IQR=1.2), a positive, statistically significant Kendall’s tau correlation was found withascriptions of nationality, rs(200) = .84, p < .001, indicating that those who perceived Pepper as more
anthropomorphic were also more inclined to ascribe it a nationality. A Kruskal-Wallis test indicated astatistically significant difference for perceived anthropomorphism by attribution of nationality, χ2(2) = 18.9, p< .001, ηp2 = .13. This reflects the H1 results for the other robots.
The results suggest that the more humanlike, the more likely the robot was perceived as having a nationality,i.e., a made-in effect by anthropomorphism, so we can accept hypothesis H1. The number of ascriptions ofsome nationality rose with degree of anthropomorphism along the Duffy continuum. Ascriptions of a “robot”
nationality were significantly low for the humanoids, while ascriptions of a human nationality weresignificantly high. The iconic robots represent a liminal case, with equal attributions of robot and humannationalities. In general, individuals who perceived higher degrees anthropomorphism in a given robot tended
to also assign it a nationality of some kind, and humanoid robots tended to receive a human nationality overall.

ACM Trans. Hum.-Robot Interact.

12

4.3 Made-In Knowledge
Knowledge of where each robot was made could have influenced results. So, we asked participants to tell us if
they knew which nation each robot was “made in.” As the incorrect responses rates (Table 5) indicate, many
made guesses, despite the instructions. Aside from the humanoid robots, most people did not know or did notguess correctly. A Chi-squared test did not find a statistically significant difference by country for number ofguesses for US-made and JP-made robots, p = .45. This suggests that the Japanese and American respondents
guessed in roughly equal numbers.
Table 5: Nationalities assigned to the robots.
Robot Nation. Duffy Category US Correct
US
Industrial
2
US
Mech. Hu.
2
US
Iconic Hu.
3
USHumanoid
20
JP
Industrial
0JP
Mech. Hu.
1
JP
Humanoid41
Nation.: Nationality. Inc.: Incorrect. Mech.: Mechanical. Hu.: Humanoid.

US Inc.
2
3
7
16
2
2
27

JP Correct
4
1
3
6
2
2
26

JP Inc.
10
13
13
11
14
13
10

Chi-squared tests indicated significant statistical differences in the number of guesses by Duffy level by the
US respondents for the US-made robots, χ2(3) = 34.78, p < .001, but not the Japan-made robots, p = .29, as well
as by the Japanese respondents for the US-made robots, χ2(2) = 74.42, p < .001, and the Japan-made robots, χ2(3)= 38.4, p < .001. The descriptive statistics (Table 5) show that this indicates a high tendency for guesses abouthumanoid robots, except by US respondents for the Japanese humanoid robot.
Let us now consider the multinational case of Pepper. A Chi-squared test indicated a statistically significant
difference between US and Japanese respondent assignations to Pepper, χ2(1) = 21.42, p < .001. Descriptive
statistics (Table 6) suggest that Japanese respondents made more assignations than those from the US. Only
twelve of 35 assignations (21.8%), all from the Japanese respondents, correctly stated Pepper’s origin. 35
assignations (63.6%) by Japanese respondents indicated a Japan-only origin; seven of 16 (43.8%) assignations by
US respondents did so, as well. This reflects Pepper’s association with Japan locally and abroad. Yet,
respondents made more incorrect guesses in general; moreover, most did not know or guess, leaving it blank.
Table 6: Nationalities assigned to Pepper.
Nation
US
JP

Japan
7
35

France01Both/SoftBank
0
12

Any
16
55

We will need to consider the results about Pepper’s Japanese origin bias carefully, especially for the Japanesecohort. Still, the results support the relationship between high anthropomorphism and perceptions ofnationality.

4.4 Made-In Eﬀects (H2)
A series of Chi-squared tests were conducted to compare each robot’s made-in origins to participant ascriptionsof nationality.
For the US-made robots, statistically significant differences were found when comparing nationalities (Table4) for US-iconic, χ2(1) = 9.6, p = .002, and US-humanoid, χ2(1) = 16.66, p < .001, but not US-industrial, p = .24,
and US-mechanical, p = .88. Participants ascribed the correct origin to the US-humanoid robot more frequently
than no nationality, χ2(1) = 12.89, p < .001. Moreover, those that attributed a human nationality to the industrial
and mechanical robots tended to attribute the correct one: US-industrial, χ2(5) = 18, p = .003, and US-mechanical,
χ2(5) = 50.69, p < .001. However, no difference was found among the human nationality attributions for the USiconic robot, p = .083, and particularly for ascriptions of North American and East Asian nationalities, p = .73.
This suggests a clear made-in effect for the US industrial and mechanical robots and a takokuseki or
multinational effect for the iconic and humanoid robots.
ACM Trans. Hum.-Robot Interact.

13

For the Japan-made robots, a statistically significant difference favouring an East Asian ascription (Table 4)
was found when comparing nationalities for the humanoid, χ2(1) = 131.84, p < .001. Participants also ascribed
the correct origin to this robot more frequently than no nationality, χ2(1) = 150.94, p < .001. Unlike the US-madeone, ascriptions of a human nationality varied for the JP-industrial robot, p = .13. Also, ascriptions of a humannationality for the JP-mechanical robot were spread between East Asian, North American, and European, withstatistically significantly fewer counts for Latin American, Middle Eastern, and African, χ2(5) = 33.04, p < .001.
This suggests a made-in affect for the JP-humanoid robot, a mukokuseki effect for the JP-industrial robot, anda takokuseki effect for the JP-mechanical robot.
As for Pepper, only three Japanese respondents correctly assigned it both European and East Asian
nationalities. Still, attributions of human nationalities (Table 4) statistically significantly differed, χ2(5) = 84.86,
p < .001. Notably, there were statistically significant differences favouring an East Asian nationality over a
North American one, χ2(1) = 7.37, p = .007, and a European one, χ2(1) = 13.09, p < .001. This suggests a takokuseki
effect, but not one that flatly affirms Pepper’s FR-JP origins.
In summary, we can partially accept hypothesis H2, that robots made in a certain nation will be more
often assigned that nationality, i.e., a made-in effect by design, for the US-made non-humanoid robots andthe Japan-made humanoid robot. The JP-industrial robot was mukokuseki. The other robots, including
Pepper, appear to be takokuseki: multinational but not of any or every nation, indicating ambiguity or hybridity[32]. As mentioned in 4.3, we should consider the results for Pepper with some caution, given that it was known
to nearly half of the Japanese participants and some of the American participants.

4.5 Perceived Similarity in Nationality (H3)
We then analyzed the relationship between the direct and indirect measures of perceived similarity andnationality.
For the US cohort, no statistically significant correlations were found for US-made robots or Japan-maderobots.
For the Japanese cohort, statistically significant positive correlations were found between perceived as samenationality (MD=2, IQR=1.8) and perceived similarity (MD=1, IQR=1) for the US-made mechanical humanoid
robot, τb = .25, p = .03, all of the Japan-made robots—the industrial robot, τb = .27, p = .03, MD=1, IQR=1 and
MD=1, IQR=1, the mechanical humanoid, τb = .30, p < .001, MD=2, IQR=2 and MD=2, IQR=2, and the humanoid
robot, τb = .31, p < .001, MD=4, IQR=1 and MD=4, IQR=1—and Pepper, τb = .24, p = .03, MD=3, IQR=2.8 and
MD=3, IQR=2.8.
In sum, Japanese respondents tended to associate national similarity with self-similarity for Japanese-maderobots and tended not with US-made ones. The US-made mechanical humanoid robot, CHARLI, may be a special
case for the Japanese cohort, but we lack the qualitative accounts to explain it. No trends appeared to occur for
American participants. This indicates a cultural effect on the relationship between perceiving a robot as
having the same nationality as oneself (direct) and perceiving similarity between oneself and that robot(indirect) for Japan. However, as the medians show, this does not speak to the degree of similarity, only thatparticipants used these measures similarly.
We now turn to degree of similarity. Figure 4 and Table 7 illustrate the results for participants who rated
robots as having the same nationality as themselves. Note that the counts vary widely by national cohortand robot, with some very low and others relatively high. We have used nonparametric statistics to account forthis. Still, these results should be taken with caution.
For the US-made robots, a positive, statistically significant correlation was found by robot type and perceived
similarity across cohorts, τb = .24, p < .001. A Kruskal-Wallis test confirmed differences in perceived similarity
by type of robot across cohorts, χ2(3, n=65) = 12.29, p = .007, ηp2 = .19. Dunn’s tests indicated statisticallysignificant differences between US-industrial and US-humanoid, p = .003 (p adjusted with the BenjaminiHochberg method = .02). One between US-iconic and US-humanoid lost statistical significance after adjustment,p = .046, adj. p = .14. In sum, perceived similarity increased for the US robots along the Duffy continuum,
especially between the lowest and highest.
We then divided by national cohort. A Kruskal-Wallis test found a statistically significant difference inperceived similarity by type of robot for US participants, χ2(3, n=42) = 10.33, p = .016, ηp2 = .31, but not for

ACM Trans. Hum.-Robot Interact.

14Japanese participants, p = .45. For the US participants, Dunn’s tests indicated no statistically significant
differences, and a loss of statistically significance for US-industrial and US-humanoid, p = .044, adj. p = .13. This
indicates that perceived similarity with US robots deemed to have the same nationality by the US cohort
increased along the Duffy continuum, with results hampered by the small and varying group sizes.
For the Japan-made robots, a positive, statistically significant correlation was found by type of robot andperceived similarity across cohorts, τb = .33, p < .001. However, a Kruskal-Wallis test did not find a statisticallysignificant difference in perceived similarity by type of robot across cohorts, p = .82, likely due to the low counts
for JP-industrial (n=2) and JP-mechanical (n=4) compared to JP-humanoid (n=74). This hints at an increase insimilarity for the Japan-made robots along the Duffy continuum.
As before, we also divided by national cohort for the Japan-made robots. Kruskal-Wallis tests did not findstatistically significant differences in perceived similarity by type of robot for US participants, p = .14, orJapanese participants, p = .39. While descriptive statistics (Figure 4 and Table 7) suggest a similar trend to the
US-made robots, the low and varying group sizes affected results. For example, only five Americans deemed
the Japan-made robots to be American, and only one Japanese participant deemed JP-industrial as having thesame nationality, compared to the JP-humanoid (n=70). Nevertheless, this again shows a connection between
the high anthropomorphism and ascriptions of same nationality and similarity.

Figure 4: Perceptions of similarity with US- and Japan-made robots by robot Duﬀy category and participant nationality.
Mech. Hu.: Mechanical Humanoid. Iconic Hu.: Iconic Humanoid.

As for Pepper, a positive, statistically significant correlation was found between agreement on samenationality and perceived similarity across cohorts, τb = .17, p = .004. A Kruskal-Wallis test did not find astatistically significant difference between the US (n=6, MD=2, IQR=.8) and Japanese (n=24, MD=2, IQR=3)
respondents, p = .85 (Figure 5 and Table 7). This indicates that those who marked Pepper as having their own
nationality had similar impressions of its similarity to themselves, regardless of nation, and that these were low
in general.
Overall, perceived similarity with the robots marked by participants as having the same nationality as
themselves increased with the anthropomorphism of the robot, despite the aforementioned cultural differencesin how Japanese respondents used the direct and indirect measures. We can thus partially accept hypothesis
H3, that if people assigned a robot the same nationality as their own, then they perceived that robot as more
like themselves, i.e., a made-in effect by similarity-to-self, but only for robots of high anthropomorphism,i.e., humanoids. We should interpret these results with caution, given the varying and small group sizes used
ACM Trans. Hum.-Robot Interact.

15

in the statistics. Still, these results reflect the earlier findings on a link between anthropomorphism andperceptions of nationality. More pointedly, the cross-cultural results provide further nuance on the earlier
results, supporting a takokuseki effect even for “made-in” robots. We discuss the possible explanations for thisnext, alongside the other results.

Table 7: Similarity-to-self when same nationality assigned.
Robot Nat.

Duffy Category

Particip.
Nat.
nUS
Industrial
US
2
JP
3
US
Mech. Hu.
US
2
JP
3
US
Iconic Hu.
US
5
JP
4
US
Humanoid
US
33
JP
13
JP
Industrial
US
1
JP
1
JP
Mech. Hu.
US
0
JP
4
JP-FR
Iconic Hu.
US
6
JP
24
JP
HumanoidUS
4JP
70
Nat.: Nationality. Particip.: Participant. Mech.: Mechanical. Hu.: Humanoid.

Perceived Similarity
M
SD
1
0
3
1.7
2
0
2.7
0.6
2
1.2
2.5
1.3
3.2
1.1
2.8
0.9
4
2

MD
1
1
2
3
2
2.5
4
3
4
2

IQR
0
1
0
0.5
1
1.5
2
0

3
2.2
2.4
2.5
3.4

3.5
2
2
2.5
4

1.5
0.8
3
1
1

1.4
0.8
1.2
0.6
1.2

Figure 5. Perceptions of similarity with Pepper, the JP-FR iconic humanoid robot, by participant nationality.

5

DISCUSSION

The phenomenon of mukokuseki can occur for robots, but as our results indicate, this was generally not thecase for most people. The standout exception was the Japan-made industrial robot. Anthropomorphism,
perceptions of the robot’s national identity and one’s own nationality identity, and feelings of similarity interact
in nuanced ways that point more often to perceptions of takokuseki, or multinationalism. Moreover, a “madein” effect was found for robots along the Duffy continuum. We dig into these results in more depth and draw
from previous research and theory to provide possible explanations and points for departure.
We discovered a clear mukokuseki effect only one robot, with few and equally varied ascriptions ofnationality to the Japan-made industrial robot across national cohorts. We also found another variety ofmukokuseki: takokuseki, or multiple but not comprehensive assignations of nationality for the US-made iconic
robots (Bandit and Octavia), the US-made humanoid (Diego-san), the Japan-made mechanical humanoid robot,
and the actually multinational iconic humanoid robot, Pepper, a collaboration between France and Japan. The
US-made industrial and mechanical robots, as well as the Japan-made humanoid robot, HRP-4C, were subject
to a "made-in” effect, somehow assigned the correct nationalities by most people, despite lack of actual
knowledge by those people of each robot’s origin. To answer RQ1, each effect occurred depending on the robot.

ACM Trans. Hum.-Robot Interact.

16

To answer RQ2, anthropomorphism clearly played a large role but in unexpected ways. Highanthropomorphism explained both the breakdown of a mukokuseki effect, i.e., the presence of a made-in effect,
as for HRP-4C, but also the presence of mukokuseki and takokuseki effects, as for Diego-san. Prior knowledge
may have influenced results in the case of Pepper but cannot account for the rest.
Diego-san was mukokuseki by way of takokuseki: ambiguity, plurality, or hybridity in national identity,
receiving the range of human nationality ascriptions, but some more than others. Yet, the relative lack ofAfrican and Middle Eastern ascriptions is notable. This hints at a hidden association between national identity
and race and/or ethnicity, given that African and Middle Eastern nations are generally associated with darkerskinned people. Specifically, these results allude to generalizations, if not stereotypes, about certain
nationalities and race and/or ethnicity, especially in terms of skin colour, the primary anthropomorphic cuethat separates Diego-san from the other types of robots along the Duffy scale. In other words, there appears to
be a racial or ethnic component underlying these ascriptions. Whiteness is often presented as the default,
including for products such as dolls [42]. Light skin is often positioned as ideal within the context of global
white supremacy, a phenomenon known as colourism [11], which affects all people but perhaps especially nonwhite people and communities of colour, i.e., non-white American respondents and the Japanese respondents[29]. The apparent age of the robot, i.e., as a baby, may have also increased its ambiguity. In contrast, the Japanmade humanoid robot, HRP-4C, was designed to look like a teenaged Japanese girl. Future work can explore
the intersections of age and race to tease out what national, ethic, racial, and/or cultural codes can be visuallyembedded into and perceptible within highly anthropomorphic robots.
We should also consider why the US- and Japan-made robots with the same relative anthropomorphism
were not subject to the same effects, especially the “made-in” US-made industrial and mechanical humanoid
robots. Perhaps these contain visual codes of US-ness that we cannot identity. The US is also a more raciallyand ethnic diverse nation than Japan, and we did not ask participants to self-report race or ethnicity. It is
possible that the American respondents who identified with HRP-4C read racial/ethnic visual cues in the robot’s
appearance that matched their own, regardless of nationality. Certainly, participants did not know where theserobots were made, as the freeform question results showed. Perhaps the global dominance of Americanindustries and/or subtle anthropomorphic visual cues coded as white and/or American were perceived. Morepointed qualitative work can confirm, refute, and/or expand on these possibilities.
The above leads us to raise a critical point about the limits and possible demerits of mukokuseki effects—
pure mukokuseki or the multiplicitous takokuseki—for certain groups of people. An important limitation of ourwork is the lack of data on the racial and ethnic identities of our participants. Yet, recent statistics on theparticipant pools we used indicate demographic biases, with mTurk, for instance, being comprised of 79.9%
white-identifying people and 9.1% Black-identifying people [34]. In the US context, at least, mukokuseki may
simply reflect the status quo, leading naïve designers and researchers to create and employ robots that reflect
those who have the greatest shares in representation and power and leaving uncharted others to the wayside.
A crucial next step will be to capture participant demographics, especially those of a variety of racial and ethnic
identities and confirm whether mukokuseki is truly “neutral” or “ambiguous,” as well as when and to what
extent takokuseki is “pluralistic.”
We also found evidence of individual differences in self- and other-categorization, in line with previous
work [36] and what we would expect for SIT, at least among people. Made-in robots might be mukokuseki forsome, as when Japanese respondents ascribed an East Asian nationality to the US-made mechanical humanoidand iconic robots. Indeed, sampling at the national group level may occlude individual differences. Ambiguity,
plurality, and hybridity in ascriptions of nationality, i.e., takokuseki, as found for the US-made Diego-san robot,
may be better understood as an individual phenomenon rather than a Japanese or American group
phenomenon. People may have different, even shifting orientations to facets of identity that then influence
how social cues embedded in the visual design of robots, such as nationality, are perceived [4]. There are
implications for diversity as Industry 5.0 approaches [2] and mass-produced robots that work with people enter
our lives in ever greater numbers.

ACM Trans. Hum.-Robot Interact.

17

5.1 Limitations
We did not have access to whether mukokuseki was intended by the designers of the selected robots. As
discussed, user perceptions may be the most critical in practice, which guided our methodological approach.
Even so, future work should engage designers about the notion of mukokuseki in their work, such as throughinterviews, focus groups, or design jams. A clear next step for design and research practice would be exploring
how to better align creator’s expectations with user perceptions, in terms of national identity and other cues inthe design of robots. A follow-up complementary trajectory would then be a matching study between expert
intentions and user perceptions.
We were also unable to collect equivalent numbers of Japanese participants in the second phase due to
unexpected limitations in the participant pool. Some groups were small because we could not select for certain
responses in advance, e.g., shared national identity with a given robot. This particularly affected the results for
H3. We hope that these results will guide the design of future work and sampling strategies. We also included
a small selection of robots for only two nations and two national participant pools. Future work should explore
a greater variety of robots and participant pools from other nations.
We could not control the number of data points for other variables, as well, notably whether and how many
ascriptions of nationality would be made for each robot by each participant. This meant that some sample sizes
were low and thus some statistical analyses were underpowered, e.g., the Chi-squared test for the humannationalities attributed to the JP-industrial robot (n = 24, power = .43). Since we could not account for this inadvance, future work can learn from our data set and recruit more participants (double, in the case of thisexample).
While we conducted manipulation checks that, apart from the US iconic humanoid, matched our
expectations, we acknowledge that our initial selection process could have been biased in subtle ways.
Ultimately, the research team, with the notions of made-in, mukokuseki, and takokuseki in mind, decided on
the baseline stimuli. The presence of such biases will be revealed in follow-up research with a broader range ofrobotic stimuli chosen by other researchers or as a result of a matching study with naïve subjects.
We did not explicitly ask about participant race and/or ethnicity, modeling our approach in line with how
mukokuseki is defined in Japan. Future work should explore the nuances between race, ethnicity, nationality,
and culture in robotic stimuli as well as in the responses from a diversity of participants across cultural contexts
to ensure that mukokuseki and its kin, takokuseki, truly apply to everyone.
We did not collect qualitative data on the reasons behind participant ascriptions, which should be done inwork.

6 CONCLUSION
People draw from human models to understand the robots that they see. This leads to mukokuseki, takokuseki,
and made-in effects. Anthropomorphism is clearly at play. In the absence of familiarity with a robot, it is notclear what else explains a robot’s relative mukokuseki-ness or made-in-ness. Future work will need to tease out
possibilities, especially considering critical race theories and other work, within and outside of HRI, on how
people perceive humanlikeness in robots.

ACKNOWLEDGMENTS
This work was funded by the department of the first author. We also thank Alexa Frank for introducing us tothe concept of mukokuseki. We are truly grateful to the editors and reviewers for their service.

REFERENCES[1]

Adamowicz, K.K. 2014. The meaning of “Mukokuseki” in Harajuku subcultures research. Research Reports Session II: Culture,
Language and Social Science, at the 10th Convention of the International Association for Japan Studies (Kyoto, Japan, 2014), 35–41.

[2]Adel, A. 2022. Future of industry 5.0 in society: Human-centric solutions, challenges and prospective research areas. Journal of
Cloud Computing. 11, 1 (Sep. 2022), 40. DOI: https://doi.org/10.1186/s13677-022-00314-5.

[3]

Alcoff, L.M. and Mendieta, E. 2003. Identities: Race, Class, Gender, and Nationality. Wiley.

[4]

Allan, D.D., Vonasch, A.J. and Bartneck, C. 2022. The doors of social robot perception: The influence of implicit self-theories.
International Journal of Social Robotics. 14, 1 (Jan. 2022), 127–140. DOI: https://doi.org/10.1007/s12369-021-00767-9.

ACM Trans. Hum.-Robot Interact.

18

[5]

Annamma, S.A., Jackson, D.D. and Morrison, D. 2017. Conceptualizing color-evasiveness: Using dis/ability critical race theory to
expand a color-blind racial ideology in education and society. Race Ethnicity and Education. 20, 2 (Mar. 2017), 147–162. DOI:
https://doi.org/10.1080/13613324.2016.1248837.

[6]

Bartneck, C., Kulić, D., Croft, E. and Zoghbi, S. 2009. Measurement instruments for the anthropomorphism, animacy, likeability,
perceived intelligence, and perceived safety of robots. International Journal of Social Robotics. 1, 1 (Jan. 2009), 71–81. DOI:
https://doi.org/10.1007/s12369-008-0001-3.

[7]

Bartneck, C., Yogeeswaran, K., Ser, Q.M., Woodward, G., Sparrow, R., Wang, S. and Eyssel, F. 2018. Robots and racism. Proceedingsof the 2018 ACM/IEEE International Conference on Human-Robot Interaction (Chicago, IL, USA, Feb. 2018), 196–204.

[8]

Bentley, F.R., Daskalova, N. and White, B. 2017. Comparing the reliability of Amazon Mechanical Turk and Survey Monkey to
traditional market research surveys. Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing
Systems (New York, NY, May 2017), 1092–1099.

[9]

Bernotat, J., Eyssel, F. and Sachse, J. 2017. Shape it – The influence of robot body shape on gender perception in robots. Proceedingsof the 2017 International Conference on Social Robotics (Cham, 2017), 75–84.

[10] Blakemore, E. 2019. Race and ethnicity facts and information. National Geographic.
[11] Burton, L.M., Bonilla-Silva, E., Ray, V., Buckelew, R. and Hordge Freeman, E. 2010. Critical race theories, colorism, and the decade’s
research on families of color. Journal of Marriage and Family. 72, 3 (2010), 440–459. DOI: https://doi.org/10.1111/j.17413737.2010.00712.x.
[12] Chen, Y.T., Smith, A.D.R., Reinecke, K. and To, A. 2022. Collecting and reporting race and ethnicity data in HCI. CHI Conference on
Human Factors in Computing Systems Extended Abstracts (New York, NY, USA, Apr. 2022), 1–8.
[13] Chidambaram, V., Chiang, Y.-H. and Mutlu, B. 2012. Designing persuasive robots: How robots might persuade people using vocal
and nonverbal cues. Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction (New York,NY, 2012), 293–300.
[14] Chung, K.-M. and Shin, D.-H. 2015. How anthropomorphism affects human perception of color-gender-labeled pet robots.
Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction Extended Abstracts (New York, NY,
USA, 2015), 75–76.
[15] Considine, J., Botti, M. and Thomas, S. 2005. Design, format, validity and reliability of multiple choice questions for use in nursing
research and education. Collegian. 12, 1 (Jan. 2005), 19–24. DOI: https://doi.org/10.1016/S1322-7696(08)60478-3.
[16] Crilly, N. 2011. Do users know what designers are up to? Product experience and the inference of persuasive intentions.
International Journal of Design. 5, 3 (2011), 1–15.
[17] Duffy, B.R. 2003. Anthropomorphism and the social robot. Robotics and Autonomous Systems. 42, 3 (Mar. 2003), 177–190. DOI:
https://doi.org/10.1016/S0921-8890(02)00374-3.
[18] Eberhardt, J.L., Goff, P.A., Purdie, V.J. and Davies, P.G. 2004. Seeing black: Race, crime, and visual processing. Journal of Personality
and Social Psychology. 87, 6 (2004), 876–893. DOI: https://doi.org/10.1037/0022-3514.87.6.876.
[19] Eyssel, F. and Hegel, F. 2012. (S)he’s got the look: Gender stereotyping of robots. Journal of Applied Social Psychology. 42, 9 (2012),
2213–2230. DOI: https://doi.org/10.1111/j.1559-1816.2012.00937.x.
[20] Fischer, K., Jung, M., Jensen, L.C. and aus der Wieschen, M.V. 2019. Emotion expression in HRI – When and why. 2019 14th
ACM/IEEE International Conference on Human-Robot Interaction (HRI) (Daegu, Korea, Mar. 2019), 29–38.
[21] Frauenberger, C. 2019. Entanglement HCI: The next wave? ACM Transactions on Computer-Human Interaction. 27, 1 (Nov. 2019),
2:1-2:27. DOI: https://doi.org/10.1145/3364998.
[22] Gambino, A., Fox, J. and Ratan, R.A. 2020. Building a stronger CASA: Extending the computers are social actors paradigm. HumanMachine Communication. 1, (2020), 71–85.
[23] de Graaf, M., Perugia, G., Fosch-Villaronga, E., Lim, A., Broz, F., Short, E.S. and Neerincx, M. 2022. Inclusive HRI: Equity anddiversity in design, application, methods, and community. Proceedings of the 2022 ACM/IEEE International Conference on HumanRobot Interaction (2022), 1247–1249.
[24] Guess, T.J. 2006. The social construction of whiteness: Racism by intent, racism by consequence. Critical Sociology. 32, 4 (2006), 649–673.
[25] Head, T.C., Griffin, R.W., Bateman, T.S., Lohman, L. and Yates, V.L. 1988. The priming effect in task design research. Journal ofManagement. 14, 1 (Mar. 1988), 33–39. DOI: https://doi.org/10.1177/014920638801400104.
[26] Hidalgo-Ternero, C.M. and Mogorrón Huerta, P. 2020. Google Translate vs. DeepL: Analysing neural machine translation
performance under the challenge of phraseological variation. Análisis multidisciplinar del fenómeno de la variación fraseológica en
traducción e interpretación / Multidisciplinary Analysis of the Phenomenon of Phraseological Variation in Translation and Interpreting.
MonTI Special Issue 6 (2020), 154–177. DOI: https://doi.org/10.6035/MonTI.2020.ne6.5.
[27] Hogg, M.A. 2016. Social Identity Theory. Understanding Peace and Conflict Through Social Identity Theory: Contemporary Global
Perspectives. S. McKeown, R. Haji, and N. Ferguson, eds. Springer International Publishing. 3–17.
[28] Hu, Y. and Hoffman, G. 2019. Using skin texture change to design emotion expression in social robots. 2019 14th ACM/IEEEInternational Conference on Human-Robot Interaction (HRI) (Mar. 2019), 2–10.
[29] Hunter, M.L. 2002. “if you’re light you’re alright”: Light skin color as social capital for women of color. Gender & Society. 16, 2 (Apr.
2002), 175–193. DOI: https://doi.org/10.1177/08912430222104895.
[30] Hyde, J.S., Bigler, R.S., Joel, D., Tate, C.C. and van Anders, S.M. 2019. The future of sex and gender in psychology: Five challenges to
the gender binary. American Psychologist. 74, 2 (2019), 171–193. DOI: https://doi.org/10.1037/amp0000307.
[31] Iwabuchi, K. 2002. Recentering Globalization: Popular Culture and Japanese Transnationalism. Duke University Press.

ACM Trans. Hum.-Robot Interact.

19[32] Kato, H. and Bauer, R. 2021. Mukokuseki and the narrative mechanics in Japanese games. Narrative Mechanics: Strategies andMeanings in Games and Real Life. B. Suter, R. Bauer, and M. Kocher, eds. transcript Verlag. 113–150.
[33] Lee, S., Lau, I.Y., Kiesler, S. and Chiu, C.-Y. 2005. Human mental models of humanoid robots. Proceedings of the 2005 IEEE
International Conference on Robotics and Automation (Barcelona, Spain, Apr. 2005), 2767–2772.
[34] Litman, L. and Robinson, J. 2020. Conducting Online Research on Amazon Mechanical Turk and Beyond. SAGE Publications.
[35] Makatchev, M., Simmons, R., Sakr, M. and Ziadee, M. 2013. Expressing ethnicity through behaviors of a robot character. Proceedingsof the 8th ACM/IEEE International Conference on Human-Robot Interaction (2013), 357–364.
[36] Marchesi, S., Roselli, C. and Wykowska, A. 2021. Cultural values, but not nationality, predict social inclusion of robots. Social
Robotics (Cham, 2021), 48–57.
[37] Mathison, S. 1988. Why triangulate? Educational Researcher. 17, 2 (Mar. 1988), 13–17. DOI:
https://doi.org/10.3102/0013189X017002013.
[38] Murphy-Shigematsu, S. 1993. Multiethnic Japan and the Monoethnic Myth. MELUS. 18, 4 (1993), 63–80. DOI:
https://doi.org/10.2307/468120.
[39] Nass, C., Steuer, J. and Tauber, E.R. 1994. Computers are social actors. Proceedings of the SIGCHI conference on Human factors incomputing systems (Boston, MA, 1994), 72–78.
[40] Pandey, A.K. and Gelin, R. 2018. A mass-produced sociable humanoid robot: Pepper: The first machine of its kind. IEEE Robotics
Automation Magazine. 25, 3 (Sep. 2018), 40–48. DOI: https://doi.org/10.1109/MRA.2018.2833157.
[41] Perugia, G., Guidi, S., Bicchi, M. and Parlangeli, O. 2022. The shape of our bias: Perceived age and gender in the humanoid robots of
the ABOT database. Proceedings of the 2022 ACM/IEEE International Conference on Human-Robot Interaction (Sapporo, Hokkaido,
Japan, Mar. 2022), 110–119.
[42] Powell-Hopson, D. and Hopson, D.S. 1988. Implications of doll color preferences among black preschool children and whitepreschool children. Journal of Black Psychology. 14, 2 (Feb. 1988), 57–63. DOI: https://doi.org/10.1177/00957984880142004.
[43] Reeves, B. and Nass, C. 1996. The Media Equation: How people treat computers, television, and new media like real people and places.
Cambridge University Press.
[44] Saha, A. 2019. Manga as mukokuseki (stateless)? Hybridism in original non-Japanese manga. Special Issue – Manga, Comics andJapan: Area Studies as Media Studies. 156, (2019), 86–97.
[45] Saunderson, S. and Nejat, G. 2019. How robots influence humans: A survey of nonverbal communication in social human–robot
interaction. International Journal of Social Robotics. 11, 4 (Aug. 2019), 575–608. DOI: https://doi.org/10.1007/s12369-019-00523-0.
[46] Schaller, M. 1985. The American Occupation of Japan: The Origins of the Cold War in Asia. Oxford University Press on Demand.
[47] Schlesinger, A., Edwards, W.K. and Grinter, R.E. 2017. Intersectional HCI: Engaging identity through gender, race, and class.
Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, May 2017), 5412–5427.
[48] Schlesinger, A., O’Hara, K.P. and Taylor, A.S. 2018. Let’s talk about race: Identity, chatbots, and AI. Proceedings of the 2018 CHI
Conference on Human Factors in Computing Systems (New York, NY, USA, Apr. 2018), 1–14.
[49] Seaborn, K. 2022. From identified to self-identifying: Social Identity Theory for socially embodied artificial agents. Proceedings of the
HRI 2022 Workshop on Robo-Identity 2 (Sapporo, Hokkaido, Japan, 2022).
[50] Seaborn, K. and Frank, A. 2022. What pronouns for Pepper? A critical review of gender/ing in research. Proceedings of the 2022 CHI
Conference on Human Factors in Computing Systems (New Orleans, Louisiana, USA, 2022), 1–15.
[51] Seaborn, K., Pennefather, P. and Kotani, H. 2022. Exploring gender-expansive categorization options for robots. Extended Abstractsof the 2022 CHI Conference on Human Factors in Computing Systems (New Orleans, LA, 2022), 1–6.
[52] Sparrow, R. 2020. Do robots have race?: Race, social construction, and HRI. IEEE Robotics Automation Magazine. 27, 3 (Sep. 2020),
144–150. DOI: https://doi.org/10.1109/MRA.2019.2927372.
[53] Sparrow, R. 2020. Robotics has a race problem. Science, Technology, & Human Values. 45, 3 (May 2020), 538–560. DOI:
https://doi.org/10.1177/0162243919862862.
[54] Spatola, N., Anier, N., Redersdorff, S., Ferrand, L., Belletier, C., Normand, A. and Huguet, P. 2019. National stereotypes and robots’perception: The “made in” effect. Frontiers in Robotics and AI. 6, (2019), 21. DOI: https://doi.org/10.3389/frobt.2019.00021.
[55] Spiel, K., Haimson, O.L. and Lottridge, D. 2019. How to do better with gender on surveys: A guide for HCI researchers. Interactions.
26, 4 (Jun. 2019), 62–65. DOI: https://doi.org/10.1145/3338283.
[56] Stanfill, M. 2018. The unbearable whiteness of fandom and fan studies. A Companion to Media Fandom and Fan Studies. John Wiley& Sons, Ltd. 305–317.
[57] Steele, C.M. 1997. A threat in the air: How stereotypes shape intellectual identity and performance. American Psychologist. 52,(1997), 613–629. DOI: https://doi.org/10.1037/0003-066X.52.6.613.
[58] Stock-Homburg, R. 2022. Survey of emotions in human–robot interactions: Perspectives from robotic psychology on 20 years of
research. International Journal of Social Robotics. 14, 2 (Mar. 2022), 389–411. DOI: https://doi.org/10.1007/s12369-021-00778-6.
[59] Sue, D.W. 2006. The invisible whiteness of being: Whiteness, white supremacy, white privilege, and racism. Addressing Racism:
Facilitating Cultural Competence in Mental Health and Educational Settings. John Wiley & Sons, Inc. 15–30.
[60] Tajfel, H. 1978. Social categorization, social identity and social comparison. Differentiation Between Social Group. (1978), 61–76.
[61] Tajfel, H. and Turner, J.C. 2004. The social identity theory of intergroup behavior. Political Psychology. Psychology Press. 276–293.
[62] Tonkin, M., Vitale, J., Herse, S., Raza, S.A., Madhisetty, S., Kang, L., Vu, T.D., Johnston, B. and Williams, M.-A. 2019. Privacy first:
Designing responsible and inclusive social robot applications for in the wild studies. 2019 28th IEEE International Conference on
Robot and Human Interactive Communication (New Delhi, India, Oct. 2019), 1–8.

ACM Trans. Hum.-Robot Interact.

20[63] Triandafyllidou, A. 1998. National identity and the “other.” Ethnic and Racial Studies. 21, 4 (Jan. 1998), 593–612. DOI:
https://doi.org/10.1080/014198798329784.
[64] Turner, J.C., Hogg, M.A., Oakes, P.J., Reicher, S.D. and Wetherell, M.S. 1987. Rediscovering the social group: A self-categorization
theory. Basil Blackwell.
[65] Urakami, J. and Seaborn, K. 2023. Nonverbal cues in human-robot interaction: A communication studies perspective. ACM
Transactions on Human-Robot Interaction. 12, 2 (Mar. 2023), 1–21. DOI: https://doi.org/10.1145/3570169.

ACM Trans. Hum.-Robot Interact.