No Joke: An Embodied Conversational Agent Greeting Older
Adults with Humour or a Smile Unrelated to Initial Acceptance
Ge “Rikaku” Li

Katie Seaborn

Tokyo Institute of Technology
Tokyo, Japanli.g.ai@m.titech.ac.jp

seaborn.k.aa@m.titech.ac.jp
Tokyo Institute of Technology
Tokyo, Japan

Figure 1: Three versions of the ECA, each representing a different affective state based on varying levels of valence and arousal:
laughing (left), smiling (middle), neutral (right).

ABSTRACTKEYWORDS

Embodied conversation agents (ECAs) are increasingly being developed for older adults as assistants or companions. Older adults may
not be familiar with ECAs, influencing uptake and acceptability.
First impressions can correlate strongly with subsequent judgments,
even of computer agents, and could influence acceptance. Using
the circumplex model of affect, we developed three versions ofan ECA—laughing, smiling, and neutral in expression—to evaluate
how positive first impressions affect acceptance. Results from 249
older adults indicated no statistically significant effects except for
general attitudes towards technology and intelligent agents. This
questions the potential of laughter, jokes, puns, and smiles as amethod of initial engagement for older adults.

Embodied conversational agents, Older adults, Acceptability, First
impressions, humor

CCS CONCEPTS• Human-centered computing → User models; Empirical studies
in HCI ; User studies; Natural language interfaces; • Social and professional topics → Seniors; • Computing methodologies →Intelligent agents.

Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.
For all other uses, contact the owner/author(s).
CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA© 2024 Copyright held by the owner/author(s).
ACM ISBN 979-8-4007-0331-7/24/05
https://doi.org/10.1145/3613905.3650918

ACM Reference Format:
Ge “Rikaku” Li and Katie Seaborn. 2024. No Joke: An Embodied Conversational Agent Greeting Older Adults with Humour or a Smile Unrelated to Initial Acceptance. In Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’24), May 11–16, 2024, Honolulu, HI, USA.
ACM, New York, NY, USA, 7 pages. https://doi.org/10.1145/3613905.3650918

1

INTRODUCTION

Embodied conversational agents (ECAs) are interactive computer
systems that mimic real agents—typically people, but also nonhumans—in appearance and/or behaviour [42, 46]. The visual appearance of ECAs compared to other socially intelligent agents(IAs) allows them to communicate with users non-verbally. Nonverbal behaviors, such as nods, posture, and facial expressions,play an important role in engagement [20], trust and empathy [58],and emotional expression [1, 35], among people and with computer agents [7]. ECAs are increasingly being explored for older
adults as emotional support [32], everyday support [49], and for
healthcare [51]. The realism of ECAs can be attractive [58] but
also uncomfortable, if “too” humanlike or uncanny [53]. Also, older
adults experience more barriers to acceptance of new technologies
compared to other age groups [3, 15, 17, 19]. Therefore, ECA design
must be carefully geared towards older adults.
In human-human interaction, first impressions are crucial andcan be lasting [4, 23, 27]—and the same appears to be true when it
comes to computer agents, like ECAs [5, 42]. This may be especially

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USAtrue for older adults, when first impressions are formed based on
an agent’s nonverbal expressions of personality and interpersonal
attitudes [6]. One nonverbal characteristic that could influence
first impressions and help older adults embrace ECAs more readily is humour [8, 26, 39]. Indeed, affective computing researchers
have called for further study on ECA emotional expression andhumour [39]. At present, very little research has been conductedon the impact of humour in ECAs generally or specifically within
the context of first impressions and for older adults.
As a first step, we evaluated whether acceptance of an ECA byolder adults could be influenced through first impressions featuring
humour. We asked: Can humour improve initial acceptanceof an ECA by older adults by influencing first impressions?
To this end, we built an ECA with three distinguishable levels ofaffective expression that introduces itself via video. We randomly
assigned older adults to each version of the ECA. We found nostatistically significant differences, except in relation to generaltechnology and IA acceptance. We report our negative results inaccordance with human-computer interaction (HCI) and general science procedures against publication biases [11, 36]. We contributethese null findings and raise questions about the who, where, andhow of humour for ECA design.

2 THEORETICAL BACKGROUND
2.1 Older Adults, Agent Acceptance, andHumourPopulations around the world are ageing at a rapid pace [38]. Advances in everyday technologies parallel this trend, with older
adults emerging as a key user group. Anderson and Perrin [2] foundthat in 2016, 67% of older adults used the Internet and about 40%
had a smartphone. Still, technology adoption among older adults is
low compared to 90% of the general adult population being “wiredin.” Older adults experience barriers, e.g., complex and inaccessibleuser interfaces (UI) [13], and may also resist new technology, which
is linked to low acceptance [18, 54].
Autonomous IA with multimodal embodiments, like ECAs, may
be an inclusive and engaging option [14, 48, 56]. Still, first impressions are critical, perhaps especially for older adults [22]. By “first
impressions,” we mean the initial feelings and attitudes—positive,
negative, and neutral—elicited in a first encounter [4]. First impressions are formed unconsciously and quickly, usually within a few
seconds. Importantly, if the first use is negative, subsequent use
may be disrupted or perceived as negative [16]. Facial expressions,such as smiling, can lead to positive first impressions [29, 40]. Humour may be especially effective [5, 25, 44, 52]. For instance, Tabak
et al. [52] found that older adults appreciated dynamic elements
such as humour over static characteristics such as agent agedness
and genderedness. Binsted at el. [5] suggested that self-deprecating
humour could be employed by an agent. As such, we designed
our ECA to express humour, including self-deprecating humour,verbally (with voice) and nonverbally (with facial expressions).

2.2

Modeling Technology Acceptance

Davis [12] used the Theory of Rational Action (TRA) to infer that
beliefs about technology can affect intention to adopt technology:

Li and Seaborn

the Technology Acceptance Model (TAM) [12]. Core concepts include perceived ease of use and perceived usefulness. TAM predicts
that people are more likely to adopt a technology when it is perceived to be easy to use and useful, and perceived ease of use can
influence perceived usefulness [12]. For example, Lee at el. [28]explored whether and to what degree older adults would accept
a soft service robot in the home, finding that perceived ease ofuse, usefulness, and subjective norms were statistically significant
predictors of acceptance. Chen at el. [9] later developed the SeniorTechnology Acceptance Model (STAM) using the TAM and othermodels, including the Unified Theory of Acceptance and Use ofTechnology (UTAUT) [55]. The elder-centred STAM includes eight
predictors: geriatric technology self-efficacy, geriatric technology
anxiety, facilitation conditions, self-reported health status, cognitive ability, social relationships, life attitudes and satisfaction, andphysical functioning. Acceptance of geriatric technology is operationalized as positive attitudes towards technology. As such, an
ECA that introduces itself in a positive way may influence older
adults’ first impressions in a similarly positive way, and thus theiracceptance of the ECA.
Combining the potential of a humorous ECA and the STAM,
we hypothesized that: H1. Use of humour expressed by an ECA
through nonverbal facial expressions and verbal content, i.e.,laughing, puns, and jokes, will improve its acceptance by older
adults. We compared this version to a smiling one (a low arousal
complement) and one with a neutral expression. We used measuresfrom STAM research. Since socio-demographic factors, including
level of familiarity with technology, could have an effect [9], we
also compared novice and non-novice groups.

3

METHODS

We conducted a between-subjects online1 experiment, where eachgroup viewed a greeting video by the ECA with one of three expressions: laughing with a pun and joke, smiling with a pun, andneutral with a pun (refer to subsection 3.4 and Figure 1 for details).
Our protocol was registered before data collection2 and approvedby the IRB (#2023064).

3.1

Participants

Japanese older adults (N=249; women n=60 or 24% and men n=189
or 76%, of which n=2 were X-gender3 and n=2 declined to reportdetails) aged 65–80 [38] (M = 69.6, SD = 3.6) were recruited throughYahoo! Crowdsourcing4 between June 12th and June 13th , 2023.
We excluded records less than 6 minutes, the bare minimum time
according to video length and pilot tests. Older adults were pseudorandomly assigned to ECA group by birthday; this and data quality
eliminations led to different totals per group: n=78 in laughing,n=69 in smiling, and n=102 in neutral. 76% (n=190) had never seenor heard about ECAs before, and 96% (n=240) had never used ECAs
before. Participants were paid in accordance with the participant
pool at roughly 1200 yen/hour, equating to 400 yen for 25 minutes.
1We used SurveyMonkey: https://www.surveymonkey.com/
2 Registered on April 2nd , 2023, at OSF: https://osf.io/yhtd6
3 X-gender is similar to non-binary in Japanese culture.

4 Yahoo! JAPAN ensures unique respondents and quality through identity verification:

https://crowdsourcing.yahoo.co.jp/

No Joke

3.2

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

Procedure

Participants answered the general attitudes questionnaire (referto subsection 3.3). Then they watched the ECA self-introduction
video (refer to subsection 3.4) and answered an attention checkquestion (input the random number at the end of the video). They
also answered questions about their attitudes towards the ECA(refer to subsection 3.3). Finally, they provided demographics andreceived a Yahoo! Crowdsourcing code for compensation.

3.3

Measures and Instruments

All responses were rated on a 5-point Likert scale ranging from 1(strongly disagree) to 5 (strongly agree), unless noted.
3.3.1 Technology Acceptance (GAT, GAIA). We used the nine TAM
items from the STAM model-based [10], validated 14-item instrument by Chen at el. [9] to capture general attitudes towards technology (GAT) and IA specifically (GAIA). Content included: attitude
towards use, perceived usefulness, perceived ease of use, technology anxiety, technology self-efficiency, facilitating conditions, selfreported conditions, social relationships, attitudes towards ageing,
and life satisfaction. We excluded the health and capability items,
since the ECA context of use was not medical.
3.3.2 Acceptance of the ECA (AECA). We followed the measurement selection protocol of Kramer at el. [24], using the same measures with 26 items for acceptance based on use of the ECA: relationship with ECA, usability, enjoyment, aesthetics, privacy concerns,
control, and perceived usefulness. Items on direct interaction were
excluded because we used videos for first impressions.
3.3.3 Helpfulness Potential (HP) and Helpfulness Ratio (HR). Behavioural responses are difficult to measure in online experiments,
but procedures may be adapted from previous research. Porath andErez [43] considered how rudeness affects task performance anddesire to help, which they operationalized as the average numberof pencils that each group helped the researchers pick up. Kühnlenz at el. [25], at the end of the experiment, gave participants theoption to go directly to the final stage or to help the robot withan object labeling task. Agreeing to this additional, arduous task
was used to measure a basic willingness to help the robot, and thenumber of labeled pictures produced was used as an indicator ofthe degree of help. In Wood et al. [57], word count was used tomeasure children’s willingness to talk to a robot by comparing thetotal number of words children used in their interactions with therobot versus in the interactions with a human interviewer, helping
the researcher to understand the differences in their performancein these two contexts. This method provided a quantitative way tocompare children’s interactions in different interviewer contexts
by translating verbalizations into numerical data.
Based on the above, we created the Helpfulness Potential (HP)and Helpfulness Ratio (HR) measures to test willingness to help
the ECA. Given the “conversational” context, we asked participants
to imagine a conversation with the ECA. We captured (i) the percentage of those willing to help train the ECA, or HP (similar topicking up a pen [43] or an object labeling task [25]) and (ii) how
many words participants were willing to contribute, or HR (likedialog word counts between children and a robot [57] and how
many labels were produced to help a robot [25]).

The task proceeded as follows. First, participants were given theoption to help or not help train the ECA (HR):Thank you for taking the time to let us know what you
think of our agents. We appreciate your help in training
our agents to speak better. (エージェントの感想をお聞かせいただき、ありがとうございます。
エージェントをもっとうまく話せるように訓練するために、ご協力をお願いいたします。)
A. Yes, I’ll help (はい、お手伝いします)
B. I’ll just finish the last questions (結構です、最後の質問を終わらせます)Then they were asked to write a conversational script between
themselves and the ECA (HP) using this template:
Please use the following template: (テンプレートに関しては、以下のものをご使用ください)Me (私)：
Mikan (みかん)：
Me (私)：
Mikan (みかん)：(...)
3.3.4 Demographics. Demographics included age, gender, healthcondition, familiarity with ECAs (seen or heard before, or not), and,if applicable, use frequency of ECAs and which kinds used.

3.4

Materials

3.4.1 Design of the ECA. We created three versions of the ECA using Unreal Engine 5, Live Link Face 1.2.1, and Metahuman Creator5
by Epic Games. Based on Kulms at el. [26] and our Japanese population, we chose a feminine Asian avatar in formal dress. Animating
the agent in line with the voice and script involved capturing a
Japanese lab member’s facial expressions and lip movements on amobile device (iPhone 11) using Live Link Face and transferring
these to Unreal Engine to sync with Metahuman Creator. We used
the mappings of Lim and Aylett [31], based on the circumplex modelof affect [45], to identify three distinct facial expressions and how
to animate these using the ECA’s facial features: laughing for thehumour condition (high arousal, positive valence), smiling as a
non-humorous complement (low arousal, positive valence), andneutral as a control (low arousal, neutral valence). The lab member
first studied Figure 9 from Lim and Aylett [31], focusing on theeyebrows, eyes, and mouth of the faces. Then, they imitated each
expressions while reading the associated script aloud.
The script was a self-introduction. For all conditions, we set
a baseline positive tone using a pun related to the ECA’s name(“Although I‘m not an edible ‘Mikan,’ I absolutely love oranges!”).
For the “laughing” condition, we used a joke related to technology6
that a Japanese lab member translated and back-translated (“Doyou know why the computer is feeling cold? Ha-ha! It is because
Windows is left open!”). The ECA introduced its name, functions,and interests (refer to Appendix A in Supplementary Materials). All
videos are on OSF 7 , with links to each video listed in Appendix B(Supplementary Materials).
5 https://metahuman.unrealengine.com/

6 https://911cybersecurity.com/tech-jokes-a-collection-of-computer-network-

infrastructure-and-cybersecurity-humor

7 https://osf.io/9a7wf/files/osfstorage

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

3.4.2 Pilot Tests and Manipulation Checks. We conducted threepilot tests to ensure (i) the design of the ECA, i.e., manipulation
checks, (ii) no deficiencies in the survey, e.g., bugs, typos, Japanese
language checks, and (iii) no instances of bias. Eight Japanese lab
members (1 woman, 7 men) participated in all tests. The first focusedon the three emotional expressions: whether these matched the
expected arousal and valence levels and were distinguishable. Each
person voted on the level of valence and arousal for each ECA.
The sums matched expectations; however, all thought that thelaughing ECA was a bit unnatural. As such, a hand was added, inline with social norms for women laughing in Japanese culture.
The second pilot test focused on the content and logic of the threequestionnaires (one for each version of the ECA). We modified thebase questionnaire by adding explanations about the ECA at thestart and editorial fixes. The third pilot test focused on the new
ECA design for the laughing condition. The original version andtwo new versions with a hand over mouth or under chin were
evaluated based on arousal and valence. Results indicated that the“hand under chin” version was highest in valence, so we used thisvideo for the laughing condition in the study.

3.5

Data Analysis

Descriptive statistics were generated for all measures (GAT, GAIA
and AECA) by ECA version and self-reported familiarity with ECAs:
novice (no knowledge) and non-novice (some knowledge or more).
Shapiro-Wilk tests were used to determine normality. Most wereatypical, so non-parametric tests, e.g., Kruskal-Wallis, were used
for analyzing the differences between groups. GoTranscript8 wasused for calculating the number of Japanese words for HP.

4

RESULTS

We present our results below, with key results provided in Figure 2.

4.1

General Acceptance of Technology (GAT)and Intelligent Agents (GAIA)

Descriptive statistics for the GAT were: laughing group (M = 3.3,
SD = 0.6), smiling group (M = 3.4, SD = 0.5), and neutral group(M = 3.4, SD = 0.6). For the GAIA: laughing group (M = 3.3, SD =0.6), smiling group (M = 3.3, SD = 0.6), and neutral group (M = 3.3,
SD = 0.5). Kruskal–Wallis tests found no statistically significant
difference by ECA group for GAT (p = .842) and GAIA (p = .890).
A Mann-Whitney U test found a statistically significant differencein GAT between novices (M = 3.3, SD = 0.6) and non-novices (M =3.6, M = 0.4), U = 2400.5, Z = -2.61, p < .001, but not for GAIA, p =.096. This indicates similar levels of technology and IA acceptancefor all older adults, but flags a potential difference between novices
and those more familiar with general technology.

4.2

Acceptance of the ECA (AECA)

A Kruskal-Wallis test found no statistically significant difference
between the laughing (M = 2.9, SD = 0.6), smiling (M = 2.8, SD =0.6), and neutral (M = 2.8, SD = 0.6) groups, p = .420 (Figure Subfigure 2(d)). This suggests that first impressions, regardless of emotional expression, did not have an impact on acceptance of the ECA.
8 https://gotranscript.com/

Li and Seaborn

A Mann-Whitney U test also did not find a statistically significant
difference for AECA between novices (M=2.8, SD=0.6) and nonnovices (M=3, SD=0.5), p = .196, meaning older adults accepted
the ECA regardless of general familiarity with ECAs. However, aSpearman’s rank correlation test for GAT and AECA was statistically significant, r(247) = .422, p < .001, as was one for GAIA and
AECA, r(247) = .454, p < .001. Older adults who were more open to
accepting technology or IA in general were also more accepting of
our ECA. In sum, general acceptance over first impressions.

4.3

Helpfulness Potential (HP) and Helpfulness
Ratio (HR)The portion of participants who chose to help train the ECA (HP)
were 71% (n=55) in the laughing group, 72% (n=50) in the smilinggroup, and 72% (n=73) in the neutral group (Figure Subfigure 2(a)).
A Chi-square test indicated no statistically significant differences,p = .966, meaning that willingness to help did not appear to beaffected by ECA version. A Chi-square test indicated that the same
was true for novices (helped n=152, declined n=6) compared to nonnovices (helped n=66, declined n=5), p = .102 (Figure Subfigure 2(b)).
However, Mann-Whitney U tests found statistically significant relationships between GTA and HP, U = 7816.5, Z = 3.42, p < .001, and
GAIA and HP, U = 8034.5, Z = 3.85, p < .001.
For those who chose to help (n = 178, 71%), a Kruskall-Wallis test
found no statistically difference for HR between the laughing group(M = 49.5, SD = 30.7), smiling group (M = 51.0, SD = 28.0), or neutral
group (M = 46.1, SD = 34.3), p = .396 (Figure Subfigure 2(c)). Nor
did a Mann-Whitney U test find a statistically significant difference
between novices (M = 48.1, SD = 31.7) and non-novices (M = 50.7,
SD = 30.4), p = .685. However, a Spearman’s rank correlation testfor GAT and HR was statistically significant, r(180) = .314, p < .001,
as was one for GAIA and HR, r(180) = .376, p < .001).
In short, the differing emotional expressions of the ECA had noreal impact on willingness to help or degree of aid, and neither did
familiarity. Instead, general technology and IA acceptance seems
to have had a greater impact.

4.4

Results Summary and H1

Altogether, the results fail to support H1. The use of laughter andhumour as well as smiling had no effect on initial acceptance of
the ECA. Instead, general technology acceptance seemed to explain
patterns in acceptance of the ECA.

5DISCUSSION

The aim of this research was to investigate the influence of humour, laughter, and positive expressions in an ECA as a facet offirst impressions that may influence older adult’s initial acceptanceof the ECA. We employed a rigorous theoretical and design process,including multiple pilot tests and manipulation checks. Yet, theexpected result was not found: humour and laughter, as well as
general positivity expressed through verbal puns and nonverbal
smiles, had no apparent effect. This included attitudes as well as behavioural measures. We could, however, explain this result throughgeneral attitudes towards technology and IA. A notable exception
was the hint of a difference between novices and non-novices, but

No Joke

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

Helpfulness Potential (HP) Ratios by ECA Version

Helpfulness Potential (HP) by ECA Version
73

80

Declined

55

0.75

50

40

29
23

19

0.28

0.28

0.71

0.72

0.72

Laughing

Smiling

Neutral

0.50

0.00

0
Laughing

Smiling

NeutralECA Version

ECA Version(a) Helpfulness potential (HP) results.

(b) Ratio for HP results.

Helpfulness Ratio (HR) by ECA Version
49.5

Acceptance of the ECA (AECA) by ECA Version
5.0

51.0
46.1

4.0
40.0
AECA

Word Count Average

Helped

0.29

0.25

20

60.0

Declined

1.00

Ratio

Counts60

Helped

20.0

2.9

2.8

2.8

Laughing

Smiling

Neutral

3.0
2.0

0.0

1.0
Laughing

Smiling

NeutralECA Version(c) Helpfulness ratio (HR) results.

ECA Version(d) Acceptance of the ECA (AECA) results.

Figure 2: Key results for behavioural and attitudinal measures by ECA version.
this did not bear out. In short, humour could not improve acceptance in the context of first impressions with the ECA, nor could
ECA positivity, at least in the case of our “Mikan.”
Prior research has emphasized the significance of positive emotions and dynamic features in ECAs for older adults [39, 44]. For
instance, Ring at el. [44] suggested that humour might have a more
substantial impact on user acceptance than what we found. Our
results may be attributed to differences in ECA design [47], thecultural backgrounds of participants [2], variations in researchmethods across studies [47], and how older adults approach humour, i.e., individual and personality factors [30, 34]. Even age may
play a role: the type of humour that older adults find most enjoyable may differ from other forms [33]. Differences could also relate
to the specific type of humour selected or the specific way it wasincorporated in the ECA, e.g., the choice of joke and particular facial expressions. Humour preferences may vary among individuals:
what may be humorous to one person may not be to another.
Future work can explore these possibilities. For example, ECAs
can be designed to adapt their humour styles based on users’ personality traits to create more personalized and engaging interactions.
Additionally, ECAs can be programmed to recognize and respondto the emotional state of a user, allowing for the strategic use of
humour to elevate the mood of a user and enhance their overall experience with the technology. Personality traits and mental healthcan also be used as variables in ECA acceptance studies and canprovide valuable insights for developing targeted interventions. For

example, if humour was found to have a more significant impact on
individuals with specific personality traits or mental health, theninterventions could be designed to use humour to increase ECA
acceptance among these groups.
How the ECA was framed as a general personal assistant witha wide array of ever-customizable interests could also have influenced responses in general and especially towards its expressionsof humour and positivity. Mikan introduced “herself” and “her”
functionalities in a certain way: able to read, converse, buy things,
and even play baseball—plus whatever else the user was interested
in. This is a rather broad framing of mostly transactional abilities
that may not lend itself to humour. Future work should explore
what ECA “types” may be best and least suited to humour.
Humour can also be influenced by cultural factors, as Easterners
and Westerners often have different attitudes towards humour [21].
As such, perceptions of humour and its impact on the acceptance of
ECA may be influenced by cultural background. We should therefore design humour in accordance with different cultures. Therefore, the incorporation of humour in ECAs designed for Japaneseusers should be culturally sensitive and consistent with current
cultural norms and preferences. humour that resonates positively
with Japanese participants can increase their engagement with the
ECA and promote more enjoyable and natural interactions.
While little work exists, some suggests that older adults may facechallenges when it comes to accepting humour expressed in technology [37]. O’Connell et al. [41] indicated that lack of exposure to

CHI EA ’24, May 11–16, 2024, Honolulu, HI, USA

technology creates an additional psychological barrier to adoptionof new technology. In our case, most participants had little or no
prior experience with ECAs, making it their first encounter withthis advanced technology. Consequently, older adults unacquainted
with the intricacies of ECAs may display hesitation, even whenmore “humanlike” factors, such as humour, are integrated into thedesign. An approach tailored to the specific desires and barriers
faced by older adults may be crucial for enhancing their receptiveness to ECAs. Perhaps first impressions are not enough. Future
work may explore longer-term engagements or run follow-up studies wherein older adults directly interact with the ECA to further
investigate the relationship between humour and acceptance.

Li and Seaborn

using a range of other factors, such as usability, functionality, familiarity with the technology, and personal preferences, perhaps
by asking older adults for their favourite jokes.

ACKNOWLEDGMENTS
This work was supported by departmental funds. We thank themembers of the Aspirational Computing Lab for pilot testing andsupport, especially Suzuka Yoshida. We thank Xiuzhu Gu and Hiroyuki Umemuro for early feedback. Katie Seaborn conscientiously
dissents to in-person participation at CHI ’24 this year; read theirpositionality statement here: https://bit.ly/chi24statement

REFERENCES
5.1

Limitations

We acknowledge several limitations in this work, particularly withthe online setup. Online experiments cannot guarantee high quality
due to various uncontrollable factors (e.g., environment setup andsudden distractions). While we used an attention check, we cannot
confirm that participants were fully attentive (e.g., savvy respondents could have skip-searched the video for the code, although
we tried to account for this shortcut by removing respondent data
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checks). This may have introduced bias into the results, as participant gender may relate to ECA acceptance [9], and we used afeminine ECA. While we avoided stereotype threats [50] by asking
about demographics at the end of the study, future work should
aim for accurate gender representation.
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may have been more open to helping the ECA after an interactive
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have also been given the task indirectly, i.e., asked to converse
through text input with the ECA. This would require some automation in the online study environment. Future work may explore
these theoretical and technical possibilities. The development andvalidation of standard objective, behavioural measures would be aboon for continued online experiments and meta-analysis work.

6

CONCLUSION

Positive first impressions and humour did not impact older adults’
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