Human-Machine Communication Scholarship Trends: An Examination of Research From 2011 to 2021 in Communication Journals

Richards, Riley; Spence, Patric R.; Edwards, Chad

doi:10.30658/hmc.4.3

Cited by 10 publications

(2 citation statements)

References 66 publications

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“…We compare social and functional verbal robot communication styles since affective and logical persuasion strategies of robots have the highest potential (Saunderson and Nejat, 2019). However, a superiority of affective strategies seems to become apparent (Saunderson and Nejat, 2022), though communication between humans and machines is context-dependent (Richards et al, 2022;Cox and Ooi, 2022;Becker et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Increased morality through social communication or decision situation worsens the acceptance of robo-advisors

Arlinghaus,

Strassmann,

Dix

2024

Preprint

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This German study (N = 317) tests social communication (i.e., self-disclosure, content intimacy, relational continuity units, we-phrases) as a potential compensation strategy for algorithm aversion. Therefore, we explore the acceptance of a robot as an advisor in non-moral, somewhat moral, and very moral decision situations and compare the influence of two verbal communication styles of the robot (functional vs. social). Subjects followed the robot’s recommendation similarly often for both communication styles (functional vs. social), but more often in the non-moral decision situation than in the moral decision situations. Subjects perceived the robot as more human and more moral during social communication than during functional communication but similarly trustworthy, likable, and intelligent for both communication styles. In moral decision situations, subjects ascribed more anthropomorphism and morality but less trust, likability, and intelligence to the robot compared to the non-moral decision situation. Subjects perceive the robot as more moral in social communication. This unexpectedly led to subjects following the robot’s recommendation less often. No other mediation effects were found. From this we conclude, that the verbal communication style alone has a rather small influence on the robot’s acceptance as an advisor for moral decision-making and does not reduce algorithm aversion. Potential reasons for this (e.g., multimodality, no visual changes), as well as implications (e.g., avoidance of self-disclosure in human-robot interaction) and limitations (e.g., video interaction) of this study, are discussed.

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Section: Introductionmentioning

confidence: 99%

Increased morality through social communication or decision situation worsens the acceptance of robo-advisors

Arlinghaus,

Strassmann,

Dix

2024

Preprint

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“…Presently, scholars continue to elucidate the conceptual shifts provoked by artificial intelligence across all facets of human-machine communication (Fortunati, Edwards, 2021;Richards et al, 2022).…”

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confidence: 99%

Artificial journalism: the reverse of human-machine communication paradigm. Mapping the field of AI critical media studies

Shilina,

Volkova,

Bombin

et al. 2023

Vestn. Ross. univ. družby nar., Ser. Literaturoved. žurnal.

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The study for the first time endeavours to elucidate the distinct conceptual nuances of AI-driven journalism, exploring how it reshapes the core technological and communicative attributes of the field while influencing societal dynamics. The crisis within AI-driven human-machine interaction in journalism rooted in the essence and processing of information is defined. Despite the paradigm of journalism is rooted in a human-centered approach, its AI-driven paradigm is the same - but in a reversible mode. Journalism involves the translation of personal perspectives and experiences through the filter of memory. Algorithms function without the nuances of personal and social memory, thereby undermining the core principles of the journalistic profession. The loss of genuine, “analog” memory among journalists and their audiences, alongside the digital “memory” of algorithms, jeopardizes the fundamental societal role of journalism-upholding social order. Re-thinking the AI phenomenon as artificial communication, the authors propose the term “artificial journalism”. At the basic technological level it is based on various forms of automation and embedded within digital infrastructures; at the societal level it is designed for the central purpose of journalism and entangled with human practices. Both the levels are reversible. The term could serve as an umbrella term for all the AI-driven journalism activities. Also it removes contradictions not only in human-machine communication but clarify the essence of AI performance in journalism and media studies, and for the users. The emergence of AI-driven media practices opens the basic crisis conceptual contradictions which provokes new realms of research and necessitates the establishment of critical AI media studies.

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Researching interactions between humans and machines: methodological challenges

et al. 2022

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Communication scholars are increasingly concerned with interactions between humans and communicative agents. These agents, however, are considerably different from digital or social media: They are designed and perceived as life-like communication partners (i.e., as “communicative subjects”), which in turn poses distinct challenges for their empirical study. Hence, in this paper, we document, discuss, and evaluate potentials and pitfalls that typically arise for communication scholars when investigating simulated or non-simulated interactions between humans and chatbots, voice assistants, or social robots. In this paper, we focus on experiments (including pre-recorded stimuli, vignettes and the “Wizard of Oz”-technique) and field studies. Overall, this paper aims to provide guidance and support for communication scholars who want to empirically study human-machine communication. To this end, we not only compile potential challenges, but also recommend specific strategies and approaches. In addition, our reflections on current methodological challenges serve as a starting point for discussions in communication science on how meaning-making between humans and machines can be investigated in the best way possible, as illustrated in the concluding section.

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Human-Machine Communication Scholarship Trends: An Examination of Research From 2011 to 2021 in Communication Journals

Cited by 10 publications

References 66 publications

Increased morality through social communication or decision situation worsens the acceptance of robo-advisors

Increased morality through social communication or decision situation worsens the acceptance of robo-advisors

Artificial journalism: the reverse of human-machine communication paradigm. Mapping the field of AI critical media studies

Researching interactions between humans and machines: methodological challenges

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