The natural point of departure for an editorial of a special issue on Human-Machine Communication (HMC) is the question of what HMC is. Addressing this question is not an easy task, as it is inherently linked to the more fundamental and general question of what communication is. The definition of communication, in turn, shapes the identity of an entire scholarly field and is thus subject to a vibrant and continuous debate. We do not aim at intensifying or complicating this debate but rather at providing an operational definition of HMC, which is merely supposed to serve as a framework for the special issue.

1 Definition of human-machine communication

In our view, communication can be regarded as the process of at least two entities “sharing” (Schramm 1954) something, suggesting an act of “bringing together” (Cobley 2008). These entities, in our context, are humans on the one hand, and machines or “digital interlocutors” (Edwards and Edwards 2017, p. 487) on the other hand. What they share is widely conceived of as messages or compilations of symbols. These messages are encoded, decoded and interpreted (cf. Schramm 1954) by both humans and machines. By exchanging and interpreting the messages, humans and machines engage in “meaning-making” (Guzman 2018, p. 17), establish “relationships” (Spence 2019, p. 285; Knorr Cetina 1997), or display social behavior (Reeves and Nass 1996). These processes can be conceptualized as merely unidirectional (from humans to machines) or bidirectional.

Any agency originating from machines may be partly observed intersubjectively (across observers) and partly attributed to them subjectively by the human communicators resulting in “hybrid constellations of inter-agency” (Rammert 2012, p. 15). For our operational definition, it is not relevant to address the philosophical questions if machines act intentionally, truly comprehend the meaning of the messages, or are completely equivalent to humans. We are also aware of the recent social theoretical development toward relational concepts of subjectivity and the related problematizations of humans’ intentionality, consciousness, and agency (Gentzel 2019). For our operational definition, however, it is sufficient that humans socially engage with machines (Geser 1989).

HMC is also embedded in different layers of social context, similar to mass media (Shoemaker and Reese 2014). Among them are a micro-level layer containing the social situation and immediate reality of the communicators (e.g., Suchman 2007; Etzrodt 2022), a meso-level layer where institutions and organizations are located (e.g., Carlson 2015), and a macro-level layer encompassing societal structures and systems (e.g., Howard 2015). HMC is influenced by these layers and influences them in complex interdependencies. For instance, the presence of additional communicators, professional routines, regulatory structures, or public discourse may affect HMC and vice versa. Against this backdrop, we define HMC as a process of message exchange between humans and machines, and its associated meaning-making, relationships, and social behavior, embedded in different layers of social context on micro level, meso level, and macro level (see Fig. 1).

Fig. 1
figure 1

Model of Human-Machine Communication

2 Relevance of human-machine communication

Why is it time for a special issue on HMC in Publizistik? On the one hand, there has been a proliferation of HMC as an object of investigation. Digital interlocutors, such as Artificial Intelligence (e.g., Gunkel 2020; Guzman and Lewis 2020; Schäfer and Wessler 2020; Sundar and Lee 2022), avatars (e.g., Banks and Bowman 2016), chatbots (e.g., Araujo 2018; Edwards et al. 2014; Brandtzaeg and Følstad 2017; Gehl and Bakardjieva 2017), voice-based assistants (e.g., Etzrodt and Engesser 2021; Humphry and Chesher 2021; Natale and Cooke 2021), and social robots (e.g., Hepp 2020; Fortunati 2018; Peter and Kühne 2018) are on the rise. As a result, we are witnessing a profound change, in which communication through technologies is extended by communication with technologies (cf. Guzman and Lewis 2020). Thus, the study of HMC is essential for socially and practically relevant communication and media studies—and it implies several theoretical, empirical, and methodological challenges, such as applying, changing, or reconceptualizing approaches.

On the other hand, the scholarly field of HMC has increasingly taken shape. Recent milestones have been Steve Jones’ programmatic piece in 2014, Andrea Guzman’s (2018) seminal anthology, the special section on the topic in Computers in Human Behavior edited by Patric R. Spence (2019), the establishment of the Human-Machine Communication journal under the auspices of Autumn Edwards, Chad Edwards, Leopoldina Fortunati, and Patric R. Spence, as well as the formation of the HMC interest group within the International Communication Association lead by founding chair, Andrea Guzman. HMC has started as an interdisciplinary field. It combines approaches from the social sciences, humanities, and engineering sciences. It also integrates other scholarly fields, such as Human-Robot Interaction (HRI), Human-Computer Interaction (HCI), and others (cf. Guzman 2018; Spence 2019). As a result, HMC is inherently inclusive and multifaceted. However, in order to be institutionally successful and to generate a cumulative epistemological gain, a framework of common research questions, theories, and methods needs to be further established. This is where this special issue comes into play.

3 Content of the special issue

With the call for this special issue, we attempted to map HMC research and to provide an overview of trends within this widely dispersed field. In addition to current research subjects, theories, findings, and methods in HMC, we were also looking for specific challenges and avenues for future research.

As a result, the special issue has gathered nine contributions: four theoretical papers (Hepp et al.; Dickel & Dogruel; Mooshammer; Edwards et al.), one methodological paper (Greussing et al.), and four empirical studies—two of them drawing on qualitative methods (van der Goot; Wassmer & Schwarzenegger) and two of them employing quantitative methods (Bastiansen et al.; Weidmüller et al.). None of the empirical studies explicitly focuses on the exchange of messages between humans and machines. Instead, Bastiansen et al., van der Goot et al., and Wassmer & Schwarzenegger place emphasis on meaning-making, while Weidmüller et al. bring relationships to machines (i.e. trustworthiness) to the fore.

Several authors suggest broadening the field: Some argue that HMC scholars should not only take the mere exchange of messages into account but also the social context (Hepp et al.; Dickel & Dogruel). Others reach out toward journalism studies (Mooshammer), computer-mediated communication (Weidmüller et al.), and interpersonal communication (Bastiansen et al.; Edwards et al.). The empirical studies, the methodological contribution, and one theoretical paper (Edwards et al.) focus on the micro level of HMC, Mooshammer addresses the meso level, and the papers of Hepp et al. and of Dickel & Dogruel deal with implications of HMC on the macro level.

Andreas Hepp, Wiebke Loosen, Stephan Dreyer, Juliane Jarke, Sigrid Kannengießer, Christian Katzenbach, Rainer Malaka, Michaela Pfadenhauer, Cornelius Puschmann, and Wolfgang Schulz take up the challenge of defining the research field of automated communication. The authors argue for complementing the analysis of direct interaction processes between humans and machines with the entire spectrum of social communication processes. By reconstructing the transformation of communication through the integration of “communicative AI”, the contribution sensitizes the readers to the breadth and depth of this transformation process at the micro, meso and macro level.

Sascha Dickel and Leyla Dogruel argue that communication with chatbots, voice assistants, and social robots is already part of our social reality. From that starting point, the essay proposes a conceptualization of HMC, which converges on a symmetric relationship between human and machine communicators. In developing their model, the authors adopt Knoblauch’s (2017) sociological approach of “Kommunikativierung”, which they trace back to three drivers: the decrease of human control over the communication process, the increase of the simulation of human mediation of meaning, and the discursive attribution of communication to machines.

Sandra Mooshammer introduces Rammert and Schulz-Schaeffer’s (2002) approach of gradualized action within socio-technical constellations to HMC by using the example of automated journalism. Thus, the author provides a conceptual framework for differentiating between levels of agency in the broader context of communication. By referring to automated journalism, the paper offers a theoretically grounded differentiation of human and machine agency and disentangles the use of the term “automation” in this regard.

Autumn Edwards, Andrew Gambino, and Chad Edwards address the question of whether concepts from theories on interpersonal relationships can be adapted to human-machine relationships. Using the example of attraction, the essay explores the peculiarities of the relationship between humans and machines compared to the human “gold standard”. They identify tensions in constructs such as aesthetics or personality, similarities concerning physical attraction, and a distinct peculiarity of machines in terms of being more available and visually appealing or configurable than humans. Based on these explorations the authors discuss further avenues for research.

Esther Greussing, Franziska Gaiser, Stefanie Helene Klein, Carolin Straßmann, Carolin Ischen, Sabrina Eimler, Katharina Frehmann, Miriam Gieselmann, Charlotte Knorr, Angelica Lermann Henestrosa, Andy Räder, and Sonja Utz summarize the challenges in the methodological conception of HMC research that focuses on the interactions between humans and machines. They differentiate between research on chatbots, smart speakers, and robots—machines that differ in modalities (written text vs. voice) and degree of embodiment. The paper focuses mainly on quantitative, in particular, experimental research because it is confronted with several challenges that do not apply to qualitative studies, such as the decision between using simulated or real interactions. The authors aim to provide a guideline for researchers by outlining the caveats of the various methods.

Margot van der Goot explores the concepts of source orientation, anthropomorphism, and social presence, which are crucial for understanding users’ entity perceptions when interacting with AI-enabled technologies such as text-based chatbots. By using a qualitative methodological approach, these concepts are explored and delineated from each other. The findings challenge CASA’s understanding that assumes source orientation to be a constant, the ascription of certain cues as exclusively human characteristics, and the labeling of mindful and mindless anthropomorphism. Furthermore, not all participants felt the presence of another entity, challenging the emergence of a communication situation in the first place.

Marlene Wassmer and Christian Schwarzenegger investigate the epistemological principles that guide users’ actions during their communication with smart speakers. Employing a qualitative methodological approach, they found that smart speakers challenge the classic media repertoire by being a peripheral part, primarily used for easy tasks but differing from other media due to their ambivalent role between object and subject, interlocutor and device, and a higher presence during use. Furthermore, the authors demonstrate that how the sensemaking of smart speakers translates into use is ambivalent and sometimes even contradictory.

Mathilde Bastiansen, Anne C. Kroon, and Theo Araujo demonstrate how the transfer of theoretical concepts and standard research methods from interpersonal interactions can inform empirical research in HMC by investigating the effect of gender stereotypes on interpersonal trust in text-based chatbots. The absence of significant effects in the study underlines how difficult the transfer of theoretical and methodological concepts from human contexts to HMC is. Furthermore, it raises the question of whether gendered chatbots are so different from humans that stereotyping does not manifest itself in a similar manner as it does in human-to-human interactions.

Finally, Lisa Weidmüller, Katrin Etzrodt, and Sven Engesser empirically investigate the trustworthiness of voice-based assistants. The authors analyze how influencing factors related to the interlocutor function and factors meaningful to the intermediary function of voice-based assistants contribute jointly to the prediction of trustworthiness. The findings demonstrate that considering both functions of the voice-based assistant—the communicator and the medium—provides a better understanding of trust in this new technology.

Although we were privileged to attract a wide range of contributions with our call, the field of HMC is even more diverse. We are aware that the special issue does not cover all of its aspects and bears some biases, one of the most evident concerning the nationalities of the authors who work in Austria, Germany, Hong Kong, the Netherlands, and the US.

4 Challenges and avenues for research

The contributions provide insight into the current challenges posed by the emergence and proliferation of digital interlocutors, which concerns our understanding of these machines and how we interact with them but also has implications for the discipline as a whole by contesting traditional conceptions of communication, being human, and social behavior.

Definitional and ontological

First of all, questions of how communication with machines can be defined, and whether or not this is communication in the first place arise. Closely related to these questions is the ontological definition of the machine as a communicator. Although communication and its neighboring disciplines have dealt with these questions extensively, fundamental problems remain to be unsolved. In particular, it is unclear if there is an ontological threshold where the machine changes from a tool/medium to a communicator and if so, how it can be determined. There are at least three ways to approach this threshold: (1) from a technological perspective through characteristics inherent to the machine, such as degrees of automation, (2) from a psychological perspective through perceptions of the human communicators, and (3) a sociological perspective through the definition of the situation and the social context. HMC is challenged to acknowledge and integrate these three perspectives.

This leads to another important question: How do changing HMC practices and HMC theories interact with general conceptions of subjectivity? This is not merely an academic gimmick because the social relevance of the social sciences is linked to their potential for critical analyses of observable processes and phenomena. Several traditional critical concepts (e.g., culture industry, alienation, acceleration) have been reserved for humans alone because those are regarded as the only sovereign subjects capable of reflecting on and critiquing social realities. More simply: Our understanding of what machines and subjects are and what HMC is should not be limited to observation and description but needs to (re)formulate standards to evaluate machines beyond observable success or failure.


There is a scarcity of theories in HMC. As a relatively new field, not many theories have had sufficient time to develop. Already existing theories are frequently derived from interpersonal communication or from computer-mediated communication. Even the Media Equation framework (Reeves and Nass 1996), one of the theoretical cornerstones of HMC, largely draws on findings from interpersonal communication. Such theories bear the challenge that it is frequently an open question if and how they can be transferred and generalized to HMC. Therefore, HMC should invest in developing and testing theories of its own that are specifically tailored to the communication with digital interlocutors.

From a theoretical and conceptual perspective, it is also vital to keep in mind and systematically reflect on a seemingly contradictory movement: While HMC scholars convincingly argue that human communication as the only “gold standard” for concept and model development can be dysfunctional, this “gold standard” seems to be exactly the point of orientation for design and further development of robots (e.g., child schema with a round face and big eyes to trigger psychological and emotional effects) or voice-based assistants (e.g., to perform small talk practices). Thus, a challenge for HMC will be to specify this tension and explore appropriate solutions.


Digital interlocutors as objects of research also pose major methodological challenges. First, maybe even more than in other areas of communication research, they imply a combination of qualitative and quantitative methods. On the one hand, they frequently offer large datasets for quantitative analyses. On the other hand, their novelty and complexity often call for qualitative methods. Second, the complexity of digital interlocutors, such as voice assistants or robots require even more technical and methodological skills from the communication scholars than digital media (Greussing et al.).

Integration of micro level, meso level, and macro level

A large proportion of empirical HMC research focuses on the micro level. This is also the case in our special issue, with papers examining chatbot cues (Bastiansen et al.; van der Goot) or the trustworthiness of smart speakers (Weidmüller et al.). The theoretical papers, in contrast, often take a meta perspective and also address the broader impacts of HMC on the societal level (Dickel & Dogruel; Hepp et al.). As our model shows, the implications of HMC also transcend to the meso and macro level. A challenge for future work is to take these effects into account in empirical studies and, ideally, integrate them through multi-level approaches.

Again, practical social and political challenges are evident. One example is the increasing spread of the Internet of Things in Smart Homes or Smart Cities. Digital interlocutors such as artificial companions, voice-based assistants, or robots and their AI-supported systems will increasingly become the interfaces for complex information, distribution and supply systems. For this reason, HMC should be equally accessible for all groups of actors (e.g., young and old, rich and poor, integrated or marginalized).

Interaction with neighboring fields of research

The proliferation of digital interlocutors in all areas of society also affects neighboring scholarly fields, such as journalism studies and computer-mediated communication. Another challenge for HMC is the interaction with these fields and to engage them in a meaningful exchange that generates common knowledge and demonstrates the contribution of HMC to these fields and vice versa.

Finally, the relevance of critical conceptions such as surveillance capitalism (Zuboff 2019), platform society (van Dijck et al. 2018) or data colonialism (Couldry and Mejias 2019) for HMC becomes apparent because the producers of, for example, voice-based assistants, such as Google Assistant, Apple’s Siri, or Amazon’s Alexa, are big technology companies which operate platforms and collect large quantities of data. Thus, it is plausible that the economic interests of these companies influence the design of digital interlocutors through the selection and prioritization of content, affordances, and communication opportunities. Thus, HMC needs to take these normative questions into account. A closer dialogue between the research fields may also focus on the “decoding process” of machines and automated communication, for example, the quantitative and qualitative expansion for data collection and processing.