Slacking with the Bot : Programmable Social Bot in Virtual Team Interaction

This is a self-archived version of an original article. This version may differ from the original in pagination and typographic details. Author(s): Title: Year: Version: Copyright: Rights: Rights url: Please cite the original version: CC BY 4.0 https://creativecommons.org/licenses/by/4.0/ Slacking with the Bot : Programmable Social Bot in Virtual Team Interaction © The Author(s) 2021. Published by Oxford University Press on behalf of International Communication Association. Published version Laitinen, Kaisa; Laaksonen, Salla-Maaria; Koivula, Minna Laitinen, K., Laaksonen, S.-M., & Koivula, M. (2021). Slacking with the Bot : Programmable Social Bot in Virtual Team Interaction. Journal of Computer-Mediated Communication, 26(6), 343-361. https://doi.org/10.1093/jcmc/zmab012 2021 Slacking with the Bot: Programmable Social Bot in Virtual Team Interaction Kaisa Laitinen 1 , Salla-Maaria Laaksonen 2 & Minna Koivula 1 1 Department of Language and Communication Studies, University of Jyva¨skyla¨, P.O. Box 35, FI-40014 Jyva¨skyla¨, Finland 2 Centre for Consumer Society Research, University of Helsinki, P.O. Box 24, FI-00014 Helsinki, Finland Nonhuman communicators are challenging the prevailing conceptualizations of technologymediated team communication. Slackbot is a social bot that can be configured to respond to trigger words and, thus, take part in discussions on the platform. A set of 84 bot-related communication episodes were identified from a journalistic team’s Slack messages (N ¼45,940) and analyzed utilizing both qualitative content analysis and interaction process analysis (IPA). This integrated mixed-methods analysis revealed novel insights into the micro-level dynamics of human–machine communication in organizational teams. In response to Slackbot’s greetings, acclamations, work-related messages, and relational messages, we identified how the team members respond to the bot, discuss it, and summon it to appear on the platform. Further, the IPA revealed that the bot-related communication episodes are shaped by the bot’s responses toward more socioemotional and personal functions. Findings suggest that a team-configured social bot can manifest and facilitate relational team communication. Lay Summary New communication technologies not only support but also take part in organizational team communication, challenging how we see the agency of these technologies. This paper examines Slackbot, a bot that “participates” in team discussions based on the use of triggering words that are configured by the team members. We used integrated mixed methods to study a set of Slackbot interactions with team members. Specifically, we examined how team members summon, interact with, and discuss the bot based on the bot’s greetings, acclamations, relational comments, and work-related messages. We found that Slackbot changes the nature of the team interaction. The analysis showed that when the bot participates in the discussion thread, it becomes more relational and less task focused. These findings suggest that a social bot can facilitate relational communication and provide assets that support organizational teamwork. Keywords: Social Bots, Organizational Communication, Virtual Teams, Slackbot, Team Communication, Qualitative Analysis, Interaction Process Analysis Corresponding author: Kaisa Laitinen; e-mail: kaisa.a.m.laitinen@jyu.fi Editorial Record: First manuscript received on 19 September 2020; Revisions received on 17 March 2021; Accepted by 1 June 2021; Final manuscript received on 8 June 2021 Journal of Computer-Mediated Communication 00 (2021) 1–19 V CThe Author(s) 2021. Published by Oxford University Press on behalf of International Communication Association. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. 1 Journal of Computer-Mediated Communication Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 doi:10.1093/jcmc/zmab012 Introduction Technology has undoubtedly become an inseparable part of communication in organizations. Teams that use some form of communication technology to do their work are nowadays an extremely common way of organizing (Gilson, Maynard, Young, Vartiainen, & Hakonen, 2015). While older accounts looking at the role of technology in organizations and social life have explored them as tools that are adapted (or are not), some streams of research literature draw a more complex picture of the relationship between technology and social action. For instance, the computers as social actors (CASAs) paradigm proposes that technology has social agency, which manifests in various forms of anthropomorphism and human–computer interaction (Nass, Steuer, & Tauber, 1994). Additionally, in organizational studies, the sociomateriality approach highlights how technologies co-construct organizational activities interconnected with social action (e.g., Leonardi & Barley, 2010;Orlikowski, 2007). The advancement of algorithm-based, intelligent, and automated technologies has set the stage for a reconceptualization of the role of technology in human communication (Guzman & Lewis, 2020;Jones, 2014). Subsequently, an increasing number of studies have emerged to examine how these intelligent systems not just host or enable communication, but rather take part in and shape it (e.g., Edwards, Spence, & Westerman, 2016;Jones, 2014). Different kinds of online collaborative software (OCS) are gaining popularity among work teams, not least in knowledge-intensive, creative fields such as journalism (Bunce, Wright, & Scott, 2017; Koivula, Villi, & Sivunen, 2020). These software not only facilitate collaboration among individuals but also introduce novel technological features in organizations. A rather recent form of communicative possibility offered by these platforms are social bots (or chatbots), small, automated programs that act in response to humans (e.g., Latzko-Toth, 2016). Social bots are defined as “automatic or semi-automatic computer programs that mimic humans and/or human behavior” (Wagner, Mitter, Korner, & Strohmaier, 2012, p. 41). They imitate a communicating human, at least in the sense that they similarly control an account in the OCS system and communicate in human language (Boshmaf, Muslukhov, Beznosov, & Ripeanu, 2011). Social bots and their implications have been studied rather extensively when they act on public social media or in customer service (e.g., Graham & Ackland, 2016;Grimme, Preuss, Adam, & Trautmann, 2017;Gorwa & Guilbeault, 2020). However, studies of bots in organizational communication contexts are only starting to emerge (Meske & Amojo, 2020;Stoeckli, Dremel, Uebernickel, & Brenner, 2020). Although there are some studies about bots and artificial intelligence (AI) facilitating connection and socialization among coworkers (Hancock, Naaman, & Levy, 2020;Meske & Amojo, 2020), so far the attention given to the more micro-level examination of team’s social interaction process with a social bot has been scarce. Additionally, cognitive responses and the perceptions of humanness as an indicator for engagement with a bot have been studied (Shin, 2021), but this, again, does not provide an understanding of the actual communication process with the bot. In order to better understand the meaning of bots and their role in organizational teams, there is a need for empirical studies in naturally occurring micro-level team settings. This kind of work is needed to both expand the emerging field of human–machine communication (HMC) (Guzman & Lewis, 2020) as well as to provide novel insights that could drive the reconceptualizations of computer-mediated communication perspectives (e.g., Flanagin, 2020 ) and small group research (e.g., Reiter-Palmon, Sinha, Gevers, Odobez, & Volpe, 2017). This study contributes to the emerging Social Bot in Virtual Team Interaction K. Laitinen et al. 2Journal of Computer-Mediated Communication 00 (2021) 1–19 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 scientific discussion by examining the role of a social bot, Slackbot in virtual team interaction taking place on a popular OCS, Slack. As a framework, we accept the HMC perspective by being open to the idea of viewing communication as a technology-inclusive process rather than human-specific (Guzman, 2018). The ability to participate in team communication can be seen as one of the main ways in which intelligent technologies can participate in teamwork and collaboration (Seeber et al., 2020). Additionally, we utilize various perspectives of small group communication research to develop the understanding of team interaction with and about a machine (e.g., Bales, 1950;Wittenbaum et al., 2004;Keyton & Beck, 2009). The aim of this study is to further understand the ways bots participate in team communication, how they are responded to by team members, and how team interaction processes are shaped by the bot’s presence. These insights are gained through an integrated mixed-methods stance (Paoletti, Bisbey, Zajac, Waller, & Salas, 2021) combining qualitative content analysis and interaction process analysis (IPA) to provide a structured understanding of the bot shaping the micro-level dynamics of team communication (Bales, 1950). We conclude by illustrating how our findings contribute to the understanding of bot-related team communication. Most importantly, we highlight both practical implications and the overall relevance of making social interaction a key entry point in the study of social bots in organizational teams. Communication in virtual teams Teams that use some form of communication technology to do their work are nowadays extremely common in organizations (Gilson et al., 2015). These virtual teams can be defined as task-focused groups of individuals that are often somewhat distributed and utilize technology to accomplish their goals (Lipnack and Stamps, 2000). One common form of communication technology in team use is OCS. OCS are used in work teams to facilitate various team processes across temporal and physical boundaries, as well as to allow team members to get to know each other thus providing a shared platform for the team to socialize on (e.g., Stoeckli et al., 2020). These kinds of platforms, including the context of this study Slack, have been increasingly utilized in the context of knowledge work. Slack has become such a household name in journalistic teams that these applications have been even referred to as “newsrooms in the cloud” (Bunce et al., 2017). Koivula et al. (2020) note that Slack allowed journalists to chip in on story ideas by sharing personal anecdotes or information whereas Bunce et al. (2017) found that Slack helped create team identity by allowing “banter” among team members. Technology-mediated collaboration in working life teams has been the focus of a vast set of literature, tackling matters such as trust, effectiveness, social presence, global teams, and team leadership (e.g., Ford, Piccolo, & Ford, 2017;Sedrine, Bouderbala, & Nasraoui, 2020;Sivunen & Nordba¨ck, 2015). As automated features, bots, and intelligent technologies become common parts of OCS, it is important to include these possibilities in studies focused on team communication. Not only because these new technologies have novel possibilities, but also because the implementation of social bots changes the perception of technology from merely as a mediator to being a non-human communicator in the collaborative system. This brings forth numerous conceptual and ontological dilemmas related to the inclusion of technology into human communication (see Guzman & Lewis, 2020). Team communication processes can be examined from various theoretical and methodological perspectives (Poole, Hollingshead, McGrath, Moreland, & Rohrbaugh, 2004). The functional perspective proposes that social interaction functions crucially affect issues such as decision-making K. Laitinen et al. Social Bot in Virtual Team Interaction Journal of Computer-Mediated Communication 00 (2021) 1–19 3 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 effectiveness and team productivity (e.g., Wittenbaum, 2004). Team communication can be seen to have task-related and relational aspects, both of which are present in the interaction between members. One popular methodological avenue for small group researchers to study the balance between different communicative functions is Bales’s (1950) IPA. IPA provides a 12-point taxonomy for systematic analysis of a team’s task-related and relational communication. The practical framework around the method proposes that task-focused team interaction should have both socioemotional responses and task-related responses, but with a slight emphasis on task-messages (Bales, 1950; Keyton, 2003;Pe~ na & Hancock, 2006). However, to accomplish the team’s relational goals, for example, safe communication climate, social support, and relational cohesion, it is important to study and highlight the importance of relational messages (Keyton & Beck, 2009). Although often criticized for its simplistic dichotomy of human interaction and the mutually exclusive categories (see e.g., McGrath, 1984), IPA provides a widely applied (Paoletti et al., 2021) and relatively clear methodology for examining the balance between task-related and relational team interaction. In this study, IPA is utilized to examine how a social bot shapes the functional balance of the team’s technology-mediated communication processes. Social bots as communicative team members Social bots can generally be seen as a part of the larger group of algorithm-based intelligent or semiintelligent technologies. It is crucial to understand these kinds of novel technologies because of the applications they might have not only as mediators of communication (see Hancock et al., 2020) but also as artificial companions and social actors (e.g., Nass et al., 1994;Hepp, 2020). The key definitive characteristics of social bots are connected to their function as nonhuman communicators and their way of mimicking human behavior (Wagner et al., 2012). They are human-like in the sense that they communicate in natural language and have their own account in the collaborative system (Boshmaf et al., 2011). Because these technologies are not only supporting communication between humans but also take part in social interaction, they lead researchers toward being open to viewing communication as a human–machine process in addition to the more traditional human-to-human perspective (Guzman & Lewis, 2020;Jones, 2014). This study focuses on the micro-level processes of organizational virtual teams utilizing a social bot. As previously reviewed, there is an extensive body of literature for social bots in the context of public social media and therapeutic use (e.g., de Gennaro, Krumhuber, & Lucas, 2020;Gorwa & Guilbeault, 2020;Grimme et al., 2017;Ho, Hancock, & Miner, 2018). However, in an organizational context, the studies are just starting to emerge. These studies have highlighted the role of bots as initiators of human social interaction (Meske & Amojo, 2020), and as facilitators of internal feedback processes (Lechler, Sto¨ckli, Rietsche, & Uebernickel, 2020). Through their social role, chatbots are considered to transform traditional enterprise information systems into systems that afford more social behavior common to enterprise social media platforms (Stoeckli et al., 2020). In the realm of teamwork, the previous research has mostly focused on various applications of AI and issues related to technology, collaborative processes, and institutional design (Seeber et al., 2020). Overall, the applications of AI are often viewed as tools and their value is seen in increasing team performance or optimizing organizational processes. For instance, AI has been predicted to shape processes such as decision-making, data processing, and management (Raisch & Krakowski, 2021;Shrestha, BenMenahem, & von Krogh, 2019). Additionally, Hancock et al. (2020) have set a research agenda for Social Bot in Virtual Team Interaction K. Laitinen et al. 4Journal of Computer-Mediated Communication 00 (2021) 1–19 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 studies focusing on intelligent technologies as mediators in various computer-mediated communication processes. The implications of different algorithm-based technologies are undoubtedly significant in terms of the effectiveness of organizational communication. Their participation in natural human communication also brings out social and relational possibilities attached to these technologies. This study aims to bring forth the micro-level dynamics of team communication with a bot. We focus on Slackbot participating in team discussions on Slack—provided that the team has programmed automated responses for the bot to use in the conversations. The current study aims to provide novel contributions (a) by studying actual, naturally occurred Slack communication, which is rarely present in previous empirical studies and (b) by examining the micro-level dynamics of human–machine team communication through the lens of IPA, thus providing information on how a social bot shapes the balance between task-related and relational interaction. We aim to answer the following three research questions: (RQ1:) What kind of responses does the social bot use to participate in team discussion? (RQ2:) What type of group interaction characterizes bot-related communication episodes? (RQ3:) How do the bot’s responses shape the team’s interaction during the bot-related episodes? Method Data collection and preprocessing Slack is an OCS that has gained increasing popularity during recent years. It is a multi-platform OCS that contains social-media-like features and facilitates both more formal collaborative processes, such as innovating, decision-making, and file sharing, as well as more relational aspects of teamwork (Koivula et al., 2020;Stoeckli et al., 2020). Slack works as an enterprise messenger, a chat tool for teams, and also has various automated features, including interfaces to other services and social bots. The Slackbot offers help for Slack users by reacting to certain keywords and by supporting a set of pre-programmed functionalities such as setting reminders. In addition, the platform allows its users to configure customized automated responses that are triggered by certain keywords. For example, the Slackbot can be configured to reply “Hello to you too” each time somebody says “hello.” The bot, therefore, takes part in the group discussion via its messages—albeit without a more sophisticated understanding of the contextual cues or other natural language processing capabilities. Data were collected from Slack conversations of a partially distributed journalistic team. The examined team consists of journalists working for a large Finnish media organization. Most of the time, the team consists of a producer, a graphic designer, and four journalists. However, the team memberships change dynamically during the lifespan of the team. In total, the studied thread includes messages from 18 different team members. Slack is not an official channel for the whole organization, but a shadow channel adopted by this particular team in August 2016. After the adoption of Slack, it quickly became an everyday communication channel for the team, a site where watercooler-type talk and work-related tasks (e.g., developing story ideas, finding interviewees, and sparring interview questions) merge in the #general channel which is open for all team members. Additionally, every journalist has their own channel for discussions on their story topics, ongoing projects, and feedback. First Slackbot’s responses were configured by the team’s producer. Over time, team members also contributed to the variety of trigger words and responses. Adding and deleting trigger words and bot responses took place organically. Responses were modified and deleted by members when it felt K. Laitinen et al. Social Bot in Virtual Team Interaction Journal of Computer-Mediated Communication 00 (2021) 1–19 5 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 necessary, for example, some of the profanities configured for the bot were removed when the #metoo movement broke out. Data collection was executed in collaboration with the studied team. Access to the Slack workspace was negotiated with the team’s supervisor and the third author spent four months (September– December 2018) on the platform with the journalists as a part of a larger research project that focuses on technology-aided innovation in newsrooms. The project included other forms of participatory observation and interviews with the team members. However, as that data have a significant focus on innovation in the newsroom rather than bots specifically, it is only used to contextualize the current study. Export of the team’s Slack workspace was provided to the researchers by the team lead after all team members had agreed with the research use. The data were pseudonymized before analysis. In reporting the data, pseudonyms are used to protect the participants’ privacy. The raw material for this study consists of all messages (N¼45,940) sent to the studied team’s #general channel. The data range over two years (August 2016–October 2018) and include altogether 2,425 messages sent by the Slackbot. In order to answer RQ2 and RQ3, we filtered the data to include only bot-related communication episodes. These episodes were identified following three criteria. First, messages mentioning the bot explicitly, coded by mentions of “bot” or “slackbot” and their abbreviations, as well as indirectly. Second, messages directly following the bot’s responses that answered or reacted to the prevailing bot message. Third, messages directly after the bot’s responses that manifested a change in topic triggered by the bot message in the discussion feed. The episodes consisted of the messages fitting these criteria and the bot messages embedded into the episode at hand. In order to include the whole episode, we also used the message topics and timestamps as cues to identify temporal and topical communication episodes, with an aim to include the entire conversation in each episode. The identification resulted in 84 bot-related communication episodes with 486 individual messages both from the Slackbot (n¼130) and human members (n¼356). The average length of an episode was 5.77 messages. As we were interested in examining the bot’s role in the team’s Slack interaction, each of these episodes was further coded into two parts: Part A (pre-bot) which included all messages sent in an episode before the bot’s initial response and part B (post-bot) which included interaction after the bot’s first message in a given episode. Data analysis The data analysis consisted of forms of qualitative and quantitative content analysis as well as an application of Bales’s (1950,1953) IPA. Hence, this study contributes to a recently promoted methodological avenue for studying small group communication, integrated mixed methods (Paoletti et al., 2021). This methodological approach suggests that “methods defined by an interconnected mix of quantitative and qualitative characteristics” (Paoletti et al., 2021, p. 1) are especially suitable for addressing some previous methodological shortcomings of group communication studies and providing rich and contextualized results. The methods highlighted in the integrated mixed-methods approach include, for instance, content analysis and interaction analysis, both of which are applied in the current study. Content analysis The content analysis was conducted in two distinctive phases in order to inductively seek answers for research questions RQ1 and RQ2 by (a) classifying the bot’s response types (RQ1) and (b) categorizing team members’ discussions with and about the bot (RQ2). Social Bot in Virtual Team Interaction K. Laitinen et al. 6Journal of Computer-Mediated Communication 00 (2021) 1–19 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 In the first phase (1) all Slackbot responses configured by the team members (79 different response types in total, 52 of which were available in the filtered data) were identified and qualitatively categorized into three response type categories (see Table 1). The categorizations were formed by examining both the content of the actual response as well as the trigger words activating the response. The classification was done by the first author but discussed and refined by all authors, thus following the peer-debriefing principles common for qualitative content analysis (e.g., Lincoln & Guba, 1985). Consequently, we examined the frequency of each type, thus gaining more quantitative information about the forms of bot’s participation in the team discussion. In the second (2) phase of the content analysis, we qualitatively coded the team members’ message functions in the bot-related communication episodes. This mainly data-driven analysis was conducted following the principles of phronetic iterative analysis (Tracy, 2018) that consisted of three rounds of coding. Following the principles of iterative examination, the coding was executed by both looking into the concepts emerging from the data itself, as well as sporadically going back to the research questions and existing literature to gain a framework for the analysis. The first round of qualitative coding was a round of data-based inductive open coding of the communication episodes regarding the bot, commenting on the bot, or impacted by the bot. This round was conducted by all authors on separate parts of the analyzed data. Second, a round of systematic coding of all individual messages that were part of the bot-related communication episodes was completed to bring out the second-level analytic codes. Third, the previous message-specific codes were compared and combined to reach the main result categories. Both second and third rounds of coding were conducted by the first author but regularly discussed among all authors to achieve credibility through peer-debriefing practices (Lincoln & Guba, 1985). Interaction process analysis In order to gain further understanding of the functions of the team’s Slack interaction and bot’s relation to team communication, we conducted an IPA for the bot-related communication episodes. IPA is designed to examine the interaction processes of a task-focused small group, such as a work team, and identify the task and relational (or socioemotional) elements of group communication (Bales, 1950,1953;Keyton, 2003) . Through IPA we were able to identify and quantify the group communication functions and changes in them. Thus, adding to the more explorative content analysis. The utilized IPA classification was drawn mostly from the original 12-point taxonomy (Bales, 1950), which includes four functional areas of group interaction, 1) positive socioemotional responses, 2) task responses: information sharing,3)task responses: questions, and 4) negative socioemotional responses (Keyton, 2003;Lo¨fstrand & Zakrisson, 2014). These broader areas are further divided into 12 mutually exclusive coding categories (Bales, 1950) which provide more specific operationalizations (see Table 2). As IPA stems from a long tradition of studying recorded face-to-face team discussions Table 1 Frequencies of Slackbot’s Response Types in the Empirical Data Response type Full data nFiltered data n Greetings and acclamations 1,033 56 Work-related messages 905 35 Relational messages 487 39 Total 2,425 130 K. Laitinen et al. Social Bot in Virtual Team Interaction Journal of Computer-Mediated Communication 00 (2021) 1–19 7 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 (Keyton, 2003), we made additions to the original taxonomy to better consider text-based computermediated interaction. Following Pe~ na and Hancock (2006) as well as (Rice and Love, 1987), we added five additional coding categories. First, we added two categories to make a distinction between sharing and asking for professional information versus sharing and asking for personal information (Pe~ na & Hancock, 2006;Rice & Love, 1987). These kinds of personal message categories have previously been connected with positive socioemotional interaction (Pe~ na & Hancock, 2006) and differ from information sharing that strictly connects to the team’s core task. Second, in order to be able to sufficiently code all messages in the subset, we added three categories related to the technological context (Pe~ na & Hancock, 2006): summons,greetings,and partings (i.e., notifying others of entering or leaving the platform), repairs (i.e., fixing an error in a previous message), as well as unclassifiable (i.e., system notifications and shared files). Table 2 IPA Coding Categories (applied from Bales, 1950;Keyton, 2003;Lo¨fstrand & Zakrisson, 2014;Pe~ na & Hancock, 2006) Functional area Coding category Example message Positive socioemotional Shows solidarity, seems friendly “Thank you Slackbot!” Shows tension release, dramatizes, jokes “Lol, that’s funny” Shows agreement, agrees “Yeah, I agree” Personal Shares personal information “I used to live in Chicago” Asks for personal information “Where are you going for holidays?” Task Gives suggestions relevant to the task “I could call Norman about this” Gives opinions relevant to the task “The photos look good” Gives information relevant to the task “This is due tomorrow” Asks for information relevant to the task “What is her last name?” Asks for opinions relevant to the task “What do you think?” Asks for suggestions relevant to the task “Should I tell them?” Negative socioemotional Shows disagreement, disagrees “That’s not right” Shows tension “This is so annoying!” Shows antagonism, seems unfriendly “Screw you!” Technical/other Summons, greetings and partings “Hello!” Repairs “Thnks” “I mean *thanks” Unclassifiable messages “User1234 has started a Google Meet” Social Bot in Virtual Team Interaction K. Laitinen et al. 8Journal of Computer-Mediated Communication 00 (2021) 1–19 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 personal discussions among the team members, and thus overall shapes the discussion toward more socioemotional and personal talk. The findings contribute to the understanding of a nonhuman communicator as a part of a team interaction process. There is some existing evidence that social bots can induce socializing among coworkers (Meske & Amojo, 2020). Additionally, our findings are in line with studies of therapy bots and other social bots designed for socioemotional needs, since they also seem to indicate that nonhuman communicators have relational and emotional impact (Ho et al., 2018;De Gennaro et al., 2020). However, the notion that the bot nudges the team towards socioemotional and personal talk contrasts the thinking that algorithm-based technologies should be utilized to optimize organizational processes and straightforwardly induce effectiveness (Shrestha et al., 2019). Interestingly, the rise in the relational functions is not visible only regarding positive socioemotional and personal interaction, but also regarding negative socioemotional talk, such as unfriendliness. The qualitative analysis revealed some explanations. When analyzing how the team members talk about and to the bot, we found multiple instances where the bot’s limited features sparked negative messages from the team members. In particular, the bot’s repetitive communication seems to emphasize its technological and configurable nature. Our findings provide a communication process perspective to the previous findings on how cognitive processing of perceived humanness can predict interaction with the bot in a journalistic setting (Shin, 2021). Repetitive responses and limited understanding of the social context could be issues that restrict perceptions of humanness regarding this bot. These issues should be considered when designing social bots to provide ideal support for relational team communication. In addition to the bot’s nature, the task-socioemotional dichotomy presented by Bales (1950) causes reason for critical evaluation. It has been understandably criticized over the years and these two forms of team interaction are often seen as inherently intertwined rather than as two ends of a spectrum (e.g., Dillard, 1997;Keyton & Beck, 2009). However, IPA is still one of the most used forms of quantitative interaction analysis for groups (Paoletti et al., 2021), and as such, provides a framework of observing team communication. In this study, an extended IPA method was utilized to build on top of the more exploratory qualitative methodology that was a necessary first step, as this kind of naturally occurring team data has not been previously examined with a focus on bot-related communication. Thus, this study adopted an integrated mixed-methods stance that has been recently recommended for small group studies (Paoletti et al., 2021). In dispersed journalistic teams, communication technologies have been reported to lend themselves specifically to creative work, such as sharing story ideas and developing working practices (Koivula et al., 2020;Bunce et al., 2017). Studies have found that this is much due to OCS platforms’ ability to facilitate lateral communication between team members. Previous studies highlight the importance of a supportive and open group culture for (creative) work. As the Slackbot, in this study, facilitates relational communication in the team and thus participates in building and enhancing group culture, it could be argued that the bot is, indeed, valuable for the team’s ability to conduct creative work. This notion provides a fruitful avenue for further studies, but some suggestions can already be made. Namely, utilizing the social aspects of the Slackbot (human-like responses) is recommended to generate relational benefits. However, the programmed responses should be carefully designed; relational messages seem to induce relational communication, but repetitive and context-blind responses might lead toward more negative socioemotional interaction. K. Laitinen et al. Social Bot in Virtual Team Interaction Journal of Computer-Mediated Communication 00 (2021) 1–19 15 Downloaded from https://academic.oup.com/jcmc/advance-article/doi/10.1093/jcmc/zmab012/6354842 by University of Jyvaskyla user on 02 September 2021 Funding This work has been funded by the Media Industry Research Foundation of Finland and The Academy of Finland (project no. 318416). Endnotes 1. All data excerpts have been translated into English and the names have been replaced with pseudonyms. References Bales, R. F. (1950). Interaction process analysis: A method for the study of small groups. Addison-Wesley. Bales, R. F. (1953). The equilibrium problem in small groups. In T. Parsons, R. F. Bales, & E. A. Shils (Eds.), Working papers in the theory of action (pp. 111–161). Free Press. Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2011). The socialbot network: when bots socialize for fame and money. 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