Negative Expressions by Social Robots and Their Effects on Persuasive Behaviors

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript investigates how an android robot’s negative (anger-like) persuasion can promote social-norm compliance, examining the joint effects of robot appearance, persuasive tone, contextual severity, and user traits. The topic is timely and fills a discernible gap in HRI literature, where positive persuasion dominates prior work. Methodologically, the authors implement a four-factor design, provide open materials, and report complete statistics. Nevertheless, several conceptual, methodological, and presentation issues must be resolved before the paper can be considered for publication.

The introduction clearly motivates the need to explore negative persuasion, but it should also draw from broader social- and moral-psychological evidence on justified negative emotions, and their differential persuasive force.

It would be good to articulate a principled design rationale—e.g. uncanny-valley predictions, facial degrees-of-freedom, skin material, or child- versus adult-like morphology—and discuss how these variables might moderate emotional displays, although the choice of ERICA and CommU is just briefly justified because they are widely used in practice.

The research hypotheses are well formulated but their labels are reused in multiple places. Please ensure unique, sequential numbering throughout.

The study is positioned as guidance for future service robots that will interact mostly with younger digital-native cohorts. It would be great to clarify how generational differences between the current generation or society and future generation.

Maybe "teleoperation versus autonomy" is not included in the scope of the paper, however, perceived agency strongly shapes moral evaluations. It would be better to discuss this in the discussion part.

The discussion acknowledges ethical risks. A deeper treatment of responsibility allocation (robot vs operator vs service provider), stress induction in vulnerable users, and alignment with IEEE-Ethics standards would strengthen the contribution.

In Fig. 1, facial expression is inexplicably left un-highlighted, whereas other modules are marked in red. Please explain why facial expression is treated differently.

Fig. 3 sub-panels are not grid-aligned; unify aspect ratios and axis labels.

Several result graphs use dissimilar color schemes; adopt a consistent palette for clarity.

It would be very interesting to cite expressive-robots from cinema and game-AI literature, where negative emotional feedback often shapes player behavior. These domains provide design inspiration and empirical hints that support your thesis.

The study presents an innovative exploration of negative-emotion-based robotic persuasion and offers a useful situation-tone design matrix. However, to reach the journal standard, the authors must (i) fortify the theoretical/psychological grounding, (ii) justify robot-design choices more rigorously, (iii) address autonomy, ethics, and behavioral validity, and (iv) improve figure consistency and hypothesis structure. These revisions collectively fall under major rather than minor corrections.

Author Response

Dear Reviewer,

We sincerely thank you for your invaluable comments and suggests towards improving the quality of our article titled " Negative Expressions by Social Robots and their Effects on Persuasive Behaviors" submitted for consideration for publication in the special issue “Advancements in Robotics: Perception, Manipulation, and Interaction” of Electronics Journal.

We have revised the article based on the comments and suggestions.

Thank you for your support

 

General Comment

The manuscript investigates how an android robot’s negative (anger-like) persuasion can promote social-norm compliance, examining the joint effects of robot appearance, persuasive tone, contextual severity, and user traits. The topic is timely and fills a discernible gap in HRI literature, where positive persuasion dominates prior work. Methodologically, the authors implement a four-factor design, provide open materials, and report complete statistics. Nevertheless, several conceptual, methodological, and presentation issues must be resolved before the paper can be considered for publication.

The study presents an innovative exploration of negative-emotion-based robotic persuasion and offers a useful situation-tone design matrix. However, to reach the journal standard, the authors must (i) fortify the theoretical/psychological grounding, (ii) justify robot-design choices more rigorously, (iii) address autonomy, ethics, and behavioral validity, and (iv) improve figure consistency and hypothesis structure. These revisions collectively fall under major rather than minor corrections.

 

Comment: The introduction clearly motivates the need to explore negative persuasion, but it should also draw from broader social- and moral-psychological evidence on justified negative emotions, and their differential persuasive force.

Response:  The following has been added “In human-human interaction (HHI), emotions such as moral anger, guilt, and disgust have been shown to play a critical role in motivating compliance and moral action [10]. Specifically, moral emotions are not only reactive but serve important social-regulatory functions—they signal norm violations and legitimize calls for behavioral change [11]. Additionally, studies on emotion-based persuasion indicate that negative emotions can outperform positive ones in certain contexts, particularly when the emotion is perceived as justified and aligned with social or moral norms [12]. For instance, anger perceived as morally grounded can increase the credibility and urgency of a message, thereby enhancing its persuasive impact [13]. Similarly, guilt appeals have been widely used in compliance and prosocial behavior literature to successfully induce action [14].”

Comment: It would be good to articulate a principled design rationale—e.g. uncanny-valley predictions, facial degrees-of-freedom, skin material, or child- versus adult-like morphology—and discuss how these variables might moderate emotional displays, although the choice of ERICA and CommU is just briefly justified because they are widely used in practice.

Response: The following has been added “Two robotic platforms were employed in the study: ERICA, a highly anthropomorphic female adult-like humanoid with facial degrees of freedom (DoF) and synthetic skin, and CommU, a compact child-like robot with limited DoF and no facial expression capability. We based our examination within HRI by focusing on the following theoretical rationales: the uncanny valley, morphological priming, and expressive bandwidth. Regarding uncanny valley and morphological priming, ERICA, with its realistic facial appearance and expressive DoF, may evoke the “uncanny valley” effect—eliciting more compliance through its behaviors in contrast to CommU in some contexts of persuasion [25]. Additionally, ERICA may engender the perception of authority, while CommU’s child-like form might prompt perceptions of innocence and a non-threatening presence [26]. Concerning the facial DoF and expressive bandwidth of the robots, ERICA’s rich facial articulation enables nuanced emotional communication—micro expressions, eyebrow raises, lip movements—whereas CommU is restricted to body posture and vocal tone. This increased expressivity may enhance the perception of ERICA’s persuasive behaviors, owing to better emotional clarity [27].”

Comment: The research hypotheses are well formulated, but their labels are reused in multiple places. Please ensure unique, sequential numbering throughout.

Response: The hypotheses have been sequentially numbered.

Comment: The study is positioned as guidance for future service robots that will interact mostly with younger digital-native cohorts. It would be great to clarify how generational differences between the current generation or society and future generation.

Response: The following was included in the limitation section “Moreover, the participants recruited for this study represented a broad age range, which raises concerns regarding the generalizability of the findings. Previous research has emphasized the significance of adapting social robot design and interaction strategies to specific generational cohorts. In particular, the research by Osakwe et al. [73] highlighted that for Generation Z (Gen Z) consumers, factors such as subjective norms, positive emotional responses, and performance expectancy are key determinants of service robot acceptance. Similarly, a systematic review in hospitality robotics emphasizes Gen Z’s enthusiastic adoption of RAISA (robots, AI, service automation) technologies, highlighting their preference for interactive, emotionally engaging service encounters [74]. Together, these studies underscore that future-design paradigms for social robots aimed at younger cohort audiences must prioritize emotional resonance, social normative cues, and performance reliability, as their generational traits shape interaction preferences in ways that older cohorts may not share. Considering this, in our subsequent studies we would strongly focus on the Gen Z.”

Comment: Maybe "teleoperation versus autonomy" is not included in the scope of the paper, however, perceived agency strongly shapes moral evaluations. It would be better to discuss this in the discussion part.

Response: The following was added in the discussion “The robots considered in this work operated autonomously. However, some studies demonstrate that increased robot autonomy can reduce users' sense of agency, influencing how responsibility and moral judgments are attributed to robot [73]. Similarly, [73] found that autonomy moderates the relationship between competence and cognitive trust, suggesting that higher autonomy prompts more stringent moral appraisals when robots fail. Together, these findings underscore that whether a robot is perceived as autonomous or teleoperated can critically influence users' moral evaluations—affecting blame, trust, and perceived responsibility. We thus recommend future work explore how varying operational modes (teleoperation vs full autonomy) interact with robot design features to shape moral outcomes during social compliance elicitation.”

Comment: The discussion acknowledges ethical risks. A deeper treatment of responsibility allocation (robot vs operator vs service provider), stress induction in vulnerable users, and alignment with IEEE-Ethics standards would strengthen the contribution.

Response: We have chosen not to include discussion in this direction as they are beyond the scope of our work.

Comment: In Fig. 1, facial expression is inexplicably left un-highlighted, whereas other modules are marked in red. Please explain why facial expression is treated differently.

Response: Fig.1 has been replaced with the correct version.

Comment: Fig. 3 sub-panels are not grid-aligned; unify aspect ratios and axis labels.

Response: Fig.3 has been replaced with the grid aligned, aspect ratio and axis label unified.

Comment: Several result graphs use dissimilar color schemes; adopt a consistent palette for clarity.

Response: The graphs have been presented with a more consistent color scheme.

Comment: It would be very interesting to cite expressive-robots from cinema and game-AI literature, where negative emotional feedback often shapes player behavior. These domains provide design inspiration and empirical hints that support your thesis.

Response: The following was added “In the realm of game AI, Mahadevan et al. [25] demonstrated that expressive robot behaviors generated through large language models can significantly enhance user engagement by simulating human-like reactions, including emotional feedback to player actions. Notably, their findings suggest that negative emotional cues—such as frowns, sighs, or verbal disappointment—often lead players to adjust their behavior, implying that affective responses from robots can function as implicit social regulators.”

 

 

 

 

 

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

This study explores context-aware persuasive behaviors for social robots through three experiments with humanoids ERICA and CommU. Results show negative behaviors are effective after repeated violations, especially in high-impact scenarios (e.g., COVID-19 rules). Preferences vary by user traits (Compliance Awareness, Agreeableness) and robot appearance, providing design insights for socially intelligent agents. Human-subject validation confirms the approach’s effectiveness. The main suggestions are as follows:

1. The overall structure of the paper is clear and logically coherent, but some paragraphs are slightly verbose. For example, the Introduction could more concisely summarize the research background and motivation.
2. The labels in Figures 3 and 7 should be more detailed to help readers quickly understand the data.
3. The sample sizes in Study 2 (N=98) and Study 3 (N=100) are relatively small, and all participants were from the U.S., which may limit the generalizability of the findings. This should be explicitly discussed in the paper.
4. In Section 4, the discussion on user traits (CA and AG) is thorough, but it could further incorporate existing literature to explain why high-CA and low-AG users prefer negative behaviors, thereby strengthening the theoretical foundation.
5. The Conclusion summarizes the main content of the paper but does not sufficiently discuss the study’s limitations (e.g., the simple sample background and limited experimental scenarios). This should be supplemented.

Author Response

Dear Reviewer,

We sincerely thank you for your invaluable comments and suggests towards improving the quality of our article titled " Negative Expressions by Social Robots and their Effects on Persuasive Behaviors" submitted for consideration for publication in the special issue “Advancements in Robotics: Perception, Manipulation, and Interaction” of Electronics Journal.

We have revised the article based on the comments and suggestions.

Thank you for your support

 

General Comment

This study explores context-aware persuasive behaviors for social robots through three experiments with humanoids ERICA and CommU. Results show negative behaviors are effective after repeated violations, especially in high-impact scenarios (e.g., COVID-19 rules). Preferences vary by user traits (Compliance Awareness, Agreeableness) and robot appearance, providing design insights for socially intelligent agents. Human-subject validation confirms the approach’s effectiveness. The main suggestions are as follows:

Comment 1: The overall structure of the paper is clear and logically coherent, but some paragraphs are slightly verbose. For example, the Introduction could more concisely summarize the research background and motivation.

Response 1: The entire article has been proofread, and the introduction has been better framed with adequate background, theorical rationales and motivation included.

Comment 2: The labels in Figures 3 and 7 should be more detailed to help readers quickly understand the data.

Response 2: Figures 3 and 7 have been modified based on the suggestions raised.

Comment 3: The sample sizes in Study 2 (N=98) and Study 3 (N=100) are relatively small, and all participants were from the U.S., which may limit the generalizability of the findings. This should be explicitly discussed in the paper.

Response 3: The following was added “Another limitation of this study is its reliance on participants exclusively residing in the United States and the sample size. Prior work in HRI has demonstrated that user responses to robot behaviors—including emotional expressions, authority cues, and norm enforcement strategies—vary significantly across cultural contexts [75]. As such, the generalizability of our findings may be limited; our subsequent work will focus on cross-cultural validation to investigate if the behavioral strategies proposed in this work are adaptable and effective across diverse sociocultural settings.”

Concerning our chosen sample sizes, they were sufficient for identifying statistically significant effects within this scope.

Comment 4: In Section 4, the discussion on user traits (CA and AG) is thorough, but it could further incorporate existing literature to explain why high-CA and low-AG users prefer negative behaviors, thereby strengthening the theoretical foundation.

Response 4: The following was added “It is noteworthy that the findings related to compliance awareness (CA) align with those of our previous study [59], which demonstrated that individuals with high levels of CA are more likely to evaluate negatively toned persuasive behaviors toward a violator favorably. Similarly, our findings regarding agreeableness (AG) groups are consistent with prior research [70], which reported that individuals low in agreeableness tend to respond more positively to leaders who display anger.”

Comment 5: The Conclusion summarizes the main content of the paper but does not sufficiently discuss the study’s limitations (e.g., the simple sample background and limited experimental scenarios). This should be supplemented.

Response 5: Sections for limitations and implications of the study have been included.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

This paper explores how people respond when social robots, specifically humanoid robots like ERICA and CommU, express negative emotions, such as anger, in situations where social norms are being violated. The paper is relevant as HRI is increasing, it is a good 3-fold study. However, I have some comments:

1. More emphasis on limitations would increase the contribution.
2. Clarify if the same script was used across ERICA and CommU.
3. Explain how CA and AG were measured and validated.

Minor comments:

1. Improve quality in Figures1 and 2.
2. A careful review of language is suggested.

Comments on the Quality of English Language

Can be improved

Author Response

Dear Reviewer,

We sincerely thank you for your invaluable comments and suggests towards improving the quality of our article titled " Negative Expressions by Social Robots and their Effects on Persuasive Behaviors" submitted for consideration for publication in the special issue “Advancements in Robotics: Perception, Manipulation, and Interaction” of Electronics Journal.

We have revised the article based on the comments and suggestions.

Thank you for your support

General Comment

This paper explores how people respond when social robots, specifically humanoid robots like ERICA and CommU, express negative emotions, such as anger, in situations where social norms are being violated. The paper is relevant as HRI is increasing, it is a good 3-fold study. However, I have some comments:

Comment 1: More emphasis on limitations would increase the contribution.

Response 1: Sections for implication and limitation of the study have been added

Comment 2: Clarify if the same script was used across ERICA and CommU.

Response 2: This was added “It is noteworthy that both robots employed an identical verbal script, and comparable gesture sets were adapted to ensure consistency across agents.”

Comment 3: Explain how CA and AG were measured and validated.

Response 3: This was added “We split participants into groups based on compliance awareness (CA) and agreeableness (AG) scores by drawing clue from the trait categorization approach proposed in [68]. We first estimated the mean score of each participant based on the responses to questions that assessed CA and AG. Based on the distribution of the mean scores of participants for each category, we set thresholds as shown in Table 1. This distribution ensured that participants were fairly split into two groups.”

Minor comments:

Comment 4: Improve quality in Figures1 and 2.

Response 4: The quality of Figures 1 and 2 has been improved.

Comment 5: A careful review of language is suggested.

Response 5: The article has been proofread.

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

Paper: Negative Expressions by Social Robots and their Effects on Persuasive Behaviors

This paper presents some findings towards equipping social robots with context-inspired persuasive behaviors for HRI. The paper needs major revisions to be in a suitable form. Please follow my comments to make the suitability of your paper.

 

Comments:

P1: Some numerical results should be mentioned in the abstract part.

P2: Figure 1 needs discussion with more information in the text of the paper. Every element of this Figure should be discussed. Also, Figure 4.

P3: The main contribution of this study and its importance to society should be discussed clearly in the text of the paper.

P4: In three cases studies presented in this paper (study 1, 2, and 3), how authors can support their followed hypothesis and criteria? Can compare with other previous published studies?

P5: In case study 1, the components and specifications of the Android robot ERICA should be presented and discussed in the paper.

P6: The results presented in this paper are subjective. Depending only on subjective results is not good and cannot be the main criteria. How authors can support this problem?

P7: The results of each case study (1, 2, 3) should be discussed with more information and compared with previously related works.

P8: List of abbreviations and their meaning should be added to the paper.

P9: In case study 3, the components and specifications of the CommU robot should be presented and discussed in the paper.

P10: In each case study, how authors determine the number of subjects or participants? Is this number enough for the study? Which criteria is followed?

P11: The restrictions of this study should be presented. In addition, some future work should be added to the conclusion part.

P12: References should be updated using 2024-2025 years.

P13: Finally, revise the English of the paper.

Comments for author File: Comments.pdf

Comments on the Quality of English Language

English should be revised carefully. 

Author Response

Comment 1: Some numerical results should be mentioned in the abstract part.

Response 1: Numeric data has been included in the abstract as suggested

Comment 2: Figure 1 needs discussion with more information in the text of the paper. Every element of this Figure should be discussed. Also, Figure 4.

Response 2: This has been added to describe Fig.1 “Specifically, we focused on humanoid robots of varying embodiments (ERICA and CommU) and considered persuasive strategies of different degrees of assertiveness (negative and non-negative persuasive strategies). Concerning expression modalities and behaviors for the robots, we considered both audio and visual modalities (facial and body gestures), and crafted these behaviors using a rule-based method where the gesture strokes for the robots were generated and inserted on focused words during expression, and after a gesture stroke, the hands of the robots were held along the sentence and turned back to the rest position before the next stroke for an entire utterance. For the context of persuasion, we considered persuasion for social compliance (dieting, COVID-19, and smoking prohibition). These points are highlighted in red in Fig. 1”

Regarding Fig 4., we included “Based on this, we employed the female-type android robot ERICA to evaluate participants’ impressions of four persuasive behaviors—politeness, logical reasoning, expressions of displeasure, and anger—across three norm-violation contexts: adherence to diet recommendations, smoking prohibition, and compliance with COVID-19 guidelines. The selection of the three contexts was intended, in part, to examine how the perceived impact of a norm violation—whether primarily self-directed (as in diet recommendation adherence) or other directed (as in smoking prohibition and adherence to COVID-19 guidelines)—influences individuals’ preferences for different persuasive strategies”

Comment 3: The main contribution of this study and its importance to society should be discussed clearly in the text of the paper.

Response 3: Sections for the implications and limitations of the study have been added to the article with discussions covering the concerns raised.

Comment 4: In three cases studies presented in this paper (study 1, 2, and 3), how authors can support their followed hypothesis and criteria? Can compare with other previous published studies?

Response 4: The following were included:

 “These hypotheses were partly informed by the work of [64] that demonstrated that higher DoF significantly enriches a robot’s nonverbal communication capabilities and [62], which suggested that the appropriateness and/or the effectiveness of an agent’s behavior may be situation dependent.”

“Specifically, H4 was formulated based on the work by [65], which suggests that people high on the agreeableness (AG) trait have high trust in others, including humanoid robots, which attract more trustworthiness due to their human-likeness and personification. Additionally, people with high AG have high empathetic concerns and a high tendency to advance social cohesion, and as such, may consider some behaviors appropriate for the robot in a specific context [6].”

“It is noteworthy that H5 was partially informed by the findings of  [41], which demonstrated that embodiment, particularly through natural gestures and expressive behaviors, enhances emotional engagement and the perceived social presence of the robot.”

Comment 5: In case study 1, the components and specifications of the Android robot ERICA should be presented and discussed in the paper.

Response 5: The following was included “Two robotic platforms were employed in the work: ERICA, a highly anthropomorphic female adult-like humanoid with facial degrees of freedom (DoF) and synthetic skin, and CommU, a compact child-like robot with limited DoF and no facial expression capability. We based our examination within HRI by focusing on the following theoretical rationales: the uncanny valley, morphological priming, and expressive bandwidth. Regarding uncanny valley and morphological priming, ERICA, with its realistic facial appearance and expressive DoF, may evoke the uncanny valley effect, eliciting more compliance through its behaviors in contrast to CommU in some contexts of persuasion [25]. Additionally, ERICA may engender more authority over CommU, which may prompt perceptions of innocence and a non-threatening presence [26]. Concerning the facial DoF and expressive bandwidth of the robots, ERICA’s rich facial articulation enables nuanced emotional communication—micro expressions, eyebrow raises, lip movements—whereas CommU is restricted to body posture and vocal tone. This increased expressivity may enhance the perception of ERICA’s persuasive behaviors, owing to better emotional clarity [27].”

Comment 6: The results presented in this paper are subjective. Depending only on subjective results is not good and cannot be the main criteria. How authors can support this problem?

Response 6: Theoretical rational for the work has been provided and limitations of the study also discussed.

Comment 7: The results of each case study (1, 2, 3) should be discussed with more information and compared with previously related works.

Response 7: The following were included:

“This finding also aligns with prior research. Li et al. [64] demonstrated that increased degrees of freedom (DoF) substantially enhance a robot’s capacity for nonverbal communication, while Fiorini et al. [41] showed that embodiment—especially through natural gestures and expressive behaviors—significantly improves users’ emotional engagement and the perceived social presence of the robot.”

“These findings are consistent with the work of Senaratne et al. [62], who proposed that the perceived appropriateness of an agent’s behavior is influenced by the contextual factors surrounding the interaction.”

“Our findings are consistent with our hypothesis (H5) and align with the results reported by [41], who demonstrated that higher levels of embodiment enhance emotional engagement and the perceived social presence—particularly the perceived authority—of a robotic agent. Moreover, our results further support the framework proposed by [62], which emphasizes that the appropriateness and effectiveness of an agent’s behavior are contingent upon the specific situational context.”

 

Comment 8: List of abbreviations and their meaning should be added to the paper.

Response 8: The list of abbreviations and their meaning have been included.

Comment 9: In case study 3, the components and specifications of the CommU robot should be presented and discussed in the paper.

Response 9: This was added to the introduction “Two robotic platforms were employed in the work: ERICA, a highly anthropomorphic female adult-like humanoid with facial degrees of freedom (DoF) and synthetic skin, and CommU, a compact child-like robot with limited DoF and no facial expression capability.”

Comment 10: In each case study, how authors determine the number of subjects or participants? Is this number enough for the study? Which criteria is followed?

Response 10: In each study, the number of participants was determined based on established guidelines for exploratory qualitative research. Specifically, sample sizes of 50, 98, and 100 participants across the three studies align with precedent in HRI and social robotics literature where within- and between-subjects designs are employed to explore nuanced social and affective responses. These sizes were sufficient to achieve reliable within-group comparisons and detect meaningful differences across conditions, particularly given the controlled and repeated-measures nature of the designs. Additionally, the criteria for determining participant numbers included statistical power considerations.

Comment 11: The restrictions of this study should be presented. In addition, some future work should be added to the conclusion part.

Response 11: A section for limitations has been included and possible future work highlighted.

Comment 12: References should be updated using 2024-2025 years.

Response 12: The references have been updated using 2024-2025 years

Comment 13: Finally, revise the English of the paper.

Response 13: The article has been proofread.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The author has carefully revised the manuscript according to the reviewers' comments, and the paper now meets the requirements for publication.

Reviewer 4 Report

Comments and Suggestions for Authors

I have no other comment.