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Odile Dumbleton

dblp:245/9121 · DBLP profile ↗
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3ranked-venue papers
0as first author
2since 2021 · last 2025
0000-0003-3857-5099ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 3 · 2 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 70% Interaction techniques and input · 30%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Usability and user experience research › psychometric evaluation
questionnaire validation
0.912025
The Partner Modelling Questionnaire: A Validated Self-Report Measure of Perceptions toward Machines as Dialogue Partners · ACM Trans. Comput. Hum. Interact. 2025
Usability and user experience research › evaluation methodology
self-report measures
0.912025
The Partner Modelling Questionnaire: A Validated Self-Report Measure of Perceptions toward Machines as Dialogue Partners · ACM Trans. Comput. Hum. Interact. 2025
Interaction techniques and input
voice interaction
0.912025
The Partner Modelling Questionnaire: A Validated Self-Report Measure of Perceptions toward Machines as Dialogue Partners · ACM Trans. Comput. Hum. Interact. 2025
Usability and user experience research
user perception
0.312025
The Partner Modelling Questionnaire: A Validated Self-Report Measure of Perceptions toward Machines as Dialogue Partners · ACM Trans. Comput. Hum. Interact. 2025

Methods — techniques the papers use, named apart from their topics

test-retest reliability · 0.9confirmatory factor analysis · 0.9
YearPublicationVenuePosition
2025 The Partner Modelling Questionnaire: A Validated Self-Report Measure of Perceptions toward Machines as Dialogue Partners
abstract
Recent work has looked to understand user perceptions of speech agent capabilities as dialogue partners (termed partner models), and how this affects user interaction. Yet, partner model effects are currently inferred from language production as no metrics are available to quantify these subjective perceptions more directly. Through three phases of work, we develop and validate the Partner Modelling Questionnaire (PMQ): an 18-item self-report semantic differential scale designed to reliably measure people’s partner models of non-embodied speech interfaces. Through confirmatory factor analysis, we confirm that the PMQ scale consists of three factors: communicative competence and dependability, human-likeness in communication and communicative flexibility. Our studies show that the measure consistently demonstrates good internal reliability, strong test-retest reliability over 4- and 12-week intervals, and predictable convergent/divergent validity. Based on our findings, we discuss the multidimensional nature of partner models, while identifying key future research avenues that the development of the PMQ facilitates. Notably, this includes the need to identify the activation, sensitivity, and dynamism of partner models in speech interface interaction.
Philip R. Doyle, Iona Gessinger, Justin Edwards, Leigh Clark, Odile Dumbleton, Diego Garaialde, Daniel J. Rough, Anna Bleakley, Holly P. Branigan, Benjamin R. Cowan
ACM Trans. Comput. Hum. Interact.5
2022 Bridging social distance during social distancing: exploring social talk and remote collegiality in video conferencing
abstract
Casual conversation, where people engage in social talk or phatic communion (Coupland et al., 1992) (i.e., non-task oriented talk) is acknowledged as important in facilitating collaboration among c...
Anna Bleakley, Daniel J. Rough, Justin Edwards, Philip R. Doyle, Odile Dumbleton, Leigh Clark, Sean Rintel, Vincent P. Wade, Benjamin R. Cowan
Hum. Comput. Interact.5
2019 Mapping Perceptions of Humanness in Intelligent Personal Assistant Interaction
abstract
Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.
Philip R. Doyle, Justin Edwards, Odile Dumbleton, Leigh Clark, Benjamin R. Cowan
MobileHCI3