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Will you donate money to a chatbot? The effect of chatbot anthropomorphic features and persuasion strategies on willingness to donate

T0 review · 5 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Giving a donation chatbot a name and a backstory makes people see it as more human but does not make them more willing to donate, and can even sour their attitudes toward it.

desk verdict A clean 2x2 study that would be worth a look, but the central H1b claim is contradicted by the paper's own reported coefficient, and the mediation design breaks temporal order. read the letter →

arxiv 2412.19976 v1 pith:KXAAKQ2I submitted 2024-12-28 cs.HC cs.AIcs.CY

Ekaterina Novozhilova , Jiacheng Huang , Le He , Ziling Li , James Cummings This is my paper · ORCID
classification cs.HCcs.AIcs.CY
keywords chatbotanthropomorphismwillingnesstodonatepersonificationpersuasionstrategymindlesshuman-chatbotinteractionnonprofitfundraisingAIdisclosure
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tests a common assumption behind charity chatbot design: that making a bot feel more human—by giving it a name, an avatar, and a personal story—will make people more willing to donate. In a 2x2 experiment (personified vs. non-personified chatbot, emotional vs. logical persuasion) with 76 participants, the personified bot did increase perceived anthropomorphism of the unconscious, 'mindless' kind, but it did not increase the amount people chose to donate. Instead, personification produced significantly less favorable overall chatbot perceptions, and this negative effect appeared when the bot used a logical, statistics-based appeal. The authors conclude that anthropomorphic cues are not a reliable route to donation behavior, and that in the donation context a plainly non-human bot giving logical reasons may be the safer design.

What carries the argument

The analytical machinery is a moderated mediation model estimated with PROCESS Model 7, in which chatbot personification is the predictor, willingness to donate is the outcome, perceived mindful anthropomorphism, perceived mindless anthropomorphism, and chatbot perceptions are parallel mediators, and persuasion strategy moderates the path from personification to the mediators. The conceptual load-bearing distinction comes from prior work separating mindful anthropomorphism (deliberate attribution of human-likeness to a non-human) from mindless anthropomorphism (the automatic, reflexive version). The model's results show that personification moves only the mindless pathway, that this pathway does not connect to donation behavior, and that the moderation pattern—personification hurting perceptions under logical appeals but not under emotional appeals—supports a consistency explanation of the findings.

What would settle it

A replication that measures perceived anthropomorphism and chatbot perceptions before the donation request—rather than after the full conversation—would settle whether the proposed causal chain holds. If the indirect effects of personification through perceptions become significant, or change sign, when perceptions are measured pre-donation, the current mediation conclusions would be refuted. A second decisive check is to replace the $10 capped endpoint with an open donation amount and recruit a larger, more donation-representative sample; if the direct and indirect effects remain null under those conditions, the 'no benefit from personification' claim is much stronger.

Watch

Extended reading notes

Core claim

The study's central claim is that commonly used anthropomorphic cues—a name, an avatar, and a personal background narrative—trigger mindless anthropomorphism (the reflexive sense that the agent is human-like) without triggering mindful anthropomorphism, and this perceptual shift does not carry over into willingness to donate. The direct effect of personification on donation amount was not significant, and the personified chatbot actually received significantly worse chatbot perceptions than the non-personified version, opposite to the authors' hypothesis. Moderation analyses show this negative effect was strongest in the logical-persuasion condition, while in the emotional-persuasion condition the personified and non-personified bots were perceived similarly. Read together with prior work on cue consistency, the authors argue that the fit between the chatbot's identity and its persuasive style matters: a machine-like agent that speaks in statistics feels more coherent than a humanized agent that does the same.

Load-bearing premise

The load-bearing assumption is that the perception ratings collected after the chatbot conversation reflect the same perceptions participants held when they chose their donation amount during the conversation; if making the donation choice changed their later ratings, the mediation analysis cannot establish the proposed causal order from personification through perceptions to donation behavior.

Editorial extensions

If this is right

  • Charity chatbot designers should not assume that human-like features increase donations; in this study, a name and backstory made attitudes worse rather than better.
  • A non-personified chatbot using logical, data-driven appeals produced the most favorable chatbot perceptions of the four conditions, suggesting this combination as a baseline design for donation contexts.
  • The study separates two routes to anthropomorphism: cues can trigger the mindless route without the mindful route, so evaluations and donation behavior should be measured separately.
  • The null mediation results imply that even when anthropomorphism is successfully evoked, it does not automatically act as a bridge to desired behavioral outcomes such as giving.
  • Because 81.6% of participants donated the full $10, the donation measure was near its ceiling; the paper's own limitation note implies the null effects on donation amount may be partly an artifact of this skew.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the consistency explanation generalizes, the same identity-persuasion mismatch should predict attitudes in other contexts: a personified assistant using dry logic would be evaluated poorly, while a clearly mechanical bot using emotional language may also seem inconsistent—a testable crossover interaction.
  • Because donation amount was elicited during the conversation and perception ratings came after it, the mediation estimates depend on the assumption that post-conversation ratings reflect pre-donation perceptions; measuring perceptions before the donation request in a follow-up would directly test that assumption.
  • The results suggest a possible negative mechanism beyond congruency: a humanized bot citing statistics may be seen as manipulative, while a non-human bot doing the same is seen as objective; measuring perceived sincerity or manipulativeness would separate these accounts.
  • The study's sample is small and homogeneous, so the null indirect effects are weak evidence of absence; a sufficiently powered replication with a continuous donation measure could reveal whether the absence of mediation is real.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 4 minor

Summary. The paper reports a between-subjects experiment (N = 76) crossing chatbot personification (name and background information vs. none) with persuasion strategy (emotional vs. logical) in a donation context, and tests whether perceived anthropomorphism (mindful and mindless) and chatbot perceptions mediate effects on willingness to donate (WTD) using PROCESS Model 7. The headline claims are that personified chatbots increase mindless anthropomorphism but do not increase WTD, and that personification can lead to negative chatbot attitudes, especially when paired with a logical appeal. The paper also reports moderated mediation results and discusses implications for AI disclosure regulations.

Significance. If the reported results were internally consistent, the study would be a useful counterpoint to CASA-based expectations that anthropomorphic cues improve chatbot outcomes, and it would extend congruency arguments to donation interactions. The paper is transparent about several limitations, including the small homogeneous sample and the ceiling effect in WTD, and it makes its coefficients and confidence intervals available in the text. However, the central claim is undermined by a sign inconsistency in the main predictor's effect on the primary mediator, and the mediation analysis has a temporal-order problem. These issues prevent the paper from supporting its abstract and discussion claims as written, though the underlying questions remain of interest to the HCI community.

major comments (5)
  1. [§4.1.2] The reported coefficient for chatbot personification on mindless anthropomorphism is β = -1.69, t(70) = -2.32, p = .02. Given that the mindless anthropomorphism scale is scored so that higher values indicate more anthropomorphism (§3.5.3), and given that §4.1.3 interprets a negative coefficient as showing that personification 'led to less favorable chatbot perceptions,' this negative sign means personification decreased mindless anthropomorphism, the opposite of H1b. The text nevertheless states 'H1b was supported.' This contradiction directly undermines the abstract's claim that a personified chatbot evokes perceived anthropomorphism and removes the X→M path required for the subsequent mediation claims. The authors must clarify the variable coding or correct the conclusion; if the coefficient is correct, the first half of the central claim collapses.
  2. [§3.5.1 and §3.1] WTD was elicited during the chatbot conversation (before the end of the interaction), while the mediator measures (mindful and mindless anthropomorphism, chatbot perceptions) were collected in the post-interaction questionnaire. The mediation model in Figure 2 treats the mediators as intervening between the manipulation and the outcome, but the measurement order is reversed. Consequently, the estimated indirect effects cannot support the proposed causal direction from personification through perceptions to WTD. The authors should either report the analysis as correlational with explicit caveats, or redesign the measurement order in future work.
  3. [§3.5.1 and §7] The WTD variable exhibits a severe ceiling effect: 81.58% of participants donated the full $10, and the mean is 8.66 (SD = 3.01) on a 0–10 scale. This near-dichotomous distribution severely limits variance and power. All bootstrap confidence intervals for indirect effects include zero, so the null mediation results may reflect measurement insensitivity rather than a true absence of effect. The limitations section acknowledges the skew but does not assess its impact; the authors should report alternative analyses (e.g., logistic or ordinal regression on donation amount, or a sensitivity analysis excluding or transforming the ceiling cases).
  4. [§3.2 and §4] Ten participants were excluded for failing the manipulation check, but no sensitivity analysis is reported. If exclusion rates differ by condition or correlate with outcomes, the reported estimates could be biased. The authors should report the number excluded per condition and rerun the key models with the full sample to establish robustness.
  5. [§3.5.2 and §3.5.3] The mindless anthropomorphism scale (attractive, exciting, pleasant, interesting, likable, sociable, friendly, personal) shares items (pleasant, interesting) with the chatbot experience component used in the aggregated chatbot perceptions measure. This construct overlap calls into question the discriminant validity of the two mediators and may inflate their intercorrelation, complicating the interpretation of the mediation paths. The authors should address this overlap, either by reporting a CFA or by re-analyzing with non-overlapping items.
minor comments (4)
  1. [§3.2] The participant age is reported as 'M = 21,06; SD = 2.39,' which appears to be a typo for 'M = 21.06.'
  2. [§3.5.2] The aggregated chatbot perceptions mean is reported as 'M = 4.81, SD = .13'; the standard deviation of .13 seems implausibly small relative to the component scales (SD = 1.04 and 1.29) and is likely a typo for SD ≈ 1.13.
  3. [§4.1.3] The interaction coefficient is reported as 'β = .979'; for readability and consistency with the other coefficients, it should be formatted as 'β = 0.98.'
  4. [Appendix] The Figure 2 description in the appendix is detailed and helpful, but it would be clearer if the significance labels (S, NS, S*) were defined in the main text near the figure rather than only in the appendix.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: this is an empirical 2x2 experiment whose hypotheses are tested against newly collected data; the reported coefficient sign inconsistency, mediation temporal ordering, and overlapping scale items are validity and internal-consistency concerns, not derivational circularity.

full rationale

This paper is a between-subjects behavioral experiment (N = 76) with manipulated factors (personification, persuasion strategy), measured mediators (mindful and mindless anthropomorphism, chatbot perceptions), and a measured outcome (willingness to donate), analyzed with PROCESS Model 7 mediation. There is no derivation chain in which an output quantity is equivalent to an input by construction: no equation is defined in terms of the result it is said to predict, no parameter is fitted to a subset of data and then renamed a prediction, and no uniqueness or ansatz result is imported from the authors' prior work to force a modeling choice. The reference list contains no self-citations by the present authors, so the self-citation patterns enumerated in the rubric do not arise. The central claim (personified chatbot evokes perceived anthropomorphism but not greater willingness to donate) rests on the reported regression and bootstrap results, all of which are computed from the new experimental data rather than derived from the hypotheses. The concerns raised by the skeptical review are real but are not circularity: the negative coefficient for personification on mindless anthropomorphism in Section 4.1.2 (beta = -1.69) that is nonetheless declared to support H1b is an internal sign/interpretation inconsistency; the elicitation of the donation amount during the conversation (Section 3.5.1) before the post-interaction perception questionnaire (Section 3.1) is a temporal-ordering threat to the causal mediation reading; and the shared adjectives ('pleasant', 'interesting') between the mindless anthropomorphism scale (Section 3.5.3) and the chatbot experience subscale (Section 3.5.2) create measurement overlap. None of these involves a claim reducing to its own inputs, and notably all indirect effects are reported non-significant, so no supportive conclusion is forced by construction. The honest finding is therefore no significant circularity (score 0).

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The study contributes an empirical test rather than a derivation. No free parameters are fitted; it relies on domain assumptions from prior theory (CASA, mindful/mindless anthropomorphism), assumes its psychometric scales measure distinct constructs, assumes mediation temporal order, and assumes manipulation-check exclusions are ignorable.

assumptions (4)
  • domain assumption The CASA paradigm and the mindful/mindless anthropomorphism distinction apply to text chatbots with name and background cues.
    The hypotheses and interpretation assume social responses to computers extend to donation chatbots; cited from [30], [25], and [2].
  • ad hoc to paper The mindless anthropomorphism scale and the chatbot perceptions scale measure distinct constructs despite overlapping items.
    Items in mindless anthropomorphism (attractive, exciting, pleasant, interesting, likable, sociable, friendly, personal) overlap with chatbot experience items (interesting, entertaining, enjoyable, pleasant), so distinct-construct validity is assumed without discriminant validity evidence.
  • ad hoc to paper Mediation via PROCESS Model 7 requires that mediators are measured after the manipulation but before the outcome.
    Causal mediation claims depend on temporal precedence of mediators over outcome; the design reverses this for all three mediators because donation amount was elicited during the conversation.
  • domain assumption Excluding participants who failed the manipulation check does not bias the estimates.
    No sensitivity analysis or per-condition failure rates are reported, so the exclusions are assumed ignorable.

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Pith. "Pith review of Will you donate money to a chatbot? The effect of chatbot anthropomorphic features and persuasion strategies on willingness to donate." pith.science (2026). https://pith.science/paper/KXAAKQ2I

@misc{pith2026241219976,
  author       = {Pith},
  title        = {Pith review of: Will you donate money to a chatbot? The effect of chatbot anthropomorphic features and persuasion strategies on willingness to donate},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/KXAAKQ2I}},
  note         = {Machine review of arXiv:2412.19976}
}
read the original abstract

This work investigates the causal mechanism behind the effect of chatbot personification and persuasion strategies on users' perceptions and donation likelihood. In a 2 (personified vs. non-personified chatbot) x 2 (emotional vs. logical persuasion strategy) between-subjects experiment (N=76), participants engaged with a chatbot that represented a non-profit charitable organization. The results suggest that interaction with a personified chatbot evokes perceived anthropomorphism; however, it does not elicit greater willingness to donate. In fact, we found that commonly used anthropomorphic features, like name and narrative, led to negative attitudes toward an AI agent in the donation context. Our results showcase a preference for non-personified chatbots paired with logical persuasion appeal, emphasizing the significance of consistency in chatbot interaction, mirroring human-human engagement. We discuss the importance of moving from exploring the common scenario of a chatbot with machine identity vs. a chatbot with human identity in light of the recent regulations of AI systems.

Figures

Figures reproduced from arXiv: 2412.19976 by the authors.

Figure 1
Figure 1. Chatbot Manipulation. On the left, an example of [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Causal diagram with tested hypotheses (see Figure Description in Appendix). 4.1 The effect of chatbot personification on perceived anthropomorphism 4.1.1Mindful Anthropomorphism. The model predicting mindful anthropomorphism was not significant, F(5, 70) = 1.98, p = .09, explaining 12.41% of the variance (R2 = .12). Neither chatbot personification, persuasion strategy, nor the interaction between personification and… view at source ↗

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Works this paper leans on

49 extracted references · 30 canonical work pages

  1. [1]

    Mirages: On Anthropomorphism in Dialogue Systems

    Gavin Abercrombie, Amanda Cercas Curry, Tanvi Dinkar, and Zeerak Talat. “Mirages: On Anthropomorphism in Dialogue Systems”. In: arXiv preprint arXiv:2305.09800 (2023). https://doi.org/10.48550/arXiv.2305.09800

    arXiv doi:10.48550/arxiv.2305.09800 2023

  2. [2]

    Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions

    Theo Araujo. “Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions”. In: Computers in Human Behavior 85 (2018), pp. 183–189. https://doi.org/10.1016/j.chb.2018.03.051

    doi:10.1016/j.chb.2018.03.051 2018

  3. [3]

    Smiling AI agents: How anthropomorphism and broad smiles increase charitable giving

    Tae Hyun Baek, Marat Bakpayev, Sukki Yoon, and Seeun Kim. “Smiling AI agents: How anthropomorphism and broad smiles increase charitable giving”. In: International Journal of Advertising 41.5 (2022), pp. 850–867. https://doi.org/10.1080/02650487.2021.2011654

    work page arXiv 2022

  4. [4]

    Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI

    Markus Blut, Cheng Wang, Nancy V. Wünderlich, and Christian Brock. “Understanding anthropomorphism in service provision: a meta-analysis of physical robots, chatbots, and other AI”. In: Journal of the Academy of Marketing Science 49 (2021), pp. 632–658. http://dx.doi.org/10.1007/s11747-020-00762-y

    doi:10.1007/s11747-020-00762-y 2021

  5. [5]

    Task-Dependent Algorithm Aversion

    Noah Castelo, Maarten Bos, and Donald Lehmann. “Task-Dependent Algorithm Aversion”. In: Journal of Marketing Research 56 (July 2019). https://doi.org/10.1177/0022243719851788

    doi:10.1177/0022243719851788 2019

  6. [6]

    Artificial Intelligence (AI) student assistants in the classroom: Designing chatbots to support student success

    Yu Chen, Scott Jensen, Leslie J. Albert, Sambhav Gupta, and Terri Lee. “Artificial Intelligence (AI) student assistants in the classroom: Designing chatbots to support student success”. In: Information Systems Frontiers (2022). https://doi.org/10.1007/s10796-022-10291-4

    work page doi:10.1007/s10796-022-10291-4 2022

  7. [7]

    Who’s Johnny?’Anthropomorphic framing in human-robot interaction, integration, and policy

    Kate Darling. “Who’s Johnny?’Anthropomorphic framing in human-robot interaction, integration, and policy”. In: Robot Ethics 2.0: From Autonomous Cars to Artificial Intelligence. IEEE, 2015, pp. 318–323. https://doi.org/10.1109/ROMAN.2015.7333675

    arXiv 2015

  8. [8]

    Testing for altruism and social pressure in charitable giving

    Stefano DellaVigna, John A. List, and Ulrike Malmendier. “Testing for altruism and social pressure in charitable giving”. In: The quarterly journal of economics 127.1 (2012), pp. 1–56. https://doi.org/10.1093/qje/qjr050

    doi:10.1093/qje/qjr050 2012

Show all 49 references
  1. [11]

    On seeing human: A three-factor theory of anthropomorphism

    Nicholas Epley, Adam Waytz, and John T. Cacioppo. “On seeing human: A three-factor theory of anthropomorphism”. In: Psychological Review 114.4 (2007), pp. 864–886. https://doi.org/10.1037/0033-295X.114.4.864

    2007 doi

  2. [12]

    2018, pp

    Springer. 2018, pp. 194–208. https://doi.org/10.1007/978-3-030-01437-7_16

    2018 doi

  3. [13]

    What makes users trust a chatbot for customer service? An exploratory interview study

    Asbjørn Følstad, Cecilie Bertinussen Nordheim, and Cato Alexander Bjørkli. “What makes users trust a chatbot for customer service? An exploratory interview study”. In: Internet Science: 5th International Conference, INSCI 2018, St. Petersburg, Russia, October 24–26, 2018, Proceedings

    2018

  4. [14]

    Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions

    Eun Go and S. Shyam Sundar. “Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions”. In: Computers in Human Behavior 97 (2019), pp. 304–316. https://doi.org/10.1016/j.chb.2019.01.020

    2019 doi

  5. [15]

    When a talking-face computer agent is half-human and half-humanoid: Human identity and consistency preference

    Li Gong and Clifford Nass. “When a talking-face computer agent is half-human and half-humanoid: Human identity and consistency preference”. In: Human communication research 33.2 (2007), pp. 163–193. https://doi.org/10.1111/j.1468-2958.2007.00295.x

    2007

  6. [16]

    The impact of anthropomorphism on consumers’ purchase decisions in chatbot commerce

    Min Chung Han. “The impact of anthropomorphism on consumers’ purchase decisions in chatbot commerce”. In: Journal of Internet Commerce 20.1 (2021), pp. 46–65. https://doi.org/10.1080/15332861.2020.1863022

    2021

  7. [17]

    Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping

    Khaled Hassanein and Milena Head. “Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping”. In: International journal of human-computer studies 65.8 (2007), pp. 689–708. https://doi.org/10.1016/j.ijhcs.2006.11.018

    2007 doi

  8. [19]

    Under pressure: Examining social conformity with computer and robot groups

    Nicholas Hertz and Eva Wiese. “Under pressure: Examining social conformity with computer and robot groups”. In: Human factors 60.8 (2018), pp. 1207–1218. https://doi.org/10.1177/0018720818788473 12

    2018 doi

  9. [20]

    https://doi.org/10.1111/jedm.12050

    doi

  10. [21]

    Using theory of mind to assess users’ sense of agency in social chatbots

    Evelien Heyselaar and Tibor Bosse. “Using theory of mind to assess users’ sense of agency in social chatbots”. In: International workshop on chatbot research and design. Springer. 2019, pp. 158–169. https://doi.org/10.1007/978-3-030-39540-7_11

    2019 doi

  11. [22]

    Marketing mix of non-profit organizations (NPO) affecting the willingness-to-donate (WTD) in Malaysia

    Chong Li Hung, May Myo Set Htwe, and Quah Kheng Siong. “Marketing mix of non-profit organizations (NPO) affecting the willingness-to-donate (WTD) in Malaysia”. In: Proceedings of the 3rd International Conference on Business and Information Management. 2019, pp. 27–31. https://...

    2019

  12. [23]

    Consistency of personality in interactive characters: verbal cues, non-verbal cues, and user characteristics

    Katherine Isbister and Clifford Nass. “Consistency of personality in interactive characters: verbal cues, non-verbal cues, and user characteristics”. In: International journal of human-computer studies 53.2 (2000), pp. 251–267. https://doi.org/10.1006/ijhc.2000.0368

    2000

  13. [24]

    Privacy concerns in chatbot interactions

    Carolin Ischen, Theo Araujo, Hilde Voorveld, Guda van Noort and Edith Smit. “Privacy concerns in chatbot interactions”. In: Chatbot Research and Design: Third International Workshop, CONVERSATIONS 2019, Amsterdam, The Netherlands, November 19–20, 2019, Revised Selected Papers

    2019

  14. [25]

    2020, pp

    Springer. 2020, pp. 34–48. https://doi.org/10.1007/978-3-030-39540-7_3

    2020 doi

  15. [26]

    How chatbots influence marketing

    Dominika Kaczorowska-Spychalska. “How chatbots influence marketing”. In: Management 23.1 (2019), pp. 251–270. https://doi.org/10.2478/manment- 2019-0015

    2019 doi

  16. [27]

    Anthropomorphism of computers: Is it mindful or mindless?

    Youjeong Kim and S. Shyam Sundar. “Anthropomorphism of computers: Is it mindful or mindless?” In: Computers in Human Behavior 28.1 (2012), pp. 241–250. https://doi.org/10.1016/j.chb.2011.09.006

    2012 doi

  17. [28]

    Algorithmic Transference: People Overgeneralize Failures of AI in the Government

    Chiara Longoni, Luca Cian, and Ellie J. Kyung. “Algorithmic Transference: People Overgeneralize Failures of AI in the Government”. In: Journal of Marketing Research 60.1 (2023), pp. 170–188. https://doi.org/10.1177/00222437221110139

    2023 doi

  18. [29]

    Does distrust in humans predict greater trust in AI? Role of individual differences in user responses to content moderation

    Maria D. Molina and S. Shyam Sundar. “Does distrust in humans predict greater trust in AI? Role of individual differences in user responses to content moderation”. In: New Media & Society (2022), p. 14614448221103534. https://doi.org/10.1177/14614448221103534

    2022 doi

  19. [30]

    Machines and mindlessness: Social responses to computers

    Clifford Nass and Youngme Moon. “Machines and mindlessness: Social responses to computers”. In: Journal of social issues 56.1 (2000), pp. 81–103. https://doi.org/10.1111/0022-4537.00153

    2000

  20. [31]

    Computers are social actors

    Clifford Nass, Jonathan Steuer, and Ellen R. Tauber. “Computers are social actors”. In: Proceedings of the SIGCHI conference on Human factors in computing systems. 1994, pp. 72–78. https://doi.org/10.1145/191666.191703

    1994

  21. [32]

    Chatbot-based Tourist Recommendations Using Model-based Reasoning

    Iulia Nica, Oliver A. Tazl, and Franz Wotawa. “Chatbot-based Tourist Recommendations Using Model-based Reasoning.” In: ConfWS. 2018, pp. 25–30. http://confws.ist.tugraz.at

    2018

  22. [33]

    Human-computer interaction in customer service: the experience with AI chatbots—a systematic literature review

    Luminit Nicolescu and Monica Teodora Tudorache. “Human-computer interaction in customer service: the experience with AI chatbots—a systematic literature review”. In: Electronics 11.10 (2022), p

    2022

  23. [34]

    Designing chatbots for guiding online peer support conversations for adults with ADHD

    Oda Elise Nordberg, Jo Dugstad Wake, Emilie Sektnan Nordby, Eivind Flobak, Tine Nordgreen, Suresh Kumar Mukhiya, and Frode Guribye. “Designing chatbots for guiding online peer support conversations for adults with ADHD”. In: International workshop on Chatbot research and desig...

    2019 doi

  24. [35]

    Effects of benefactor and recipient status on helping behavior

    Janak Pandey. “Effects of benefactor and recipient status on helping behavior”. In: The Journal of Social Psychology 108.2 (1979), pp. 171–176. https://doi.org/10.1080/00224545.1979.9711628

    1979

  25. [36]

    Effect of AI chatbot empathy and identity disclosure on willingness to donate: the mediation of humanness and social presence

    Gain Park, Myungok Chris Yim, Jiyun Chung, and Seyoung Lee. “Effect of AI chatbot empathy and identity disclosure on willingness to donate: the mediation of humanness and social presence”. In: Behaviour & Information Technology (2022), pp. 1–13. https://doi.org/10.1080/0144929...

    2022

  26. [37]

    I, chatbot! The impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions

    Gabriele Pizzi, Virginia Vannucci, Valentina Mazzoli, and Raffaele Donvito. “I, chatbot! The impact of anthropomorphism and gaze direction on willingness to disclose personal information and behavioral intentions”. In: Psychology Marketing (2023), pp. 1–16. https://doi.org/10....

    2023 doi

  27. [38]

    Does the chimpanzee have a theory of mind?

    David Premack and Guy Woodruff. “Does the chimpanzee have a theory of mind?” In: Behavioral and brain sciences 1.4 (1978), pp. 515–526. https://doi.org/10.1017/s0140525x00076512

    1978 doi

  28. [39]

    The impact of chatbots on customer service performance

    Alexander Rossmann, Alfred Zimmermann, and Dieter Hertweck. “The impact of chatbots on customer service performance”. In: Advances in the human side of service engineering: Proceedings of the AHFE 2020 Virtual Conference on The Human Side of Service Engineering, July 16-20, 20...

    2020

  29. [40]

    Effects of visual and linguistic anthropomorphic cues on social perception, self-awareness, and information disclosure in a health website

    Young June Sah and Wei Peng. “Effects of visual and linguistic anthropomorphic cues on social perception, self-awareness, and information disclosure in a health website”. In: Computers in Human Behavior 45 (2015), pp. 392–401. https://doi.org/10.1016/j.chb.2014.12.055

    2015 doi

  30. [41]

    Effects of persuasive dialogues: testing bot identities and inquiry strategies

    Weiyan Shi, Xuewei Wang, Yoo Jung Oh, Jingwen Zhang, Saurav Sahay, and Zhou Yu. “Effects of persuasive dialogues: testing bot identities and inquiry strategies”. In: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. 2020, pp. 1–13. https://doi.org/1...

    2020

  31. [42]

    Conceptualizing sources in online news

    S. Shyam Sundar and Clifford Nass. “Conceptualizing sources in online news”. In: Journal of communication 51.1 (2001), pp. 52–72. https://doi.org/10.1111/j.1460-2466.2001.tb02872.x

    2001

  32. [43]

    Empathy as added value in predicting donation behavior

    Griet A. Verhaert and Dirk Van den Poel. “Empathy as added value in predicting donation behavior”. In: Journal of Business Research 64.12 (2011), pp. 1288–1295. https://doi.org/10.1016/j.jbusres.2010.12.024

    2011 doi

  33. [44]

    Children conform, adults resist: A robot group induced peer pressure on normative social conformity

    Anna-Lisa Vollmer, Robin Read, Dries Trippas, and Tony Belpaeme. “Children conform, adults resist: A robot group induced peer pressure on normative social conformity”. In: Science robotics 3.21 (2018), eaat7111. https://doi.org/10.1126/scirobotics.aat7111

    2018 doi

  34. [45]

    Perceived effectiveness of interpersonal persuasion strategies in computer-mediated communication

    E. Vance Wilson. “Perceived effectiveness of interpersonal persuasion strategies in computer-mediated communication”. In: Computers in Human 13 Behavior 19.5 (2003), pp. 537–552. https://doi.org/10.1016/s0747-5632(03)00006-2

    2003 doi

  35. [46]

    Is somebody there? Anthropomorphic website design and intention to purchase from online stores

    Steffen Wölfl, Jasmin Maria Feste, and Leonore Dorothea Katharina Peters. “Is somebody there? Anthropomorphic website design and intention to purchase from online stores”. In: Proceedings of the 2019 AMCIS Conference

    2019

  36. [48]

    Non-local or local brands? A multi-level investigation into confidence in brand origin identification and its strategic implications

    Lianxi Zhou, Zhiyong Yang, and Michael K Hui. “Non-local or local brands? A multi-level investigation into confidence in brand origin identification and its strategic implications”. In: Journal of the academy of marketing science 38 (2010), pp. 202–218. https://doi.org/10.1007...

    2010 doi

  37. [49]

    How human–chatbot interaction impairs charitable giving: the role of moral judgment

    Yuanyuan Zhou, Zhuoying Fei, Yuanqiong He, and Zhilin Yang. “How human–chatbot interaction impairs charitable giving: the role of moral judgment”. In: Journal of Business Ethics 178.3 (2022), pp. 849–865. https://doi.org/10.1007/s10551-022-05045-w APPENDIX Figure 2 Description

    2022 doi

  38. [1579]

    https://doi.org/10.3390/electronics11101579

    doi

  39. [2015]

    Springer Singapore, 2017, pp. 15–26. https://doi.org/10.1007/978-981-10-3662-0_2

    2017 doi

  40. [2019]

    https://doi.org/10.48550/arXiv.1906.06725

    1906 doi arXiv

  41. [2023]

    https://doi.org/10.4324/9781003273851-6

    doi

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.