# Kawaii Computing: Scoping Out the Japanese Notion of Cute in User
Experiences with Interactive Systems

Yijia Wang<EMAIL_ADDRESS>0009-0004-2250-9163 Tokyo Institute of
TechnologyTokyoJapan and  Katie Seaborn<EMAIL_ADDRESS>0000-0002-7812-9096 Tokyo Institute of TechnologyTokyoJapan

(2024)

###### Abstract.

Kawaii computing is a new term for a steadily growing body of work on the
Japanese notion of “cute” in human-computer interaction (HCI) research and
practice. Kawaii is distinguished from general notions of cute by its
experiential and culturally-sensitive nature. While it can be designed into
the appearance and behaviour of interactive agents, interfaces, and systems,
kawaii also refers to certain affective and cultural dimensions experienced by
culturally Japanese users, i.e., kawaii user experiences (UX) and mental
models of kawaii elicited by the socio-cultural context of Japan. In this
scoping review, we map out the ways in which kawaii has been explored within
HCI research and related fields as a factor of design and experience. We
illuminate theoretical and methodological gaps and opportunities for future
work on kawaii computing.

Kawaii computing, kawaii, computer agents, virtual characters, robots, scoping
review, literature review, human-computer interaction

††journalyear: 2024††copyright: rightsretained††conference: Extended Abstracts
of the CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Extended Abstracts of the CHI Conference on
Human Factors in Computing Systems (CHI EA ’24), May 11–16, 2024, Honolulu,
HI, USA††doi: 10.1145/3613905.3651001††isbn: 979-8-4007-0331-7/24/05††ccs:
Human-centered computing Human computer interaction (HCI)††ccs: General and
reference Surveys and overviews††ccs: Applied computing Psychology

##  1\. Introduction

”Kawaii” is the Japanese term for ”cute,” simply put. Kawaii is recognized the
world over as representative of Japanese popular culture (Nittono, 2010). Not
limited in Japan, kawaii has been adopted as a global phenomenon across
various forms of media and beyond (Seaborn et al., 2023c). It plays a critical
role in the worldwide success of many Japanese products (Laohakangvalvit et
al., 2021) and cultural exports (Kinsella, 2013; Lieber-Milo and Nittono,
2019), such as Hello Kitty (Kovarovic, 2011) and the Pokémon series of anime
and games (Allison, 2003). Although kawaii is associated with the general idea
of ”cute” and often translated into English as ”cute,” there are nuanced
differences between these concepts that psychological research has linked to
Japanese culture (Nittono, 2016). Kawaii has positive meanings, such as
lovable, adorable, pretty, charming, and sometimes pity (Seaborn et al.,
2023c; Nittono, 2016; Laohakangvalvit et al., 2019; Berque et al., 2022;
Laohakangvalvit et al., 2016; Patrick Rau et al., 2018). However, kawaii is
not only a matter of appearance, but also a positive affective response that,
among Japanese people, can be heightened by the social context (Nittono,
2022).

The concept of kawaii has been explored in psychology from cognitive science
and behavioural perspectives (Nittono, 2010, 2016, 2022). A small amount of
work in human-computer interaction (HCI), human-robot interaction (HRI), and
human-agent interaction (HAI) (Dobrosovestnova et al., 2022; Seaborn et al.,
2023a) has followed suit. A wide array of often disconnected work exists.
Kawaii has been explored in agent design (e.g., crafting agents with kawaii
features to improve user ratings (Samani et al., 2012; Scissors et al.,
2011)), user experience or UX (e.g., people having more tolerance (Lu et al.,
2021), care responses to agents (Nakanishi et al., 2022)), interpretive
frameworks (e.g., a visual framework of kawaii attributes (Sripian et al.,
2020), and user models, including those grounded in participant data
(Laohakangvalvit et al., 2019, 2022)). However, little research has
distinguished kawaii and cute clearly in HCI context. This makes it difficult
to clarify what is meant by ”kawaii,” especially when the term is used outside
of the Japanese context, i.e., without Japanese researchers, designers, and/or
users (e.g., compare (Zhang et al., 2021)) and (Samani et al., 2012)). Indeed,
there is yet no kawaii computing ”field of study” nor consensus, such as by
way of a systematic review. This creates a disconnect between HCI and related
domains with the strong theoretical and empirical foundation established in
psychology (Nittono, 2010, 2022, 2016). We appear to have a gap in our
understanding of how kawaii has been, could be, and perhaps should be explored
in HCI and adjacent fields.

In this preliminary work, we offer a scoping review (Munn et al., 2018; Tricco
et al., 2018) to better understand how kawaii has been used in HCI and related
fields as a factor of interaction design and UX. Our goal was to map out and
delineate kawaii computing as a concrete domain in HCI. To this end, we asked
several sub-questions so as to be comprehensive in our coverage of the
research content offered by this wide-reaching literature: What is ”kawaii”?
How has it been defined and operationalized? What fields and contexts of use
has kawaii been explored in? What agents, interfaces, and systems have been
deemed to embody kawaii design? What methods have been employed to study it as
a UX phenomenon? What factors have been found to influence user perceptions of
and reactions to kawaii? How has kawaii been approached as an object of study
in HCI? We contribute a map of research that may be characterized as kawaii
computing, as well as identify gaps and opportunities for future work. We set
the stage for a more robust kawaii future in HCI.

##  2\. Methods

We carried out a scoping review of the literature using the guidelines by Munn
et al. (2018) and the protocol offered by the Preferred Reporting Items for
Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-
ScR) (Tricco et al., 2018). The purpose of a scoping review, compared to other
types of reviews, is to map out the “scope” of a topic or domain of study to
identify gaps and generate new research directions (Munn et al., 2018).
Typically, scoping reviews reveal the state of affairs and help determine
whether a systematic review is possible or necessary. Scoping reviews can also
act as a way of solidifying an emerging area of focus, which we believe to be
true in the case of kawaii computing. Our process is presented in
Supplementary Materials Figure 1. Notably, we deviated from the PRISMA-ScR by
not implementing those parts related to conventions in the medical field,
e.g., structured abstracts. Our protocol was registered on January 11th,
2024111https://osf.io/g5knm. We provide our data set as part of the Open
Science initiative (Nature, 2017) here:

https://bit.ly/kawaiicomputing.

###  2.1. Terms and Definitions

####  2.1.1. Kawaii

We restricted our focus to the Japanese conceptualization of “kawaii.”
However, reports written in English by and about Japanese people may translate
kawaii as “cute” (Nittono et al., 2012). We structured our search queries
around this possibility by considering “cute” within the context of Japan or
Japanese participants. During screening, we carefully checked whether the
authors or participants were Japanese when “cute” rather than “kawaii” was
used.

####  2.1.2. Kawaii Computing

We characterize “kawaii computing” as _all human-centred research on kawaii
involving computer-based agents, interfaces, environments, and systems_. This
broad definition includes virtual characters, UI design patterns, machine
learning techniques to process human data, user reactions to chatbots, and so
on. The guiding principle is of kawaii as a fundamental psychological
phenomenon premised in Japanese culture and experienced as emotion,
cognitions, and/or behaviours by Japanese people, in line with the work of
Nittono and colleagues (Nittono et al., 2012).

###  2.2. Eligibility Criteria

We included research reports (short and long papers, conference proceedings
and journals, etc.) on kawaii computing (according to the Japanese notion or
“cute”) from 2000 to 2023. We excluded non-human subjects research,
researching not involving computer-based systems (not relevant to HCI), grey
literature (unverifiable quality or publication status), and papers not in
English, Japanese or Chinese languages (the languages known to the authors).

###  2.3. Information Sources and Search Strategy

We searched disciplinary databases relevant to HCI—the ACM Digital Library and
IEEE Xplore–as well as Web of Science, a general database to which we had
access through our institution, for HCI research or edge cases published
outside of ACM and IEEE. We ran our searches between January 3rd and 25th,
2024. Our basic query structure was: _kawaii OR (cute AND Japan) AND (human
factors computing OR human-computer interaction OR interaction design)_. Full
details for each database search query are presented in Table 1. We used
Zotero to store, remove duplicates, and count the downloaded records returned
by these searches. Refer to Supplementary Materials Figure 1 for counts at
each stage.

Table 1. Search queries used in each data base and number of records returned. Database |  Query |  Filters |  Count |  Date
---|---|---|---|---
ACM DL |  [[All: ”kawaii”] OR [All: ”cute in japan”]] AND [[All: ”computer”] OR [All: ”computing”] OR [All: ”interactive design”] OR [ALL:”robot”]] |  / |  43 |  2024.1.3
|  [[All: ”kawaii”] OR [[All: ”cute”] AND [All: ”japan”]]] AND [[All: ”human factors computing”] OR [All: ”human-computer interaction”] OR [All: ”interaction design”]] |  Research Article |  146 |  2024.1.10
|  [[All: ”kawaii”] OR [[All: ”cute”] AND [All: ”japan”]]] AND [[All: ”human factors computing”] OR [All: ”human-computer interaction”] OR [All: ”interaction design”]] |  Abstract, Poster, Extended Abstract, Short paper, Work in progress |  38 |  2024.1.24
IEEE Xplore |  ((kawaii OR (cute AND Japan)) AND (human factors computing OR human-computer interaction OR interaction design) ) |  / |  10 |  2024.1.10
Web of Science |  ((kawaii OR (cute AND Japan)) AND (human factors computing OR human-computer interaction OR interaction design) )(All Fields) |  / |  44 |  2024.1.10

###  2.4. Screening and Conflict Management

Two authors carried out two screening stages. The first involved checking the
title, abstract, and keywords for face viability. This was done by the first
author. The second author then checked the excluded records, re-including as
needed. The second phase of screening involved both reading pertinent parts of
the full texts with the aid of keyword searches to better determine viability.
Importantly, we expected to find one of the author’s own work returned. When
this occurred, screening, data extraction and synthesis of these studies was
conducted by the other author.

###  2.5. Data Items and Data Extraction

We used Google Sheets to screen, extract data, and carry out analysis of the
data. Specifically, we extracted the following: metadata, including Authors,
Year of publication, Title, Abstract, and URL; Topic, specifically Topic of
study and Context; Terms, including Definition of kawaii, whether
Explicit/implied, Citations for definitions/, and whether there was No mention
of kawaii but from the Japanese context; Stimuli, including Stimuli details,
the Measure of kawaii, and what Kawaii factors were identified; Methods,
including Type of research, Research design, Participant nationality,
Demographics, Sample size, and Data Analysis; Agent Types, including Virtual
agents, Robots with a physical body, Interfaces, and Other; and How was Kawaii
used, including Design (Feature, Control), User Reaction (Self-Reports,
Biometrics), and Interpretive Framework (Data Analysis, Machine Learning,
Heuristic analysis).

###  2.6. Data Analysis

We carried out two data analyses. For quantitative data, we generated
descriptive statistics, including counts and percentages. For qualitative
data, the first author used content analysis to categorize the ”broad surface
structure,” focusing on surface meanings rather than latent interpretations,
also called a ”manifest analysis” (Bengtsson, 2016).

##  3\. Results

We identified 238 records through database searching. After screening and
eligibility, 69 records were included (Supplementary Materials Figure 1). The
full list of included papers is available at https://bit.ly/kawaiicomputing

###  3.1. Kawaii in Topics and Contexts

We found seven contexts and a variety of topics in kawaii computing. The most
common context was HRI (n=29, 42%), followed by HCI (n=16, 23%) and HAI (n=15,
22%). Several papers covered machine learning (ML) (n=4, 6%), human-machine
interaction (HMI) (n=2, 3%), culture (n=2, 3%) and human-human communication
(n=1, 1%).

Although covering a wide array of topics, most papers only referenced kawaii
in participants’ open-ended answers and the authors’ discussion. Less than
half (n=31, 45%) included kawaii as a main topic. In HAI, models for kawaii
virtual agents were proposed, e.g., Ohtsuka et al. (2022) set up kawaii
attributes for evaluating kawaii virtual characters and Seaborn et al. (2023c)
proposed a preliminary model for kawaii game vocalics. Others evaluated user
feelings of kawaii-ness towards virtual characters (Laohakangvalvit et al.,
2016; Sajadieh, 2023), 3D models of animals (Tsuji et al., 2019), VTubers (Lu
et al., 2021), and preferences for kawaii and non-kawaii game characters
(Poeller et al., 2021). In HRI, kawaii was framed as a feature of robot
design, with the focus on identifying kawaii factors, e.g., NAO robot (Patrick
Rau et al., 2018), 3D robots (Laohakangvalvit et al., 2021; Ohkura et al.,
2022), commercial delivery robots (Dobrosovestnova et al., 2022), virtual
cross-cultural kawaii robots (Berque et al., 2022), and the design of Pepper’s
touch motion to express kawaii (Okada et al., 2020). In HCI, kawaii was
applied in social media, e.g., Hashiguchi and Ogawa (2011) proposed a kawaii
search engine for blogs, and Berque and Chiba (2017) proposed and evaluated
LINE’s kawaii chat interface. Kawaii was also introduced to design other
interfaces, such as the appearance of Kissenger (a remote communication
device) (Samani et al., 2012), the LiveDeck interface for real-time
collaborative editing (Scissors et al., 2011), and a research through design
(RtD) critical analysis of the aesthetic object design fiction ”Menstruation
Machine” (Bardzell et al., 2015). In ML, Sripian et al. (2020) and
Laohakangvalvit et al. (2021) developed framework to classify kawaii fashion
with colour and clothing. Laohakangvalvit et al. (2019) also proposed a model
to compare the kawaiiness of online images of cosmetic bottles. Hohendanner et
al. (2023) explored the influence of culture-specific narratives on the
imagination of future AI technology, in which kawaii is viewed as a specific
feature of Japanese culture.

###  3.2. Distribution of Kawaii Computing Research

Since kawaii is a concept from Japan and directly tied to Japanese culture, we
considered participant demographics. Most studies included Japanese
participants (n=56, 81%). Among these, 44 papers included only Japanese
participants (64%) and 12 included both Japanese and participants from other
countries (17%) and aimed at cross-cultural comparison or generalization. For
example, Ohkura et al. (2022) compared kawaii robot designs by Japanese and
U.S. students, and Laohakangvalvit et al. (2019, 2016) included both Japanese
and Thai participants to establish a framework for kawaii feeling evaluations.
Some papers did not include Japanese participants (7%). These papers
introduced ”kawaii” into other culture contexts. For example, Lu et al. (2021)
studied kawaii as a part of otaku culture, which influenced Chinese viewer
perceptions and attitudes towards live-stream VTubers. Patrick Rau et al.
(2018) also had a study in Chinese context to investigate if kawaii design is
valuable to Chinese customers. The three papers about ML had no participants
(6%). Other demographics used were gender, age, occupation, education, etc.
Notably, most work included combinations of women and men, except one paper
for a new HMI interface for a straddle-type vehicle design tested with men
(Suegami et al., 2017). One paper investigated the effect of robotic mediation
in promoting conversation among older adults for improving behavioural and
psychological symptoms of dementia (BPSD) and included only women (Yamazaki et
al., 2019). Another was about a system with kawaii (cute in Japan) icons for
product retrieval and only tested women because the design was inspired by the
psychology of women’s shopping activity (Koike and Itoh, 2015). Many papers
(n=17, 25%) focused on students, from junior high to college, and another six
(9%) focused on younger generations (under 30 years old) (Sakashita et al.,
2017; Samani et al., 2012; Mashimo et al., 2016; Ohkubo et al., 2016;
Nakanishi et al., 2019; Xu et al., 2014). Four focused on kawaii computing for
older adults (7%), e.g., comparing perceptions of kawaii between the young and
the old (Patrick Rau et al., 2018), a kawaii social robot for children-older
adult interaction (Nakanishi et al., 2022), and conversational robots for a
care center (Sabelli et al., 2011) and older adults with dementia (Yamazaki et
al., 2019). Two (4%) conducted research on children: a kawaii social robot
mediating children-older adult interaction (Nakanishi et al., 2022), and
delivering negative feedback for improving the in-class learning experience
(Sakamoto et al., 2023).

###  3.3. Definition of Kawaii

More than a half papers (n=41, 65%) did not mention the word ”kawaii,” but we
can infer that the use of ”cute” meant ”kawaii” because of the Japanese
context. We equate uses of ”cute” within the Japanese context to the concept
of kawaii. In 96 papers, 24 (35%) used the definition of kawaii or cute in the
Japanese context. 17 (25%) provided an explicit definition (13 with citations,
4 without citations), and nine (13%) provided an implicit definition (5 with
citations, 4 without citations). The citations were sourced from critical,
cultural, and psychological studies.

For explicit definitions, most wrote that kawaii means Japanese cuteness,
period. Ohkura et al. (2022, abstract) defined kawaii as ”a Japanese adjective
representing cute and adorable” (Marcus et al., 2017; Ohkura, 2019; Nittono et
al., 2012; Nittono, 2019). Seaborn et al. (2023c, b) characterized kawaii as
the Japanese concept of ”cute++,” linked to cognition, behaviour, and emotion,
citing Nittono (2010). They also referenced impressions of cuteness, charm,
endearment, and pity. Similarly, Scissors et al. (2011, p. 1126) explained
that kawaii is ”an aspect of Japanese culture that refers to cute things that
evoke positive emotions and feelings of social affiliation,” also citing
Nittono (2010). Some mentioned that kawaii has positive connotations.Berque et
al. (2022) used the same definition in their previous work (Berque and Chiba,
2017) that described kawaii as Japanese cuteness, a concept ingrained in
Japanese contemporary society. They looked into Japanese history and
literature, which described kawaii as pitiful, shameful, or too sad to see,
small, weak, and someone or something that gives one the feeling of “wanting
to protect” (Yomota, 2006). Laohakangvalvit et al. (2019, 2016, 2021) defined
kawaii as a positive adjective that denotes such meanings as cute or lovable.
They state that it is critical as a kansei value plays a leading role in the
worldwide success of Japanese products, such as Hello Kitty (Kovarovic, 2011)
and Pokémon (Allison, 2003). Okada et al. (2020) and Minakawa and Takada
(2017) defined kawaii as a Japanese word that has positive connotations as
cute (Nittono et al., 2012; Nittono, 2016), which is employed when designing
robot. Okada et al. (2020, p. 11276) position kawaii as is a critical factor
in Japanese commercial aspects and pop culture (Kinsella, 2013; Lieber-Milo
and Nittono, 2019).

Others provided a definition of kawaii in design. Hashiguchi and Ogawa (2011)
linked kawaii with pretty or lovely design. Patrick Rau et al. (2018, 258)
characterized kawaii as ”an affective concept rooted in Japanese aesthetics”.
For artificial products, they thought kawaii is a critical factor of Japanese
kansei design, which can mean cute, lovely, small, and other related emotional
values (Ohkura and Aoto, 2010). Dobrosovestnova et al. (2022) described kawaii
in the context of robot design. Kawaii, an important concept in Japanese pop
culture and design (Okada et al., 2020), has been operationalized in some
studies in HRI (Berque et al., 2020), as not only the perceivable features of
robots, but also sociality and approach motivation (Dobrosovestnova et al.,
2022; Nittono, 2022). Mubin et al. (2020) also described kawaii in robot
design as descriptive features that characterize a robot, such as manga eyes
and low resemblance to a human. Some related kawaii with girlish. Sajadieh
(2023, p. 4) proposed that ”kawaii has become an important component of
aesthetics in Japanese consumer culture and has also spread to international
markets and audiences”, which became a popular way to describe female idols in
80s Japanese pop culture (Sone, 2016). They also argued that kawaii inspired
the performance of cuteness, innocence, and femininity which contributes to
the fetishization and commodification of a particular type of culturally-
desired gender dynamics: cute artificial women and male consumers (Black,
2008). Bardzell et al. (2015) described kawaii style as Japanese cute girl
aesthetic characterized by rainbow-colored hair, glamour make-up and so on.

Implicit definitions of kawaii were introduced with reference to other studies
or general knowledge, without author clarification. Kawaii was described as am
important part of otaku culture (Lu et al., 2021), by way of agents like
Kewpie (Hohendanner et al., 2023), which is one of the most universally
recognized symbols of cuteness in Japan (Riessland, 1998), attractive in a
pretty or endearing way (Kimura and Nakajima, 2022), an appearance similar to
a stuffed toy (Samani et al., 2012; Ootsubo and Inoue, 2022), borderline
before reaching the Uncanny Valley (Cheok and Cheok, 2010), and Harajuku
street fashion as kawaii fashion (Sripian et al., 2020). Ohtsuka et al. (2022)
and Sripian et al. (2020) agreed that kawaii attracts attention as an
affective value. Sripian et al. (2020) further mentioned there are various
kawaii attributes for design, such as color (Ohkura et al., 2014).

We used word frequency analysis to analyze the 20 definitions extracted. The
top five frequency words (except kawaii) were ”Japanese” (n=27), ”cute”
(n=13), ”cuteness” (n=9), ”culture” (n=8), ”positive” (n=6), ”pop” (n=5).
”Japan,” ”affective,” ”products,” and ”important” (n=4) followed. This
indicates that the definition of kawaii is highly related to a Japanese pop
and consumer culture, is deemed connected to ”cute,” and has positive
meanings.

###  3.4. Kawaii Stimuli, Measurement, and Factors

We found six general stimuli: visual, audio, movements, gestures, and text.
Most of studies focus on one stimuli (n=49, 71%). The most common stimuli was
visual (n=83, 87%), followed by movements (n=12, 17%), audio (n=8, 12%),
gestures (n=2, 3%), text (n=1, 1%), and animation (n=1, 1%). The specific
stimuli for visual were various, e.g., virtual agents, robots, avatars,
images, emotes, etc. The movement stimuli were mainly robots with entities
(n=8) followed by interfaces (n=2) (Ohkubo et al., 2016; Ootsubo and Inoue,
2022), virtual agents (n=2) (Sakashita et al., 2017; Gui et al., 2023), and
cars with eyes (Gui et al., 2023). As for audio stimuli, the specific stimuli
were game vocalics (Seaborn et al., 2023c), robot (Edirisinghe et al., 2023;
Kasuga et al., 2017; Nakanishi et al., 2018), Hatsune Miku virtual character
and TTS voice (Sajadieh, 2023), and mucis vidio (Bardzell et al., 2015).
Specific stimuli of gestures were virtual agent (Matsui and Yamada, 2016), and
robots (Kanda et al., 2023).

41 papers (59%) proposed measurements of kawaii (refer to Supplementary Table
1 for full details). Among them, six papers measured kawaiiness without
discussing kawaii factors. On the other hand, 15 papers discussed kawaii
factors but did not provide or use measurements of kawaii. Researchers
considered a variety of kawaii factors, but given the variety of the research,
it is hard to summarize, so we describe the kawaii factors that occurred most
frequently in our corpus. These were: physical features related to Baby
Schema; anthropomorphism, which is an important feature for non-living things;
and factors related to Japanese culture, such as animation, girlishness, and
colour. Hatada et al. (2019); Shiomi and Hagita (2016); Poeller et al. (2021);
Yeh et al. (2017); Berque et al. (2022); Ohkura et al. (2022) deemed the
physical features of size and shape as one of the most common factors, which
is consistent with Baby Schema. They found that a round shape (Yeh et al.,
2017; Berque et al., 2022) and small size (Hatada et al., 2019; Poeller et
al., 2021) was perceived as more kawaii, because people felt less physical
pressure or stress (Shiomi and Hagita, 2016). Anthropomorphism was another
common factor in robot kawaiiness for visual but also audio stimuli (Seaborn
et al., 2023c; Shinohara et al., 2018). Human-like robots, animal-like robots,
and virtual agents (Sakamoto et al., 2023; Saga et al., 2014) were deemed most
kawaii. Animation and manga styling, which is a symbol of Japanese pop
culture, were linked to greater levels of kawaii (Minakawa and Takada, 2017;
Mubin et al., 2020). Femininity is another common factor related to high
levels of kawaiiness, especially girlishness (Seaborn et al., 2023c; Ohtsuka
et al., 2022; Sajadieh, 2023). Colour was a common kawaii factor. Yeh et al.
(2017) found that people perceived colourful drones as more kawaii.
Laohakangvalvit et al. (2022); Berque et al. (2022); Ohkura et al. (2022);
Patrick Rau et al. (2018) found similar colour factors and results linked to
kawaiiness.

###  3.5. Use of Kawaii

Kawaii has been used in different ways in HCI and related areas. We summarized
how it has been approached in Table 2. Kawaii has be applied in HCI research
for design, UX measurement, and establishment of an interpretive framework. In
HCI design, kawaii has been used as a feature that makes robots more
acceptable to people, especially social robots and interactive communication
agents. Instead of embedding kawaii features in design, Tsuji et al. (2019)
controlled different levels of kawaii and compared these in 3D models to find
a better design. Another study directly included the meaning of kawaii using
the Japanese word ”Puyo” in the product name to appeal to users (FUKUMOTO,
2009). Kawaii, which is related to emotions, can act as a user reaction for UX
evaluations. Furthermore, kawaii has been included in interpretive frameworks.
Laohakangvalvit et al. (2016) constructed a mathematical model of kawaii
feelings, while Laohakangvalvit et al. (2019) proposed a machine learning
model to evaluate kawaii attributes of kawaii products. Sajadieh (2023) used
heuristic analysis to study the kawaii impacts on virtual agent design. Kawaii
thus has versatility as a feature of design and a measurable user reaction or
experience.

Table 2. Summary of how kawaii has been approached in HCI and adjacent domains. Components |  Details |  Papers |  Count (%)
---|---|---|---
Design |  Feature |  (Matsui and Yamada, 2016; Hohendanner et al., 2023; Nakanishi et al., 2022; Nakagawa et al., 2011; Edirisinghe et al., 2023; Samani et al., 2012; Gui et al., 2022; Yeh et al., 2017; Nakanishi et al., 2018; Chang et al., 2022; Scissors et al., 2011; Laohakangvalvit et al., 2019, 2022; Berque et al., 2022; Laohakangvalvit et al., 2016, 2021; Ohkura et al., 2022; Ohtsuka et al., 2022; Berque and Chiba, 2017; Patrick Rau et al., 2018; Sripian et al., 2020; Sajadieh, 2023; Dobrosovestnova et al., 2022; Bardzell et al., 2015; Seaborn et al., 2023b; Minakawa and Takada, 2017; Ohkubo et al., 2016; Kimura and Nakajima, 2022; Ootsubo and Inoue, 2022; Noguchi et al., 2021; Nakanishi et al., 2019; Chen et al., 2021; Mubin et al., 2020) |  34 (49%)
|  Control |  (Tsuji et al., 2019) |  1 (1%)
|  Name |  (FUKUMOTO, 2009) |  1 (1%)
User Reaction |  Self-reports |  (Seaborn et al., 2023c; Lu et al., 2021; Hatada et al., 2019; Nakajima and Niitsuma, 2020; Kanda et al., 2009; Shinohara et al., 2023; Cooney et al., 2014; Allen et al., 2012; Kanda et al., 2023; Cho et al., 2009; Poeller et al., 2021; Yamamoto et al., 2014; Sabelli et al., 2011; Yamazaki et al., 2019; Suegami et al., 2017; Mizuho et al., 2023; Sakamoto et al., 2023; Sakashita et al., 2017; Iwamoto et al., 2022; Saga et al., 2014; Iwamoto et al., 2021; Shinohara et al., 2018; Yeh et al., 2017; Kasuga et al., 2017; Koike and Itoh, 2015; Mashimo et al., 2016; Nakanishi et al., 2018; Scissors et al., 2011; Okada et al., 2020; Tsuji et al., 2019; Berque et al., 2022; Laohakangvalvit et al., 2016, 2021; Ohkura et al., 2022; Ohtsuka et al., 2022; Patrick Rau et al., 2018; Hashiguchi and Ogawa, 2011; Dobrosovestnova et al., 2022; Bardzell et al., 2015; Seaborn et al., 2023b; Fujita et al., 2020; Suzuki and Umemuro, 2012; Ohkubo et al., 2016; Kobayashi et al., 2019; Noguchi et al., 2021; Nakanishi et al., 2019; Xu et al., 2014; Chen et al., 2021; Mubin et al., 2020; Gui et al., 2023) |  50 (72%)
|  Biometrics |  (Laohakangvalvit et al., 2016; Ohkura et al., 2022) |  2 (3%)
Interpretive Framework |  Data Analysis |  (Laohakangvalvit et al., 2016) |  1 (1%)
Machine Learning (ML) |  (Hohendanner et al., 2023; Choi et al., 2019; Laohakangvalvit et al., 2019, 2022; Ohtsuka et al., 2022; Sripian et al., 2020) |  6 (9%)
|  Heuristic Analysis |  (Hashiguchi and Ogawa, 2011; Sajadieh, 2023) |  2 (3%)

##  4\. Discussion

With the global spread of kawaii and Japanese culture, attention to the notion
of ”cute” as a cultural facet of Japan is on the rise. Kawaii research and
design practice is also steadily increasing. When it comes to HCI and related
fields, how do we ride this trend? Although existing studies were not
exhaustive, they do provide inspiration and directions for future work.

###  4.1. Kawaii vs. Cute

The review of kawaii allows us to have an understanding of the differences
between kawaii and cute. Compared to cute, kawaii embodies a particular
Japanese aesthetic and culture frame (Lu et al., 2021; Scissors et al., 2011;
Laohakangvalvit et al., 2022; Berque and Chiba, 2017; Sajadieh, 2023). It goes
beyond just being cute, as cute does not have any cultural connotations. Cute
works in HCI typically applies the ”Baby Schema” concept defined by Lorenz in
1943 (Lorenz, 1943) to guide the creation of interactive artefacts (Fan et
al., 2023). Kawaii, however, is not limited to being a Baby Schema phenomenon,
nor is it simply a feature of design (Nittono, 2019). Nittono (2016) proposed
a two-layer model of kawaii, with one layer as emotion and the other layer as
social value. The kawaii as an emotion frame presents kawaii as a stimulus
perceived as cute, friendly, harmless, pretty and so on, regardless of whether
the stimulus is a living being. When kawaii affect is evaluated as significant
through cognitive appraisal processes, it can be linked to a psychological
state of kawaii (Nittono, 2016). Kawaii is also framed as social value,
referring to kawaii as a medium of sociality (Nittono, 2016). This has be
adopted in many kawaii works as well as in HCI (e.g., (Seaborn et al., 2023c;
Scissors et al., 2011; Okada et al., 2020; Berque et al., 2022; Ohkura et al.,
2022)). A more latent feature of kawaii is its basis in Japanese culture.
Nittono (2019) notes that features particular to Japanese culture appear to
influence and temper the concept of kawaii, such as amae, one’s drive for
acceptance and adoration from other people, and chizimi shikou, the love of
small things. We found no specific reference to these yet.

Research in kawaii computing is not the same topically as research on cute
design based on Baby Schema in HCI. Kawaii is not only the feature of the
design of a robot, virtual agent, or interface, but also emotion that occurs
in the interaction between human-agent, human-robot, and human-interface (and
perhaps between people with a facilitating computer). We propose that kawaii
computing provides an approach to improve the relationship between people and
technologies. Moreover, kawaii may vary in different social contexts,
especially different culture. For example, a robot may become kawaii when it
interacts with your pet (Kasuga et al., 2017). Although work is limited (17%),
some cross-cultural research has found that Japanese people are more sensitive
and connected to kawaii computing (Hohendanner et al., 2023; Scissors et al.,
2011; Berque et al., 2022; Laohakangvalvit et al., 2016). As expected, most
kawaii computing work was done by Japanese researchers and included Japanese
subjects (81%). Still, kawaii as a psychological state of feeling positive
emotions (Nittono, 2016) is not limited in Japan. This shows a gap in cross-
cultural kawaii design and user perceptions of kawaii towards robots, agents,
interfaces, and so on.

###  4.2. An Emerging Map of Kawaii Computing

Kawaii was found across HCI and adjacent contexts, with work in design (51%),
user reactions (75%), and interpretive frameworks (13%). This indicates that
kawaii can play several different roles in research and design. Most work
studied user reactions through participants’ kawaii emotions. There was
limited work on kawaii data analysis frameworks (Laohakangvalvit et al.,
2016), ML structures for kawaii evaluation (Laohakangvalvit et al., 2022;
Ohtsuka et al., 2022; Sripian et al., 2020), and heuristic analysis (Sripian
et al., 2020; Hashiguchi and Ogawa, 2011). However, even much work covered
kawaii user reactions, there was no universal framework for kawaii
measurement. Most researchers created their own scales (Seaborn et al., 2023c;
Nakajima and Niitsuma, 2020; Suegami et al., 2017; Sakashita et al., 2017;
Ohtsuka et al., 2022; Chen et al., 2021; Mubin et al., 2020), some employed
(un/validated) items from other kawaii works (Nittono, 2019, 2016) in
psychological and behavioural fields (Okada et al., 2020; Tsuji et al., 2019;
Nakanishi et al., 2019), and two used the objective measurement of biometrics
(Ohkura et al., 2022; Patrick Rau et al., 2018). Many (28%) studied kawaii
both as a feature of design and as a user reaction, which is consistent with
the two-factor model of kawaii: not only features but also kawaii emotions in
the interaction. Kawaii attributes of design were various within and across
agents, robots, and interfaces (Shinohara et al., 2018; Yeh et al., 2017;
Laohakangvalvit et al., 2022; Minakawa and Takada, 2017; Mubin et al., 2020;
Gui et al., 2023), which is similar to the situation of kawaii measures. This
shows a need for researchers to establish a validated frameworks for
understanding the components of kawaii computing.

###  4.3. An Agenda for Future Work

Based on this scoping review, we offer some ideas and prompts for future work
on kawaii computing.

  * •

Standardization: Devise a taxonomy of attributes (stimulus design) and
standard measures (user response) for kawaii computing. In HCI and related
domains, kawaii attributes across agents, robots, and interface designs as
well as measures of evaluating kawaii are fragmented. Most relied on
(unvalidated) self-designed materials based on the specific context.
Establishing sets of attributes for kawaii computing and interpretive
frameworks for evaluating kawaii reactions in HCI, HRI, and HAI will be a key
endeavour.

  * •

Multiple Modalities: Explore kawaii computing with stimuli beyond the visual.
The body of work covered in this review indicates a focus on visual stimuli.
While only a few works involved other stimuli, such as audio (Seaborn et al.,
2023c, b), HCI is not always visually based, and sometimes uses only non-
visual modalities, e.g., voice assistants.

  * •

Cultural Significance: Adapt kawaii cross-culturally. ”Kawaii” is defined by
and has a reciprocal relationship with Japanese culture. Most research has
been conducted in Japan or by Japanese people. However, the power of kawaii
may not only be limited to the Japanese context. Future work can study the
cross-cultural effects on and differences in kawaii perceptions and
experiences. For example, comparisons of Japanese and non-Japanese foreigners
living in Japan, or comparisons of kawaii computing artefacts and computing
artefacts designed to be cute in non-Japanese contexts.

  * •

Sociality: Evaluate the social impacts of kawaii computing platforms.
Sociality refers to social interactions between users and agents and social
reactions to kawaii stimuli. We may also need to explore the impact on society
when it comes to kawaii computing platforms deployed at scale. Future work
should explore how the social plays out so as to understand the social impacts
of kawaii computing.

  * •

Adjacent Concepts: Explore the theorized relevance of other aspects of
Japanese culture connected to kawaii, such as amae and chizimi shikou. Amae
may also relate to sociality. Could kawaii chatbots make us feel loved and
accepted? Chizimi shikou could help explain preferences to small devices and
robots.

##  5\. Conclusion

Kawaii computing is nascent. While not exactly new, the lack of formalization
around what it is leaves this area of study brimming with possibility.
Researchers and designers across HCI, HRI, HAI, and beyond should draw from
the formal theories and models established in psychology. We have the
opportunity now to build on this foundation and direct the future of kawaii
computing in a more rigorous, culturally-sensitive, and theoretically-driven
way.

###### Acknowledgements.

Yijia Wang is grateful to the China Government Scholarship that sponsored this
work as part of her doctoral programme. Katie Seaborn conscientiously dissents
to in-person participation at CHI; read their positionality statement here:
https://bit.ly/chi24statement

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