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Corporate characters through research ／ Make action easier

Can corporate characters make the next step easier?

The conclusion first

Characters may ease anxiety or uncertainty and support a small next step. Evidence that a static corporate character alone increases inquiries or purchases is weak.

What is known
Relevant studies include response behavior with human-like chatbot language, choosing robots for embarrassing consultations, and interactive guidance that answers questions.
Conditions
Identify what stops people: fear, uncertainty, effort or another reason
What remains unknown
It remains unclear whether simply placing a static corporate character increases inquiries or purchases.

What research tells us relatively clearly

Relevant studies include response behavior with human-like chatbot language, choosing robots for embarrassing consultations, and interactive guidance that answers questions.

This includes related-field research on health behavior. It does not establish isolated corporate-character effects or medical benefits provided by YataLogic.

Which conditions seem important?

  • Identify what stops people: fear, uncertainty, effort or another reason
  • Specify the next action
  • Simplify the procedure itself
  • Connect users with a person when they need help

Representative studies

What was compared, and what changed? Read the findings together with the participants and conditions. YataLogic’s practical interpretations are distinguished from the study results.

Positive difference under these conditionsResearch in a related field

A sense of someone being there may help prompt a small response

In a chatbot experiment, human-like language made a response to a small request more likely. A sense of someone being present explained part of that relationship.

What did the study compare?
A chatbot using human-like language versus a reference condition
What was measured?
Whether users responded to a small request
Participants or setting
Analysis sample n=153
What this does not establish

This is research on conversational chatbots, not an effect of static images or a rate of increase in inquiries.

YataLogic’s interpretation: Consider applications that put anxieties into words and show the first available choices.

Read the study details
Study design
Experiment
Comparison and conditions
A chatbot using human-like language versus the reference condition
Measures
Response behavior to a small request
Main findings
Response odds were higher with human-like language, with social presence explaining the pathway.
Key figures (scales differ between studies)
OR=3.975; analysis n=153
What can be said
A sense of someone being present may encourage a small action in some situations.
Important limitations
OR=3.975 does not mean that the response rate or conversion rate was 3.975 times higher. This was not a static mascot.
Go to notes on statistics and research methods ↓

Adam, M., Wessel, M., & Benlian, A.（2021）

AI-based chatbots in customer service and their effects on user compliance

Electronic Markets

View the original paper or source
Varies by condition and measureResearch in a related field

When people worry about being judged

In highly embarrassing service situations, robot service points were chosen more often than human ones. In low-embarrassment situations, humans were chosen more often, so results depended on the situation.

What did the study compare?
Robot versus human service points in high- and low-embarrassment situations
What was measured?
Choice of service point and concern about others’ judgments
Participants or setting
Preliminary study n=101, among others
What this does not establish

Robots are not always preferred. The study also did not test whether animal-like characters have an advantage.

YataLogic’s interpretation: For sensitive consultations, consider an interface whose role is to reduce fear of being judged by another person.

Read the study details
Study design
Multiple studies
Comparison and conditions
Robots versus humans in services involving high or low embarrassment
Measures
Choice and evaluation anxiety
Main findings
Robots were chosen more often in highly embarrassing situations, while humans were chosen more often in low-embarrassment situations.
Key figures (scales differ between studies)
High: 64.7% vs 35.3% / low: 44% vs 56%
What can be said
A non-human service point may reduce evaluation anxiety in some situations.
Important limitations
Robots are not always preferred. An advantage of animal-like characters was not directly tested.
Go to notes on statistics and research methods ↓

Holthöwer, J., & van Doorn, J.（2023）

Robots do not judge: Service robots can alleviate embarrassment in service encounters

Journal of the Academy of Marketing Science

View the original paper or source
Positive difference under these conditionsResearch in a related field

Guidance that answers questions and makes the next step clear

In a large comparative vaccination trial, interactive support—including questions and guidance tailored to the situation—increased actual vaccination rates. A difference was also found compared with one-way notifications alone.

What did the study compare?
Interactive chat, one-way notifications and no guidance
What was measured?
Whether participants actually received a vaccination
Participants or setting
n=249,705
What this does not establish

This is related-field research on health behavior. It concerns the whole support intervention, including questions and guidance, not a character image’s effect.

YataLogic’s interpretation: Alongside encouragement, provide the information needed to act at that moment.

Read the study details
Study design
Preregistered three-arm randomized controlled trial
Comparison and conditions
Interactive chatbot versus one-way reminders versus no intervention
Measures
Actual vaccination
Main findings
The interactive intervention increased actual vaccination behavior.
Key figures (scales differ between studies)
+1.6pp versus control; +1.0pp versus one-way messages; n=249,705
What can be said
Interactive support involving questions, situation-specific guidance and clarification of the next step can affect actual behavior.
Important limitations
This is the effect of the entire intervention package, not of a character image.
Go to notes on statistics and research methods ↓

Brown, D., Barrera, A., Ibañez, L., Budassi, I., Murphy, B., Shrestha, P., Salomon-Ballada, S., Kriscovich, J., & Torrente, F.（2024）

A behaviourally informed chatbot increases vaccination rates in Argentina more than a one-way reminder

Nature Human Behaviour

View the original paper or source

Studies without positive results and other cautions

Some analyses did not find a clear direct effect of human-likeness itself. With angry customers, human-likeness may raise expectations and worsen evaluations if the problem is not resolved.

No clear difference foundResearch in a related field

No clear direct effect of human-likeness alone

An analysis of chatbot research in services did not find a clear direct effect of human-likeness itself. Trust and expected usefulness were central to adoption decisions.

What did the study compare?
Relationships between chatbot adoption and human-likeness, trust, expected usefulness and other factors
What was measured?
Factors associated with chatbot use and adoption
Participants or setting
Chatbot studies in service industries
What this does not establish

This concerns the direct effect in this analysis. It does not mean human-likeness is irrelevant in every situation.

YataLogic’s interpretation: Before deciding on appearance, define the specific support the user will receive.

Read the study details
Study design
Meta-analysis and meta-SEM
Comparison and conditions
Meta-SEM of AI chatbot adoption research
Measures
Use and adoption factors
Main findings
Trust and performance expectancy were central; no significant direct effect of anthropomorphism was found.
Key figures (scales differ between studies)
Meta-analysis
What can be said
Usefulness and trustworthiness matter more than human-likeness.
Important limitations
This does not mean anthropomorphism is always ineffective.
Go to notes on statistics and research methods ↓

Rohit, K., & Vishwakarma, P.（2026）

Meta-analytic insights into AI chatbots adoption in the service industry: uncovering dynamics and contextual insights using a meta-SEM approach

Marketing Intelligence & Planning

View the original paper or source
Adverse effect under specific conditionsResearch in a related field

Failing to meet expectations may lower evaluations

In interactions with angry customers, human-like chatbots raised expectations of problem-solving ability. When those expectations were not met, satisfaction, company evaluations and purchase intentions could decline.

What did the study compare?
Interactions between angry customers and chatbots with human-like expression
What was measured?
Satisfaction, company evaluations and purchase intentions
Participants or setting
Real-world data from approximately 462,000 sessions plus 4 experiments
What this does not establish

Customer anger and inability to resolve the problem are important conditions. Friendly expression is not always counterproductive.

YataLogic’s interpretation: For complaints and issues involving money or safety, make it possible to reach a human quickly.

Read the study details
Study design
Real-world data and multiple experiments
Comparison and conditions
Angry customers × anthropomorphic chatbots
Measures
Satisfaction, company evaluations and purchase intentions
Main findings
Expectations of problem-solving ability rose; when the chatbot could not respond adequately, satisfaction, company evaluations and purchase intentions declined.
Key figures (scales differ between studies)
Real-world data from approximately 462,000 sessions plus 4 experiments
What can be said
Anthropomorphism also raises expectations and can backfire in situations involving anger or serious problems.
Important limitations
Friendliness does not always increase satisfaction or trust.
Go to notes on statistics and research methods ↓

Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T.（2022）

Blame the Bot: Anthropomorphism and Anger in Customer–Chatbot Interactions

Journal of Marketing

View the original paper or source

Not finding a difference does not mean there is no effect in every situation. Adverse effects should also be read within the conditions studied.

How YataLogic interprets this

These are design ideas drawn from several studies for practical use. They do not guarantee the same results for your business.

What the study found

In a chatbot experiment, human-like language made a response to a small request more likely. A sense of someone being present explained part of that relationship.

YataLogic’s practical application

Consider applications that put anxieties into words and show the first available choices.

Check the original study and conditions ↑
What the study found

In highly embarrassing service situations, robot service points were chosen more often than human ones. In low-embarrassment situations, humans were chosen more often, so results depended on the situation.

YataLogic’s practical application

For sensitive consultations, consider an interface whose role is to reduce fear of being judged by another person.

Check the original study and conditions ↑

What should you look at in practice?

Assess the following responses separately, according to the job assigned.

  • Did users begin answering?
  • Did they proceed to the next option or inquiry form?
  • Did they complete the form?
  • Could they reach a person when needed?

Research notes

The main text can be understood without reading the statistics. Use these notes to check what the figures mean.

Understand the statistics and research methods

Figures supplement the findings by showing measured differences and uncertainty. Different measures cannot be converted into one common improvement percentage.

OR (odds ratio)
The ratio of the odds of an event between conditions. OR=3.975 does not mean that the response rate is 3.975 times higher. Say that the odds were higher without converting the result into a rate.
Percentage points (pp)
The difference obtained by directly subtracting one percentage from another. This differs from a relative percentage increase. 10% → 11.6% is an increase of 1.6 percentage points.
RCT
A comparative trial that randomly assigns participants to groups. Generally strong for causal inference, but the effect concerns the whole intervention. When other functions are added alongside a character, do not call it an isolated character effect.
Meta-analysis
A method for statistically combining results from multiple studies. When studies differ substantially, the average alone is insufficient. Also check I² and differences in populations and interventions.
NS / no significant difference
A statistically clear difference was not found. This does not necessarily prove that the effect is zero. State that no significant difference was found under these conditions.

This synthesis is YataLogic’s practical reading of multiple studies. It is not a scientifically established proprietary model.

References

This page covers 5 findings from 5 papers or sources. Different findings from the same paper are presented separately.

  1. Adam, M., Wessel, M., & Benlian, A.（2021）

    AI-based chatbots in customer service and their effects on user compliance

    Electronic Markets

    View the original paper or source

    Finding discussed: A sense of someone being there may help prompt a small response

  2. Holthöwer, J., & van Doorn, J.（2023）

    Robots do not judge: Service robots can alleviate embarrassment in service encounters

    Journal of the Academy of Marketing Science

    View the original paper or source

    Finding discussed: When people worry about being judged

  3. Brown, D., Barrera, A., Ibañez, L., Budassi, I., Murphy, B., Shrestha, P., Salomon-Ballada, S., Kriscovich, J., & Torrente, F.（2024）

    A behaviourally informed chatbot increases vaccination rates in Argentina more than a one-way reminder

    Nature Human Behaviour

    View the original paper or source

    Finding discussed: Guidance that answers questions and makes the next step clear

  4. Rohit, K., & Vishwakarma, P.（2026）

    Meta-analytic insights into AI chatbots adoption in the service industry: uncovering dynamics and contextual insights using a meta-SEM approach

    Marketing Intelligence & Planning

    View the original paper or source

    Finding discussed: No clear direct effect of human-likeness alone

  5. Crolic, C., Thomaz, F., Hadi, R., & Stephen, A. T.（2022）

    Blame the Bot: Anthropomorphism and Anger in Customer–Chatbot Interactions

    Journal of Marketing

    View the original paper or source

    Finding discussed: Failing to meet expectations may lower evaluations

What job should a character do?

Use six roles to organize the job, the touchpoint and the measure.

See how this role can be used →

How do companies actually use characters?

Explore business applications by role.

Explore Make action easier cases →
See the research overview of all six roles →

Explore the research and design in more depth

The book Sono Kigyo Character, Imi Aru? examines the research and character design systematically. The book is available in Japanese only.

View the book on Amazon (Japanese only) ↗

What about your business?

Identify the job you want a character to do from your current challenge.

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English summaries and any translated source titles are provided by YataLogic. Linked papers and source documents have not been translated and may be in Japanese or another language.