一种新的评估社交机器人态度的量表

IF 0.9 4区心理学 Q3 COMMUNICATION

Interaction Studies Pub Date : 2020-01-24 DOI:10.1075/is.18055.fle

M. Damholdt, C. Vestergaard, M. Nørskov, Raul Hakli, Stefan Larsen, J. Seibt

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引用次数: 13

摘要

摘要背景：社交机器人的发展激增，导致对评估对机器人态度的系统方法的需求增加。目的：本研究提出了一种评估对社交机器人态度的问卷：ASOR。方法：由来自不同学科的成员组成的工作组编制了37项ASOR问卷。它建立在对社交机器人如何影响关系的五个不同方面的理论考虑之上。结果：三百三十九人对调查做出了回应。ASOR的因素分析得出了一个由25个项目组成的三因素解决方案：“心理能力的归属”、“社会实践能力的归属》和“社会道德地位的归属”。该数据与访谈数据进行了进一步的三角测量（n=10）。结论：ASOR允许评估社交机器人能力归属的三个不同方面，并提供了一种新型的对社交机器人态度的评估。ASOR似乎不仅评估了社交机器人的能力归属，还衡量了对社交机器人的总体积极态度。

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Towards a new scale for assessing attitudes towards social robots

Abstract Background: The surge in the development of social robots gives rise to an increased need for systematic methods of assessing attitudes towards robots. Aim: This study presents the development of a questionnaire for assessing attitudinal stance towards social robots: the ASOR. Methods: The 37-item ASOR questionnaire was developed by a task-force with members from different disciplines. It was founded on theoretical considerations of how social robots could influence five different aspects of relatedness. Results: Three hundred thirty-nine people responded to the survey. Factor analysis of the ASOR yielded a three-factor solution consisting of a total of 25 items: “ascription of mental capacities”, “ascription of socio-practical capacities”, and “ascription of socio-moral status”. This data was further triangulated with data from interviews (n = 10). Conclusion: the ASOR allows for assessment of three distinct facets of ascription of capacities to social robots and offers a new type of assessment of attitudes towards social robots. It appeared that ASOR not only assesses ascription of capacities to social robots but it also gauged overall positive attitudes towards social robots.

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来源期刊

Interaction Studies Multiple-

CiteScore

3.30

自引率

6.70%

发文量

期刊介绍： This international peer-reviewed journal aims to advance knowledge in the growing and strongly interdisciplinary area of Interaction Studies in biological and artificial systems. Understanding social behaviour and communication in biological and artificial systems requires knowledge of evolutionary, developmental and neurobiological aspects of social behaviour and communication; the embodied nature of interactions; origins and characteristics of social and narrative intelligence; perception, action and communication in the context of dynamic and social environments; social learning.