A Need for Trust in Conversational Interface Research

Elaheh Sanoubari; Justin Edwards

arxiv: 1907.01923 · v1 · pith:XZ4WGMDWnew · submitted 2019-07-03 · 💻 cs.HC

A Need for Trust in Conversational Interface Research

Justin Edwards , Elaheh Sanoubari This is my paper

Pith reviewed 2026-05-25 09:56 UTC · model grok-4.3

classification 💻 cs.HC

keywords trustconversational interfacessocial robotsembodied agentsconversational assistantshuman-computer interactionmeasurement

0 comments

The pith

Trust is critical yet inconsistently defined and measured across conversational interface research.

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

The paper examines how trust has been treated in research on social robots, embodied agents, and conversational assistants. It finds that while users consistently flag trust as important, the field has not settled on a shared understanding of the concept or reliable ways to measure it. The authors review various dimensions of trust and existing measurement approaches with the goal of encouraging more cross-field discussion. This matters because without clearer concepts, it is difficult to compare results or design systems that reliably earn user trust.

Core claim

Across several branches of conversational interaction research including interactions with social robots, embodied agents, and conversational assistants, users have identified trust as a critical part of those interactions. Nevertheless, there is little agreement on what trust means within these sort of interactions or how trust can be measured. The paper explores some of the dimensions of trust as it has been understood in previous work and outlines some of the ways trust has been measured in the hopes of furthering discussion of the concept across the field.

What carries the argument

The review and comparison of trust dimensions and measurement techniques drawn from prior studies on robots, agents, and assistants.

If this is right

Shared definitions would let researchers directly compare trust findings from robot studies with those from agent and assistant studies.
Agreed-upon measures could improve how conversational systems are evaluated for their ability to build trust.
Greater consensus on trust might guide the design of interfaces that more consistently earn user confidence.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If trust differs by interaction type, context-specific scales for robots versus assistants may work better than one universal framework.
Future tests could check whether using a common trust measure alters which features designers prioritize in new interfaces.
Linking this review to psychological models of trust might clarify whether conversational trust is a distinct type of relationship.

Load-bearing premise

The lack of agreement on trust definitions and measures is mainly a barrier to progress rather than a sign that trust means genuinely different things in each context.

What would settle it

An empirical study showing that trust in social robot interactions and trust in text-based conversational assistant interactions are unrelated constructs with no shared ability to predict user behavior or preferences.

read the original abstract

Across several branches of conversational interaction research including interactions with social robots, embodied agents, and conversational assistants, users have identified trust as a critical part of those interactions. Nevertheless, there is little agreement on what trust means within these sort of interactions or how trust can be measured. In this paper, we explore some of the dimensions of trust as it has been understood in previous work and we outline some of the ways trust has been measured in the hopes of furthering discussion of the concept across the field.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a short position paper noting inconsistent trust definitions across conversational HCI subfields but providing no new data, framework, or detailed evidence of the disagreement.

read the letter

The main thing here is that trust gets invoked in work on social robots, embodied agents, and conversational assistants, yet the field lacks shared definitions or measures. The paper flags this and lists some dimensions and measurement approaches from existing literature to encourage talk about it. That's the extent of the contribution. It does not claim new results or downstream applications. The observation itself is reasonable; anyone who has read across these areas has probably noticed the same scatter in how trust is operationalized. The authors stay within their subfield and do not overreach. The text is clear about its goal as a discussion starter rather than a solution. The soft spot is that the central claim of little agreement is asserted without concrete examples or a systematic comparison of definitions and scales. An abstract-only review already showed this gap, and the full text does not close it with citations that demonstrate the variance in practice. No quantitative backing or first-principles argument appears either. This leaves the reader to take the authors' word on the scale of the problem. The paper is for HCI researchers already working on conversational systems who want a quick prompt on trust measurement issues. It will not move the needle for someone seeking a new model or reproducible findings. A serious referee could usefully check whether the listed dimensions and measures are representative and whether the call for discussion is backed by enough prior work to justify publication as a position piece. I would send it to review in a venue that accepts short discussion notes, with the expectation that the authors add specific examples of conflicting usages.

Referee Report

2 major / 2 minor

Summary. The manuscript is a position paper claiming that trust is identified as a critical factor by users in interactions with social robots, embodied agents, and conversational assistants across conversational interaction research. However, there is little agreement on the meaning of trust in these contexts or on appropriate measurement methods. The paper reviews dimensions of trust from prior work and outlines measurement approaches with the goal of stimulating discussion in the field.

Significance. If the central observation of fragmented understanding of trust holds, the paper could play a useful role in prompting the conversational interfaces community to develop more shared definitions and metrics, potentially improving comparability of studies across sub-areas like robotics and virtual agents.

major comments (2)

Abstract: The assertion that 'there is little agreement on what trust means within these sort of interactions or how trust can be measured' is presented without specific examples of conflicting definitions or measures from the literature, which is load-bearing for the paper's motivation to survey dimensions and methods.
Introduction (or equivalent section): The paper does not address whether observed differences in trust conceptualizations reflect genuinely distinct phenomena across robot, agent, and assistant contexts rather than a lack of consensus that requires resolution.

minor comments (2)

The abstract could benefit from one or two concrete citations illustrating divergent trust definitions to ground the claim of disagreement.
The manuscript would be strengthened by a brief concluding section that proposes next steps for the community rather than ending solely on the invitation to discuss.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our position paper. We address each major comment below and indicate planned revisions where appropriate.

read point-by-point responses

Referee: Abstract: The assertion that 'there is little agreement on what trust means within these sort of interactions or how trust can be measured' is presented without specific examples of conflicting definitions or measures from the literature, which is load-bearing for the paper's motivation to survey dimensions and methods.

Authors: We agree the abstract claim would be stronger with immediate grounding. The manuscript body reviews multiple dimensions and measurement approaches drawn from prior work across robots, agents, and assistants, which collectively illustrate the variation. In revision we will expand the introduction to include two or three brief, concrete examples of conflicting definitions and measures (e.g., differing emphasis on competence versus benevolence, or questionnaire versus behavioral metrics) so the motivation is explicitly supported before the survey sections. revision: yes
Referee: Introduction (or equivalent section): The paper does not address whether observed differences in trust conceptualizations reflect genuinely distinct phenomena across robot, agent, and assistant contexts rather than a lack of consensus that requires resolution.

Authors: The paper frames the observed fragmentation as motivation for cross-field discussion rather than asserting that all differences must be resolved into a single consensus. We acknowledge the referee's point that some differences may legitimately reflect context-specific phenomena. In the revised introduction we will add a short paragraph explicitly noting this alternative explanation and positioning the call for discussion as a means to determine whether shared metrics, context-tailored approaches, or both are warranted. revision: yes

Circularity Check

0 steps flagged

No significant circularity identified

full rationale

The paper is a qualitative position statement surveying dimensions and measurements of trust across conversational interaction research. It advances no derivations, equations, predictions, or fitted quantities. The core claim of limited agreement on trust definitions is presented as an observation drawn from external literature rather than derived from any internal construction or self-citation chain. No load-bearing steps reduce to inputs by definition or renaming.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Position paper without formal models, data fitting, or technical derivations; no free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5595 in / 953 out tokens · 22429 ms · 2026-05-25T09:56:42.988331+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages

[1]

Oya Celiktutan and Hatice Gunes. 2017. Automatic Predic tion of Impres- sions in Time and across Varying Context: Personality, Attr activeness and Likeability. IEEE Transactions on Aﬀective Computing 8, 1 (Jan. 2017), 29–42. https://doi.org/10.1109/TAFFC.2015.2513401

work page doi:10.1109/taffc.2015.2513401 2017
[2]

Leigh Clark, Phillip Doyle, Diego Garaialde, Emer Gilma rtin, Stephan Schlögl, Jens Edlund, Matthew Aylett, João Cabral, Cosmin Munteanu, and Benjamin Cowan. 2018. The State of Speech in HCI: Trends, Themes and Ch allenges. arXiv:1810.06828 [cs] (Oct. 2018). http://arxiv.org/abs/1810.06828 arXiv: 1810.06828

work page arXiv 2018
[3]

Cowan, Na- dia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Ju stin Edwards, Brendan Spillane, Emer Gilmartin, and Christine Murad

Leigh Clark, Cosmin Munteanu, Vincent Wade, Benjamin R. Cowan, Na- dia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Ju stin Edwards, Brendan Spillane, Emer Gilmartin, and Christine Murad. 201 9. What Makes a Good Conversation?: Challenges in Designing Truly C onversa- tional Agents. In Proceedings of the 2019 CHI Conference on Human Factors in Com...

work page doi:10.1145/3290605.3300705 2019
[4]

Benjamin R Cowan, Nadia Pantidi, David Coyle, Kellie Mor rissey, Peter Clarke, Sara Al-Shehri, David Earley, and Natasha Bandeira. 2017. W hat can i help you with?: infrequent users’ experiences of intelligent perso nal assistants. In Proceed- ings of the 19th International Conference on Human-Compute r Interaction with Mobile Devices and Services . ACM, 43

work page 2017
[5]

Ewart J de Visser, Samuel S Monfort, Ryan McKendrick, Mel issa AB Smith, Patrick E McKnight, Frank Krueger, and Raja Parasuraman. 20 16. Almost hu- man: Anthropomorphism increases trust resilience in cogni tive agents. Journal of Experimental Psychology: Applied 22, 3 (2016), 331

work page 2016
[6]

Florian N Egger. 2000. Trust me, I’m an online vendor: tow ards a model of trust for e-commerce system design. In CHI’00 extended abstracts on Human factors in computing systems. ACM, 101–102

work page 2000
[7]

Flanagin and Miriam J

Andrew J. Flanagin and Miriam J. Metzger. 2007. The role o f site features, user attributes, and information veriﬁcation behaviors on the p erceived credibility of web-based information. New Media & Society 9, 2 (April 2007), 319–342. https://doi.org/10.1177/1461444807075015

work page doi:10.1177/1461444807075015 2007
[8]

BJ Fogg and Hsiang Tseng. 1999. The elements of computer c redibility. In Pro- ceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 80–87

work page 1999
[9]

Amos Freedy, Ewart DeVisser, Gershon Weltman, and Nicol e Coeyman. 2007. Measurement of trust in human-robot collaboration. In 2007 International Sym- posium on Collaborative Technologies and Systems . IEEE, 106–114

work page 2007
[10]

Kerstin Sophie Haring, David Silvera-Tawil, Yoshio Ma tsumoto, Mari Velonaki, and Katsumi Watanabe. 2014. Perception of an android robot i n Japan and Aus- tralia: A cross-cultural comparison. In International conference on social robotics . Springer, 166–175

work page 2014
[11]

Kevin Anthony Hoﬀ and Masooda Bashir. 2015. Trust in aut omation: Integrating empirical evidence on factors that inﬂuence trust. Human Factors 57, 3 (2015), 407–434

work page 2015
[12]

Oliver P John, Sanjay Srivastava, and others. 1999. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research 2, 1999 (1999), 102–138

work page 1999
[13]

Spiro Kiousis. 2001. Public Trust or Mistrust? Percept ions of Media Credibility in the Information Age. Mass Communication and Society 4, 4 (Nov. 2001), 381–403. https://doi.org/10.1207/S15327825MCS0404_4

work page doi:10.1207/s15327825mcs0404_4 2001
[14]

John D Lee and Katrina A See. 2004. Trust in automation: D esigning for appro- priate reliance. Human factors 46, 1 (2004), 50–80

work page 2004
[15]

Jin Joo Lee, Brad Knox, Jolie Baumann, Cynthia Breazeal , and David DeSteno

work page
[16]

Frontiers in psychology 4 (2013), 893

Computationally modeling interpersonal trust. Frontiers in psychology 4 (2013), 893

work page 2013
[17]

Like Having a Reall y Bad PA

Ewa Luger and Abigail Sellen. 2016. "Like Having a Reall y Bad PA": The Gulf between User Expectation and Experience of Conversati onal Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Com put- ing Systems - CHI ’16 . ACM Press, Santa Clara, California, USA, 5286–5297. https://doi.org/10.1145/2858036.2858288

work page doi:10.1145/2858036.2858288 2016
[18]

D Harrison McKnight, Vivek Choudhury, and Charles Kacm ar. 2002. The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems 11, 3-4 (2002), 297–323. A Need for Trust in Conversational Interface Research CUI 20 19, August 22–23, 2019, Dublin, Ireland

work page 2002
[19]

Panagiotis Mitkidis, John J McGraw, Andreas Roepstorﬀ , and Sebastian Wallot

work page
[20]

Physiology & behavior 149 (2015), 101–106

Building trust: Heart rate synchrony and arousal duri ng joint action in- creased by public goods game. Physiology & behavior 149 (2015), 101–106

work page 2015
[21]

Christie Olson and Kelli Kemery. 2019. 2019 Voice report: Consumer adoption of voice technology and digital assistants . Technical Report. Microsoft

work page 2019
[22]

Jens Riegelsberger, M Angela Sasse, and John D McCarthy . 2003. Shiny happy people building trust?: photos on e-commerce websites and c onsumer trust. In Proceedings of the SIGCHI conference on Human factors in comp uting systems . ACM, 121–128

work page 2003
[23]

Denise M Rousseau, Sim B Sitkin, Ronald S Burt, and Colin Camerer. 1998. Not so diﬀerent after all: A cross-discipline view of trust. Academy of management review 23, 3 (1998), 393–404

work page 1998
[24]

Maha Salem, Gabriella Lakatos, Farshid Amirabdollahi an, and Kerstin Dauten- hahn. 2015. Would you trust a (faulty) robot?: Eﬀects of erro r, task type and personality on human-robot cooperation and trust. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot I nteraction. ACM, 141–148

work page 2015
[25]

Scissors, Alastair J

Lauren E. Scissors, Alastair J. Gill, Kathleen Geraght y, and Darren Gergle. 2009. In CMC we trust: the role of similarity. In Proceedings of the 27th international conference on Human factors in computing systems - CHI 09 . ACM Press, Boston, MA, USA, 527. https://doi.org/10.1145/1518701.1518783

work page doi:10.1145/1518701.1518783 2009
[26]

Elaine Short, Justin Hart, Michelle Vu, and Brian Scass ellati. 2010. No fair an interaction with a cheating robot. In 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI) . IEEE, 219–226

work page 2010
[27]

Ilaria Torre, Leigh Clark, and Benjamin R Cowan. 2018. Measuring and designing trust in Human-Agent Interaction

work page 2018
[28]

Ilaria Torre, Jeremy Goslin, Laurence White, and Debor a Zanatto. 2018. Trust in artiﬁcial voices: A congruency eﬀect of ﬁrst impressions an d behavioural expe- rience. In Proceedings of the Technology, Mind, and Society . ACM, 40

work page 2018
[29]

Lin Wang, Pei-Luen Patrick Rau, Vanessa Evers, Benjamin Krisper Robinson, and Pamela Hinds. 2010. When in Rome: the role of culture & contex t in adherence to robot recommendations. In Proceedings of the 5th ACM/IEEE international con- ference on Human-robot interaction . IEEE Press, 359–366

work page 2010
[30]

James E Young, Richard Hawkins, Ehud Sharlin, and Takeo Igarashi. 2009. To- ward acceptable domestic robots: Applying insights from so cial psychology. In- ternational Journal of Social Robotics 1, 1 (2009), 95

work page 2009

[1] [1]

Oya Celiktutan and Hatice Gunes. 2017. Automatic Predic tion of Impres- sions in Time and across Varying Context: Personality, Attr activeness and Likeability. IEEE Transactions on Aﬀective Computing 8, 1 (Jan. 2017), 29–42. https://doi.org/10.1109/TAFFC.2015.2513401

work page doi:10.1109/taffc.2015.2513401 2017

[2] [2]

Leigh Clark, Phillip Doyle, Diego Garaialde, Emer Gilma rtin, Stephan Schlögl, Jens Edlund, Matthew Aylett, João Cabral, Cosmin Munteanu, and Benjamin Cowan. 2018. The State of Speech in HCI: Trends, Themes and Ch allenges. arXiv:1810.06828 [cs] (Oct. 2018). http://arxiv.org/abs/1810.06828 arXiv: 1810.06828

work page arXiv 2018

[3] [3]

Cowan, Na- dia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Ju stin Edwards, Brendan Spillane, Emer Gilmartin, and Christine Murad

Leigh Clark, Cosmin Munteanu, Vincent Wade, Benjamin R. Cowan, Na- dia Pantidi, Orla Cooney, Philip Doyle, Diego Garaialde, Ju stin Edwards, Brendan Spillane, Emer Gilmartin, and Christine Murad. 201 9. What Makes a Good Conversation?: Challenges in Designing Truly C onversa- tional Agents. In Proceedings of the 2019 CHI Conference on Human Factors in Com...

work page doi:10.1145/3290605.3300705 2019

[4] [4]

Benjamin R Cowan, Nadia Pantidi, David Coyle, Kellie Mor rissey, Peter Clarke, Sara Al-Shehri, David Earley, and Natasha Bandeira. 2017. W hat can i help you with?: infrequent users’ experiences of intelligent perso nal assistants. In Proceed- ings of the 19th International Conference on Human-Compute r Interaction with Mobile Devices and Services . ACM, 43

work page 2017

[5] [5]

Ewart J de Visser, Samuel S Monfort, Ryan McKendrick, Mel issa AB Smith, Patrick E McKnight, Frank Krueger, and Raja Parasuraman. 20 16. Almost hu- man: Anthropomorphism increases trust resilience in cogni tive agents. Journal of Experimental Psychology: Applied 22, 3 (2016), 331

work page 2016

[6] [6]

Florian N Egger. 2000. Trust me, I’m an online vendor: tow ards a model of trust for e-commerce system design. In CHI’00 extended abstracts on Human factors in computing systems. ACM, 101–102

work page 2000

[7] [7]

Flanagin and Miriam J

Andrew J. Flanagin and Miriam J. Metzger. 2007. The role o f site features, user attributes, and information veriﬁcation behaviors on the p erceived credibility of web-based information. New Media & Society 9, 2 (April 2007), 319–342. https://doi.org/10.1177/1461444807075015

work page doi:10.1177/1461444807075015 2007

[8] [8]

BJ Fogg and Hsiang Tseng. 1999. The elements of computer c redibility. In Pro- ceedings of the SIGCHI conference on Human Factors in Computing Systems. ACM, 80–87

work page 1999

[9] [9]

Amos Freedy, Ewart DeVisser, Gershon Weltman, and Nicol e Coeyman. 2007. Measurement of trust in human-robot collaboration. In 2007 International Sym- posium on Collaborative Technologies and Systems . IEEE, 106–114

work page 2007

[10] [10]

Kerstin Sophie Haring, David Silvera-Tawil, Yoshio Ma tsumoto, Mari Velonaki, and Katsumi Watanabe. 2014. Perception of an android robot i n Japan and Aus- tralia: A cross-cultural comparison. In International conference on social robotics . Springer, 166–175

work page 2014

[11] [11]

Kevin Anthony Hoﬀ and Masooda Bashir. 2015. Trust in aut omation: Integrating empirical evidence on factors that inﬂuence trust. Human Factors 57, 3 (2015), 407–434

work page 2015

[12] [12]

Oliver P John, Sanjay Srivastava, and others. 1999. The Big Five trait taxonomy: History, measurement, and theoretical perspectives. Handbook of personality: Theory and research 2, 1999 (1999), 102–138

work page 1999

[13] [13]

Spiro Kiousis. 2001. Public Trust or Mistrust? Percept ions of Media Credibility in the Information Age. Mass Communication and Society 4, 4 (Nov. 2001), 381–403. https://doi.org/10.1207/S15327825MCS0404_4

work page doi:10.1207/s15327825mcs0404_4 2001

[14] [14]

John D Lee and Katrina A See. 2004. Trust in automation: D esigning for appro- priate reliance. Human factors 46, 1 (2004), 50–80

work page 2004

[15] [15]

Jin Joo Lee, Brad Knox, Jolie Baumann, Cynthia Breazeal , and David DeSteno

work page

[16] [16]

Frontiers in psychology 4 (2013), 893

Computationally modeling interpersonal trust. Frontiers in psychology 4 (2013), 893

work page 2013

[17] [17]

Like Having a Reall y Bad PA

Ewa Luger and Abigail Sellen. 2016. "Like Having a Reall y Bad PA": The Gulf between User Expectation and Experience of Conversati onal Agents. In Proceedings of the 2016 CHI Conference on Human Factors in Com put- ing Systems - CHI ’16 . ACM Press, Santa Clara, California, USA, 5286–5297. https://doi.org/10.1145/2858036.2858288

work page doi:10.1145/2858036.2858288 2016

[18] [18]

D Harrison McKnight, Vivek Choudhury, and Charles Kacm ar. 2002. The impact of initial consumer trust on intentions to transact with a web site: a trust building model. The journal of strategic information systems 11, 3-4 (2002), 297–323. A Need for Trust in Conversational Interface Research CUI 20 19, August 22–23, 2019, Dublin, Ireland

work page 2002

[19] [19]

Panagiotis Mitkidis, John J McGraw, Andreas Roepstorﬀ , and Sebastian Wallot

work page

[20] [20]

Physiology & behavior 149 (2015), 101–106

Building trust: Heart rate synchrony and arousal duri ng joint action in- creased by public goods game. Physiology & behavior 149 (2015), 101–106

work page 2015

[21] [21]

Christie Olson and Kelli Kemery. 2019. 2019 Voice report: Consumer adoption of voice technology and digital assistants . Technical Report. Microsoft

work page 2019

[22] [22]

Jens Riegelsberger, M Angela Sasse, and John D McCarthy . 2003. Shiny happy people building trust?: photos on e-commerce websites and c onsumer trust. In Proceedings of the SIGCHI conference on Human factors in comp uting systems . ACM, 121–128

work page 2003

[23] [23]

Denise M Rousseau, Sim B Sitkin, Ronald S Burt, and Colin Camerer. 1998. Not so diﬀerent after all: A cross-discipline view of trust. Academy of management review 23, 3 (1998), 393–404

work page 1998

[24] [24]

Maha Salem, Gabriella Lakatos, Farshid Amirabdollahi an, and Kerstin Dauten- hahn. 2015. Would you trust a (faulty) robot?: Eﬀects of erro r, task type and personality on human-robot cooperation and trust. In Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot I nteraction. ACM, 141–148

work page 2015

[25] [25]

Scissors, Alastair J

Lauren E. Scissors, Alastair J. Gill, Kathleen Geraght y, and Darren Gergle. 2009. In CMC we trust: the role of similarity. In Proceedings of the 27th international conference on Human factors in computing systems - CHI 09 . ACM Press, Boston, MA, USA, 527. https://doi.org/10.1145/1518701.1518783

work page doi:10.1145/1518701.1518783 2009

[26] [26]

Elaine Short, Justin Hart, Michelle Vu, and Brian Scass ellati. 2010. No fair an interaction with a cheating robot. In 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI) . IEEE, 219–226

work page 2010

[27] [27]

Ilaria Torre, Leigh Clark, and Benjamin R Cowan. 2018. Measuring and designing trust in Human-Agent Interaction

work page 2018

[28] [28]

Ilaria Torre, Jeremy Goslin, Laurence White, and Debor a Zanatto. 2018. Trust in artiﬁcial voices: A congruency eﬀect of ﬁrst impressions an d behavioural expe- rience. In Proceedings of the Technology, Mind, and Society . ACM, 40

work page 2018

[29] [29]

Lin Wang, Pei-Luen Patrick Rau, Vanessa Evers, Benjamin Krisper Robinson, and Pamela Hinds. 2010. When in Rome: the role of culture & contex t in adherence to robot recommendations. In Proceedings of the 5th ACM/IEEE international con- ference on Human-robot interaction . IEEE Press, 359–366

work page 2010

[30] [30]

James E Young, Richard Hawkins, Ehud Sharlin, and Takeo Igarashi. 2009. To- ward acceptable domestic robots: Applying insights from so cial psychology. In- ternational Journal of Social Robotics 1, 1 (2009), 95

work page 2009