Cracks in the Foundation: How Relational Communication Dynamics Predict Performance Improvement in Cross‐Functional Teams

Schweitzer, VeraM. et al. Article — Published Version Cracks in the Foundation: How Relational Communication Dynamics Predict Performance Improvement in Cross‐ Functional Teams Journal of Supply Chain Management Provided in Cooperation with: John Wiley & Sons Suggested Citation: Schweitzer, VeraM. et al. (2025) : Cracks in the Foundation: How Relational Communication Dynamics Predict Performance Improvement in Cross‐Functional Teams, Journal of Supply Chain Management, ISSN 1745-493X, Wiley, Hoboken, NJ, Vol. 61, Iss. 3, pp. 36-54, https://doi.org/10.1111/jscm.12341 This Version is available at: https://hdl.handle.net/10419/329819 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Journal of Supply Chain Management, 2025; 61:36–54 https://doi.org/10.1111/jscm.12341 36 Journal of Supply Chain Management ORIGINAL ARTICLE OPEN ACCESS Cracks in the Foundation: How Relational Communication Dynamics Predict Performance Improvement in CrossFunctional Teams VeraM.Schweitzer1 | FabiolaH.Gerpott2 | NaleLehmannWillenbrock3 | Sanderde Leeuw4,5 | MichaélaSchippers6 | JiachunLu2 1Corporate Development, University of Cologne, Cologne, Germany | 2WHU – Otto Beisheim School of Management, Vallendar / Düsseldorf, Germany | 3Industrial and Organizational Psychology, University of Hamburg, Hamburg, Germany | 4Operations Research and Logistics, Wageningen University, Wageningen, The Netherlands | 5Nottingham Business School, Nottingham Trent University, Nottingham, UK | 6Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands Correspondence: Jiachun Lu ([email protected]) Received: 27 December 2023 | Revised: 15 January 2025 | Accepted: 29 January 2025 Funding: This work was supported by Deutsche Forschungsgemeinschaft Project #3004/21. Keywords: behavioral supply chain management| communication pattern| crossfunctional teams| interaction coding| lag sequential analysis| latent growth modeling ABSTRACT Crossfunctional teams are vital decisionmaking units in supply chain management, and scholars emphasize the need to understand how team processes shape performance improvement. Despite promising research on communication within crossfunctional teams, scant attention has been paid to realtime communication patterns—integral to behavioral supply chain management—which are fundamental to team processes in practice. This article posits, drawing on interaction ritual theory, that early communication patterns are correlated with the performance trajectories of crossfunctional teams, suggesting a potential influence. The authors tested this idea in a complex supply chain management simulation featuring crossfunctional teams. They employed a novel coding approach to capture temporal interactions, which yielded 25,641 coded verbal behaviors from crossfunctional team meeting interactions. To identify systematic communication patterns, lag sequential analysis was performed on this corpus of data. The results show that the frequency of relational communication was associated with weaker performance improvement in crossfunctional teams across six simulation iterations. Even more interestingly, when relational communication was frequently followed by taskoriented communication, no association with team performance improvement was observed. Further, crossfunctional teams in which relational communication was more frequently followed by counterproductive communication showed notably weaker performance improvements. Focusing on interactional flow within team dynamics, this research challenges the common belief regarding the value of broadly evaluating crossfunctional teams. As such, it advocates for adopting both a behavioral and a temporal lens to uncover how crossfunctional teams can prevent detrimental interactions in their daily operations. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). Journal of Supply Chain Management published by Wiley Periodicals LLC. Jiachun Lu was added as a coauthor in the process of developing this article, in recognition of her expertise in supply chain management and her significant contributions to the writing. She played a key role in rewriting the manuscript, revising it in response to feedback, and conducting qualitative followup interviews to contextualize and situate our quantitative findings. Michaéla Schippers is included as a coauthor in recognition of her contribution to the initial conceptualization of the study. She was not actively involved in the data collection, analysis, or writing of this manuscript. 37 1 | Introduction Crossfunctional teams—groups drawn from different departments—are crucial in supply chain operations, yet they often face challenges that undermine their effectiveness. The diverse knowledge base of crossfunctional team members helps navigate challenging situations, such as supply chain disruptions or supplier selection (Kaufmann, Wagner, and Carter 2017; van den Adel, de Vries, and van Donk2022). At the same time, crossfunctional teams often face major challenges due to different perspectives and perceived dissimilarities among team members (Lonsdale, Sanderson, and Esfahbodi 2024). These challenges can hinder crossfunctional integration and organizational performance (Mehta and Mehta2018). Recognizing the critical role of team processes and dynamics, scholars have begun examining how to leverage these mechanisms to achieve effective teamwork despite the challenges. Their work provides valuable insights that showcase the importance of centralized decisionmaking, team effort, team conflict, and knowledge creation (Arumugam, Antony, and Linderman 2016; de Vries etal.2022; Franke, Eckerd, and Foerstl2022). A few studies have also investigated the role of direct interactions among team members, particularly focusing on communication (Malhotra, Ahire, and Shang2017). Two noteworthy observations arise from the existing research on communication in crossfunctional teams. First, team processes are shaped by dynamic communication patterns (LehmannWillenbrock 2025). However, emerging supply chain studies tend to reduce communication in crossfunctional teams to generic, broad evaluations. Many studies have suggested that communication is deemed effective when it is positive, open, active, and frequent but detrimental when it is negative, siloed, passive, and infrequent (Bruccoleri, Riccobono, and Größler2019; Driedonks, Gevers, and Weele2010; Malhotra, Ahire, and Shang 2017; Mehta and Mehta 2018; Montoya, Massey, and Lockwood2011). This article challenges this consensus, arguing that such broad evaluations may oversimplify the complexities of communication by overlooking the interactional and temporal contexts in which it unfolds. To provide a more nuanced understanding of communication effectiveness (or lack thereof) in crossfunctional teams, this article examines communication patterns—that is, the sequences of verbal exchanges between team members in real time (LehmannWillenbrock2025). Second, the discourse on crossfunctional teams mainly focuses on formal, taskoriented aspects of teamwork (e.g., Arumugam, Antony, and Kumar2013; de Vries etal.2022), which may obscure the role of relational dynamics in team performance (e.g., Gifford etal.2022; Kaufmann, Wagner, and Carter2017). To account for such relational dynamics in teams and follow the call for more attention to behavioral interaction phenomena, this article examines relational communication patterns. Specifically, it asks two main questions: (1) Which relational communication patterns manifest in crossfunctional teams? and (2) How do these patterns influence team performance improvement? To answer these questions, this article draws on interaction ritual theory (Collins 2005) and incorporates insights from team and communication research (e.g., Bales1950; van Dun and Wilderom 2021). The central argument presented is that specific relational communication patterns (defined as how one team member reacts to another team member's relational communication, signaling one form of interaction ritual) are critical in early crossfunctional team meetings. These patterns set the foundation for team performance improvement, defined as a positive change in team performance over time (van Iddekinge etal.2009). In line with core team learning principles, we anticipate that all teams will improve (Marks, Mathieu, and Zaccaro2001), but our focus is on why some improve more than others. A stronger improvement in performance over time reflects a positive team outcome, whereas a weaker improvement is seen as detrimental, as it indicates falling below the improvement baseline and lagging behind other teams. Following the literature, relational communication is differentiated from taskoriented communication and counterproductive communication (e.g., Bales 1950; van Dun and Wilderom 2021). Accordingly, we investigate how relational statements followed by either taskoriented or counterproductive responses (see Figure1) influence team outcomes. Methodologically, this article investigates communication patterns in 32 crossfunctional teams participating in a complex supply chain management simulation (i.e., The Fresh Connection [TFC]; see de Vries et al.2022; van den Adel, de Vries, and van Donk2022). Using a novel approach to interaction coding, this article assigns specific communication codes to each verbal sense unit, accounting for the entire stream of team interactions. It assesses the communication patterns of 130 individuals during their first and last team meetings in the simulation, totaling 42 h and 28 min of recorded meetings and 25,641 coded behaviors. The quantitative analyses were complemented with ad hoc qualitative insights. This article makes three main theoretical contributions. First, it challenges the prevailing conceptualization of team processes and dynamics in the supply chain literature by adopting a microlevel focus on interaction rituals, particularly communication patterns. This approach moves beyond the broad evaluations of communication emphasized in prior studies. In doing so, this article also responds to recent calls for research on the impact of team communication on performance improvement (e.g., van Dun and Wilderom2021). Second, this article contributes to the ongoing debate about the interplay of relational and taskfocused processes in crossfunctional teams (Bruccoleri, Riccobono, and Größler 2019; Lonsdale, Sanderson, and Esfahbodi2024). By highlighting the importance of relational communication, it extends prior research that predominantly focuses on taskoriented factors, offering a complementary understanding of effective crossfunctional teams (e.g., Lonsdale, Sanderson, and Esfahbodi2024; van Dun and Wilderom2021). Third, by introducing interaction ritual theory alongside interaction coding and pattern analysis methodology to the supply chain management field, this article offers a novel approach to understanding crossfunctional teams. Specifically, by demonstrating the crucial role of communication patterns, it encourages future research to enrich theoretical and methodological perspectives on crossfunctional team processes and dynamics and their impact on team performance improvement. In the next section, we outline the theoretical foundations of our study and develop our hypotheses. 38 Journal of Supply Chain Management, 2025 2 | Literature and Hypotheses 2.1 | Team Processes and Dynamics in CrossFunctional Teams As crossfunctional teams are widely used in organizations to manage supply chains, it has become crucial to understand what differentiates effective teams from less effective ones (Driedonks, Gevers, and Weele2010). Research on crossfunctional teams, which are interdepartmental groups jointly managing supply chain decisions, has grown since the early 2000s (e.g., Carter etal.2024; de Vries etal.2022; Lu, Kaufmann, and Carter2021; Wu, Loch, and Ahmad2011). A closer review reveals two important observations. First, most articles investigate specific characteristics of crossfunctional teams (i.e., members' knowledge, skills, abilities, personality, and demographic characteristics, Mathieu etal.2017) or emergent states in crossfunctional teams (i.e., dynamic cognitive, affective, and motivational states such as cohesion and goal alignment, Rapp etal.2021). For example, Kaufmann and Wagner(2017) demonstrate that specific team attributes—such as affective diversity and emotional intelligence—predict cohesion, which in turn enhances performance. However, the dominant focus on team characteristics and emergent states cannot speak to more dynamic and fluid team processes and dynamics (i.e., team members' interdependent acts reflected in cognitive, verbal, and behavioral activities such as decisionmaking and communication (Mathieu etal.2017). These processes and dynamics are essential for crossfunctional team success because they determine how effectively a diverse knowledge base is translated into team performance (e.g., Malhotra, Ahire, and Shang2017). This pinpoints a critical need for novel theoretical approaches and empirical insights. Taking steps toward understanding team processes and dynamics, supply chain scholars have started paying attention to communication within teams (Driedonks, Gevers, and Weele2010; Malhotra, Ahire, and Shang 2017; Montoya, Massey, and Lockwood 2011; Sanderson, Esfahbodi, and Lonsdale 2022). While providing valuable insights, prior studies typically focus on the broad and generic characteristics of communication rather than capturing the social interactions that define team processes and dynamics (LehmannWillenbrock2025). For instance, while Driedonks, Gevers, and Weele(2010) highlight the importance of communication frequency and quality for crossfunctional team effectiveness, their surveybased measures are not meant to capture the temporal and interactional complexity of communication. Moving beyond surveybased methods, Montoya, Massey, and Lockwood(2011) examine the frequency of team communication as the number of messages sent in a virtual reality simulation in which members had to collaborate on a joint task. However, this approach does not include the specific content and context of team interactions. More recently, Sanderson, Esfahbodi, and Lonsdale(2022) show that a decentralized, open, and informal communication style is beneficial for crossfunctional teamwork effectiveness. This stream of work underscores the critical role of communication in crossfunctional teams. The present article extends this by conceptualizing team communication as an interactional, contextual, and dynamic phenomenon (LehmannWillenbrock2025). A second observation from the review of research on crossfunctional teams is that most studies prioritize taskoriented processes (e.g., decisionmaking, knowledge creation) over relational phenomena (e.g., providing support, sharing feelings). This emphasis is surprising given that both hard skills (i.e., taskoriented skills, such as business process mapping) and soft skills (i.e., relational skills, such as communication) in teams are essential for operational performance (Bruccoleri, Riccobono, and Größler 2019). Lonsdale, Sanderson, and Esfahbodi(2024) similarly highlight the importance of balancing taskwork (e.g., a sourcing strategy) and teamwork (e.g., communication) to ensure crossfunctional team effectiveness. Thus, while scholars increasingly recognize that the success of crossfunctional teams relies on both taskoriented and FIGURE 1 | Research model. [Colour figure can be viewed at wileyonlinelibrary.com] 39 relational factors, the research landscape pays much greater attention to the task domain. This imbalance is reflected in the calls for more research on the oftenoverlooked relational side of supply chain management (Avgerinos and Gokpinar2017; Gifford etal.2022; Jacobs, Yu, and Chavez2016). Thus, while a broad consensus exists on the importance of communication and relational dynamics for performance in crossfunctional teams, the present article challenges conventional conceptualization by focusing on relational communication patterns as central to understanding the core processes and dynamics within crossfunctional teams. 2.2 | Communication Patterns in CrossFunctional Teams Interaction ritual theory provides a comprehensive theoretical lens through which to examine team processes and dynamics by explaining social phenomena through the structure of concrete situations (Collins 2005; Goffman 1967; Krishnan etal.2021). Specifically, it posits that interaction rituals (i.e., patterns of interpersonal behaviors such as communication) are essential to the development and effectiveness of social groups, as they promote shared emotional experiences and a joint attentional focus among individuals (Collins2005; Wang etal.2023). While originating in sociology, interaction ritual theory has recently gained traction in general management and organizational behavior research (Krishnan etal.2021; LehmannWillenbrock etal.2017; Metiu and Rothbard2013; Wang etal.2023). For example, Metiu and Rothbard(2013) apply it to investigate team engagement in software development teams, highlighting the role of frequent, informal interactions in fostering engagement and driving problemsolving breakthroughs. Given its core propositions and applications, interaction ritual theory helps explain how communication patterns within a crossfunctional team (i.e., how one team member reacts to another member's statement, which signals one form of interaction ritual) form the basis of team performance improvement. Interaction ritual theory not only offers a solid theoretical basis for examining ritualized interactions, such as communication patterns within teams, but it also provides a solution to a common challenge present in prior studies on team communication. Specifically, the literature seems to focus primarily on the absolute frequencies of specific types of communication. This approach overlooks how communication is embedded in the interactional flow of a team (i.e., how other team members respond to a communicative act). One notable exception is Bennett et al. (2008), who find that, while communication frequency does not account for differences in team effectiveness, specific patterns of information seeking and sharing reveal how certain teams stabilize their performance. In another study, Kauffeld and LehmannWillenbrock (2012) show that more relational communication in teams is associated with lower team satisfaction. Subsequent research suggests that this counterintuitive finding may arise from an exclusive focus on communication frequencies, which overlooks the interactional context (i.e., communication patterns), thereby leading to an incomplete or potentially misleading interpretation (LehmannWillenbrock and Allen2018). This article extrapolates from these research streams to propose that translating the terminology and rationale of interaction ritual theory to supply chain management provides a useful theoretical foundation for identifying relevant communication patterns that influence crossfunctional team performance (Hoogeboom and Wilderom2020). In addition, as interaction ritual theory itself remains silent on the content of successful versus unsuccessful communication patterns, or what are termed team rituals, this article complements this theory by drawing on fundamental insights from communication research. Before discussing communication patterns in crossfunctional teams, the basic components of these patterns should be outlined. Based on the fundamentals of human interaction (Bales1950), the supply chain literature typically distinguishes between two overarching functions of communication in teams: (i) collaborating on and coordinating team tasks and (ii) maintaining relationships within the team. For example, Pagell etal.(2015) note that relational coordination in teams, which is deemed crucial for achieving operational success, depends on both formal taskoriented structures and interpersonal relationships. Similarly, Bruccoleri, Riccobono, and Größler(2019) argue that an effective and efficient team requires members to demonstrate both technical and soft skills. More recently, Lonsdale, Sanderson, and Esfahbodi(2024) differentiate between taskwork and teamwork, highlighting their joint importance in enhancing supply chain team effectiveness. Translating this distinction into concrete team processes, scholars identify two types of verbal communication in teams, namely, (1) taskoriented communication to accomplish task work and achieve a highquality solution, such as solving problems or sharing knowledge, and (2) relational communication to show appreciation of other team members and improve interpersonal relations, such as providing support or sharing humor within a team (Bales1950; Liao etal.2023; van Dun and Wilderom2021). While both taskoriented and relational communication are inherently goaldirected and aim to improve taskwork and relationships (even though the intended effect is not guaranteed), they alone do not provide a complete picture of communication within crossfunctional teams. This is because communication can also be classified as counterproductive (Kauffeld and LehmannWillenbrock 2012; van Dun and Wilderom 2021). (3) Counterproductive communication disrupts task progress or damages relationships, often through distracting or criticizing (e.g., Bakhtiar, Webster, and Hadwin 2018; Bales 1950). This tripartite differentiation of communication types has also been noted by van Dun and Wilderom(2021), finding that effective leaders in lean workfloor teams are characterized by low levels of counterproductive communication and a balance of relational and taskoriented communication. Drawing on interaction ritual theory (Collins2005), this article extends the established differentiation of three communication types by investigating how communication patterns can boost, maintain, or diminish the performance improvement of crossfunctional teams. It specifically focuses on relational communication patterns (i.e., patterns starting with relational communication; Bales1950). While relational communication is relevant across various team types, its role is particularly vital for crossfunctional teams for two main reasons. First, the 40 Journal of Supply Chain Management, 2025 supply chain literature repeatedly suggests the importance of relational factors for crossfunctional team performance. For example, Kaufmann and Wagner (2017) show how emotional intelligence increases the effectiveness of procurement teams, while Avgerinos and Gokpinar (2017) emphasize relational dynamics as the key to overcoming failures in surgical teams. Similarly, Oliveira, Argyres, and Lumineau(2022) underscore the importance of friendly communication within supply chain teams for successfully adapting to disruptions, and Gifford etal.(2022) stress the need to study team dynamics for better integration and performance. Second, relational communication plays a greater role for crossfunctional teams compared with other forms of teamwork, as members in such teams have to particularly avoid communication problems in order to synergize their diverse resources and competencies when jointly performing supply chain tasks (e.g., Lu, Kaufmann, and Carter2021). When performing tasks, interactions within crossfunctional teams ideally serve the overarching goal of achieving operational excellence (Ambrose, Matthews, and Rutherford 2018). Thus, based on the theoretical rationale outlined earlier, this article suggests that a common communication pattern in crossfunctional teams involves a relational statement followed by a taskoriented response (i.e., a relational → taskoriented communication pattern). Further, given the inevitable clash of different goals and opinions in crossfunctional teams (Kaufmann, Wagner, and Carter2017), this article also proposes that another important communication pattern entails a relational statement followed by a counterproductive statement (i.e., a relational → counterproductive communication pattern). 2.3 | Hypothesis Development Scholars in supply chain and general management have empirically shown that early interaction patterns in teams set the stage for subsequent interactions and outcomes (e.g., Ericksen and Dyer2004; Lu, Kaufmann, and Carter2021; Mathieu and Rapp2009; van Dun and Wilderom2021). Building on this line of research, this article focuses on relational communication in the early phases of crossfunctional teamwork and its impact on subsequent team performance improvement. This research examines team improvement as a crucial team outcome because, in line with team learning principles, a positive development in team performance across time is expected, but it is not clear which factors lead to stronger or weaker improvement (de Leeuw, Schippers, and Hoogervorst2015). Following the consensus of extant research, this article first considers the overall frequency of relational communication in early team interactions and then shifts the focus to communication patterns (Driedonks, Gevers, and Weele2010; Montoya, Massey, and Lockwood2011). The frequency of relational communication is defined as the number of relational statements per team in the first meeting (Kauffeld and LehmannWillenbrock 2012). Given the mixed results and conflicting theoretical views of previous work on relational communication in teams (e.g., Jämsen, Sivunen, and Blomqvist2022; Kauffeld and LehmannWillenbrock2012), the relationship between the mere frequency of relational communication and team performance improvement can be hypothesized in two opposing ways. On the one hand, the emotional focus mechanism postulated in interaction ritual theory suggests that successful relational communication can boost a team's shared emotional focus and emotional energy (Collins 2005). Elevated emotional energy levels among team members, in turn, foster effective teamwork (Kaufmann, Wagner, and Carter2017; Methot etal.2021). Moreover, substantial evidence shows that relational communication promotes wellbeing and trust, which are essential for team performance (Agarwal and Narayana2020; Jämsen, Sivunen, and Blomqvist 2022; Sias 2005; Vuorela 2005). Consistent with these findings, effective teams have been found to engage in more relational communication (Lu, Kaufmann, and Carter 2021; Staudinger2005). Thus, both theoretical arguments from interaction rituals and empirical evidence underscore the potential benefits of relational communication for team effectiveness. Put formally: Hypothesis 1. The frequency of relational communication during early team interactions is positively related to the performance improvement of crossfunctional teams; that is, more relational communication is associated with stronger performance improvement. On the other hand, interaction ritual theory emphasizes the importance of joint attentional focus for team effectiveness (Collins 2005). Thus, relational communication could hinder team performance if perceived as “offtopic” or unrelated to task completion (Kauffeld and LehmannWillenbrock2012; Methot etal.2021). Moreover, the frequent use of relational communication can foster groupthink in teams (Keyton1999). Supporting this perspective, Kauffeld and LehmannWillenbrock (2012) find a negative relationship between relational communication and team meeting satisfaction. Similarly, van Dun and Wilderom (2021) observe that relational communication impedes the performance of lean workfloor teams. Drawing on this alternative rationale rooted in the attentional mechanism proposed by interaction ritual theory, this article presents the following competing hypothesis: Hypothesis 1 competing. The frequency of relational communication during early team interactions is negatively related to the performance improvement of crossfunctional teams; that is, more relational communication is associated with weaker performance improvement. While the role of the frequency of relational communication remains ambiguous, the core tenet of interaction ritual theory holds that relational communication patterns (i.e., how one team member reacts to another member's relational communication) may play a more dominant role in overall team outcomes (Collins2005). Recent supply chain literature echoes this idea, highlighting the “importance of dynamic interactions within and across organizational units” for operational performance (van Dun and Wilderom2021, 88; Hoogeboom and Wilderom2020). For the present research, this suggests a need to examine behavioral patterns beyond the mere frequency of specific communication types and to assess their impact on crossfunctional team performance. Specifically, relational communication may affect team performance not only through its frequency but also through its embeddedness in the team interaction flow and its potential to trigger taskoriented communication. 41 Therefore, the second hypothesis of this article considers systematic patterns of relational and taskoriented communication and their relationship to team performance improvement. An example of a relational → taskoriented communication pattern (see Figure1) during a crossfunctional team meeting would be that when the team is facing increasing upstream supply disruptions, a team member might encourage others to participate in the discussion (i.e., relational communication). Another team member may respond by suggesting ways to improve risk management strategies (i.e., taskoriented communication). This communication pattern exemplifies a positive upward team interaction ritual wherein team members increasingly acknowledge shared goals and build positive emotional experiences within the team (Collins2005, 50). Such interaction rituals create both emotional energy and attentional focus, establishing a solid foundation for continuous improvement, thereby boosting team performance improvement (Goffman1967). Therefore, it is expected that functional communication patterns (i.e., relational → taskoriented communication patterns) during the initial phase of teamwork in crossfunctional teams are associated with greater performance improvement in subsequent phases: Hypothesis 2. Relational → taskoriented communication patterns during early team interactions are positively related to the performance improvement of crossfunctional teams; that is, more of such patterns are associated with stronger performance improvement.1 In view of the three communication types in teams (relational, task, and counterproductive), relational communication patterns can also comprise a relational communicative act, followed by a counterproductive one. A relational → counterproductive communication pattern in crossfunctional meetings is illustrated, for example, in a situation in which one team member encourages another person's participation on the topic of outsourcing outbound warehousing (i.e., relational communication), and the other person responds by drifting off into side conversations with other team members or even interrupting or undermining the speaker's encouragement (i.e., counterproductive communication). Such a communication pattern represents a negative downward interaction ritual because team members lose their sense of shared goals and drift away from collective emotional expression (Collins2005; Krishnan etal.2021). This disruption of shared team focus and emotional energy likely hinders performance improvement. Therefore, crossfunctional teams that more frequently experience relational → counterproductive communication patterns in the initial teamwork phase are expected to show weaker performance improvement over the subsequent phases: Hypothesis 3. Relational → counterproductive communication patterns during early team interactions are negatively related to the performance improvement of crossfunctional teams; that is, more such patterns are associated with weaker performance improvement. Figure 1 summarizes the research model and proposed hypotheses. 3 | Methods This research adopts a pragmatic philosophy, recognizing that various interpretations of the world exist and that no single perspective can provide a complete picture of a phenomenon (Kaushik and Walsh 2019). Accordingly, it combines quantitative analysis in the main study with an ad hoc qualitative study to address the research question from complementary angles. 3.1 | Sample and Data Collection Data were collected from selfmanaged teams of graduate students majoring in supply chain management at VU University in Amsterdam. Participants engaged in six rounds of a team simulation called TFC over 3 weeks embedded in a supply chain management course (see Figure2 for the study procedure). A student sample is appropriate for behavioral research, particularly for testing theories such as interaction ritual theory, which does not require a professional sample for its scope conditions (Thomas2011). Moreover, recruiting a professional sample for such a team simulation would be extremely challenging due to time commitments and coordination difficulties for over 100 professionals. TFC has been widely used in the psychology field (e.g., Brazhkin and Zimmerman2019; Schippers and Rus2021), and it has recently gained attention in supply chain research (e.g., de Vries etal.2022; van den Adel, Vries, and Donk2022). In the present study, participants were first randomly assigned to selfmanaged teams of three to five members. If individuals had prior formal collaboration, adjustments were made to ensure that they were not placed on the same team, thus maintaining comparability. Furthermore, no participant had prior experience with TFC. The sample comprised 130 participants, allocated across 32 teams (mean = 4.06 members per team, SD = 0.35), with an average age of 23.84 years (SD = 1.23) and 31% female participants per team. FIGURE 2 | Study procedure. 42 Journal of Supply Chain Management, 2025 Each team in TFC managed a lossmaking fruit juice company with four roles: VP Sales, VP Supply Chain, VP Operations, and VP Purchasing. In teams of three, one member takes two roles; in teams of five, one member takes on the observer role. Over six rounds, each simulating 6 months of management decisions, teams made strategic supply chain decisions—such as those related to suppliers, production speed, shelf life, and pallet location—with each role handling an interdependent scope (see de Leeuw, Schippers, and Hoogervorst2015). These decisions required balancing conflicting functional interests to optimize team performance (i.e., maximizing the ROI). As the simulation emphasizes strategic learning rather than cumulative performance, prior rounds do not affect subsequent ones. Overall, TFC serves as an appropriate tool for investigating communication processes and dynamics in crossfunctional teams and their effects on team performance (de Vries etal.2022; Schippers and Rus2021). Participants were informed that their first and last team meetings would be videorecorded, and they were asked to complete a short survey afterward. They were assured that their consent and the recordings would not affect their treatment or grading and that their data would be anonymized for research purposes in line with GDPR standards. One team declined to be recorded and was excluded from the study. Those who consented completed an informed consent form and a brief demographic questionnaire before the first meeting. They were then video recorded during the first and last meetings, and after each, they completed a survey on their emotional experiences. Research assistants set up the video cameras with builtin microphones before the teams arrived and ensured good audio quality. Each team was recorded separately. Prior research demonstrates that participants exhibit low reactivity to video recording in team meetings, with behavior closely resembling that of nonrecorded participants (Kauffeld and LehmannWillenbrock2012). Demand effects were less of a concern here than in experimental settings (Eckerd etal.2021), as participants were observed in a natural setting without overt manipulation or direct interactions with researchers (one coauthor taught the course but was unaware of the hypotheses before data collection). Following TFCbased studies (Schippers and Rus2021), simulation performance represented a small portion of the course grade to incentivize participation and prevent crossteam collaboration. Participants could opt out at any time without explanation. For each of the six rounds, an overall ROI (i.e., performance data) was extracted from the TFC simulation for each team based on their decisions. Of the 33 teams that agreed to participate, one was excluded at t1 and five at t6 due to insufficient audio quality, resulting in sample sizes ranging from N = 27 teams (for the ancillary analyses of the communication at t6) to N = 32 (for all other analyses). Team meetings averaged 43 min and 35 s (SD = 4:28, ranging from 27 to 45 min). The videos from 32 teams at t1 and 27 teams at t6 totaled 42 h and 28 min. These recordings were analyzed using a finegrained quantitative interaction coding procedure (Kauffeld, LehmannWillenbrock, and Meinecke2018), resulting in 25,641 coded behaviors (i.e., 13,983 at t1 and 11,658 at t6). The final sample characteristics are provided in AppendixS1. 3.2 | Coding of Communication This research employed interaction coding to extract team members' specific verbal behaviors during meetings, facilitated by Interact software (Version 14; Mangold International 2010).2 Research assistants used the act4team coding scheme (see Table1) to assign one code to each sense unit (i.e., the smallest meaningful speech segment containing a complete thought; Bales1950) that occurred during a team meeting. One sense unit is typically a simple sentence consisting of a subject and predicate (e.g., “I agree”) or a single word (e.g., “Okay”). In line with the best practices (Güntner, Meinecke, and Lüders2023), sense units were separated when (1) the speaker changed; (2) one speaker voiced several statements, each with a complete thought (e.g., giving feedback and then identifying a problem); or (3) one speaker shifted the main argument within the same communication type (e.g., identifying three different problems in a row). This coding approach allowed for ruling out overlapping codes and patterns (see Table2 for examples of verbatim transcripts illustrating taskoriented or counterproductive responses to relational communication). Four trained research assistants conducted the interaction coding of videorecorded meetings using a subset approach. A random sample of 11 videos was doublecoded by two research assistants (i.e., 22 videos were coded twice at t1 and t6). The interrater reliability for these subsets was satisfactory (κ = 0.76), comparable to prior studies where interrater reliability typically ranged from 0.70 to 0.90 (e.g., Gerpott etal.2019). Further, according to Landis and Koch(1977), κvalues between 0.61 and 0.80 indicate substantial interrater agreement. After the coding of all video recordings, the frequencies of each verbal type were aggregated at the team level (i.e., all individual codes per type and team were added up). Following best practices (Gerpott etal.2019; Meinecke, LehmannWillenbrock, and Kauffeld2017), the aggregated values were standardized by dividing the number of codes per type by the meeting duration in minutes and multiplying them by 45 (i.e., the standard meeting length). Raw and timestamped data were used for sequential analyses. 3.3 | Analysis Strategy The aggregated data and analysis code for this study are available on the Open Science Framework.3 A detailed description of the analysis strategy is available from the authors upon request, with key analytical elements integrated into the results section below. 4 | Results The following results emerged from our analyses. Table3 displays descriptive statistics and correlations concerning the average ROI, communication types, relational communication patterns, and team demographics. Figure3 shows that the average weekly ROI of the participating teams constantly increased, which is in line with TFC as a learning experience. In the following section, linear latent growth models were first estimated 43 to extract the extent of team performance improvement, which allowed for testing Hypothesis1 by assessing the impact of the mere frequency of relational communication on the extent of team performance improvement (i.e., from weaker to stronger improvement). Second, hypothesistesting results regarding how relational → taskoriented and relational → counterproductive communication patterns predict team performance improvement are presented. To complement these findings, TABLE 1 | Categories of communication types and corresponding verbal codes. Relational communication Taskoriented communication Counterproductive communication Definition Definition Definition Using praise and other forms of recognition to support and show appreciation of other team members and to improve interpersonal relationships Solving problems and sharing or clarifying taskrelated knowledge to accomplish taskwork and achieve a highquality solution Offtask communication that distracts from the actual taskwork or uncivil communication that jeopardizes interpersonal relationships Verbal codes • Encouraging participation • Providing support • Active listening • Reasoned disagreement • Giving feedback • Humor and laughter • Separating opinions from facts • Expressing feelings • Offering praise • Personal responsibility Verbal codes • Identifying a problem • Describing a problem • Identifying a solution • Describing a solution • Connection with problems • Connection with solution • Weighing costs/benefits • Summarizing • Visualizing • Goalsetting Verbal codes • Losing train of thought in details and examples • Criticizing • Interrupting • Side conversation • Selfpromotion • No interest in change • Complaining • Empty talk • Blaming • Denying responsibility • Terminating the discussion Note: These categories are based on and adapted from the act4teams coding scheme by Kauffeld, LehmannWillenbrock, and Meinecke(2018) to reflect the differentiation of relational, taskoriented, and counterproductive communication. TABLE 2 | Examples of relational → taskoriented and relational → counterproductive communication patterns. Response following relational communication Communication pattern Example Taskoriented Organizational knowledgea • Providing support (relational) • Connection with a solution (taskoriented) A: This will be very flexible. (referring to a procedure in TFC and pointing at the screen) B: Yes, yes. (nodding) A: Then, we change the transportation costs. Defining the objectivea • Active listening (relational) • Describing a solution (taskoriented) A: We have to try to work together […]. B and C: Hmm … Yeah … D: That's how we can only change the direct payment. (pointing at the screen) Counterproductive Separating opinion from facts (relational) • Blaming (counterproductive) A: Yeah, I personally do not know if this is right. B: I thought you (emphasized) should have said that beforehand. Organizational knowledgea • Active listening (relational) • Side conversation (counterproductive) A: You can make the decisions and control the changes (referring to the functions of TFC). B: (Nodding and looking at A while they speaks). C and D: See here … (talking about a different topic). aIn some cases, the first statement is added to provide sufficient context to understand each communication pattern. Only the last two statements of each example represent those communication patterns that are relevant to the hypotheses. 50 Journal of Supply Chain Management, 2025 teams. By suggesting concrete communication patterns to encourage or avoid in such teams, this article makes a small yet meaningful contribution to supporting the sustainable development goal of decent work and economic growth (UN General Assembly2015). Furthermore, this article offers insights into successful crossfunctional collaboration beyond the private sector. For instance, crossfunctional collaboration is also essential in the public sector, particularly in change management within local political authorities (Piercy, Phillips, and Lewis2013). Hence, the findings and recommendations regarding specific communication practices in crossfunctional teams may also be adapted to the political context. This could help shift from rigid topdown policymaking to more decentralized decisionmaking, thereby overcoming institutional silos (Piercy, Phillips, and Lewis2013). 5.4 | Limitations and Future Research The article has several limitations that provide avenues for future research. First, the simulation setting offers key advantages over purely experimental methods—for instance, it more realistically represents crossfunctional teamwork and largely minimizes the concerns about demand effects. However, it also limits the ability to make causal claims, so future research could investigate experimental or quasiexperimental approaches, building on these findings. For instance, scholars could randomly assign newly formed crossfunctional teams to training designed to enhance awareness of how to respond to relational communication with taskoriented rather than counterproductive communication. A control group of teams would engage in teamwork without such an intervention (but would, of course, receive the training afterward). Second, the crossfunctional teams in the simulation were purposefully structured to ensure that the team members had no prior formal collaboration. However, it cannot be ruled out that some team members engaged in informal interactions. Although such informally gained familiarity may also be present in crossfunctional teams in a reallife setting, preliminary evidence shows that informal interactions can impact crossfunctional sourcing collaboration (Lu, Kaufmann, and Carter 2021). Metaanalytic findings further indicate that the link between communication and performance becomes stronger with increasing team familiarity, as team members communicate more effectively (Marlow etal.2018). Contrarily, Frasier etal.(2019) show that familiarity does not necessarily correlate with the frequency or effectiveness of communication. Oliveira, Argyres, and Lumineau(2022) also make the case that relational contracting (i.e., trust based on previous relationships) is less critical for team effectiveness than actual communication in addressing interorganizational project disruptions. Future research could thus extend the present study by explicitly investigating crossfunctional teams with varying levels of familiarity among team members and exploring how team familiarity interacts with communication patterns to affect team performance. On the one hand, in line with metaanalytical findings (Marlow etal.2018), teams with higher familiarity may exhibit a stronger positive (rather than neutral) association between relational → taskoriented communication patterns and team performance improvement. On the other hand, the negative relation between relational → counterproductive communication patterns and performance improvement may likewise be more pronounced in more familiar teams. This is because team members may attribute more value to counterproductive responses if they are more familiar with the individuals involved (Xie etal.2020). In a similar vein, one could argue that teams engaging in a supply chain management simulation may differ from crossfunctional teams in real organizational contexts, where members bring diverse skills and knowledge gained from years of experience. While this distinction may be true, it does not necessarily limit the generalizability of the findings. Specifically, in realworld contexts, team members can be more strongly imprinted by their functional backgrounds, making them more prone to conflict and counterproductive communication (Boroş etal.2017; Majchrzak, More, and Faraj2012). Therefore, the identification of detrimental communication patterns in the “light touch” simulation underscores the need to remain alert to even subtle signs of negative phenomena, such as counterproductive communication, in crossfunctional teams. Third, in addition to team familiarity and jobspecific knowledge, the findings may also be influenced by individual teamwork competencies, that is, the ability to interact and cooperate with others (e.g., conflict resolution and task coordination; Aguado etal. 2014). While it was assumed that the (pseudo- )random allocation of team members would lead to an equal distribution of teamwork competencies across teams, potential biases could not be entirely ruled out. Therefore, future research on team processes and dynamics in crossfunctional teams should specifically assess members' teamwork competencies as a potential third variable that influences both communication (patterns) and supply chain performance (Fernando and Wulansari2021). Fourth, this research did not capture all possible communication patterns that might occur in crossfunctional teams. For example, relational → relational communication patterns were not explicitly considered due to methodological considerations about overlaps when including this pattern in the model.5 From a theoretical standpoint, excessive use of relational → relational communication patterns may have a detrimental effect on team performance improvement, as it could detract from essential taskrelated discussion (Eldor, Hodor, and Cappelli 2023; van Dun and Wilderom 2021). Further, the focus on relational communication patterns was motivated by the burgeoning interest in the relational side of team processes and dynamics in supply chain management (Lu, Kaufmann, and Carter 2021) and the limited empirical insights into the effects of relational communication on team effectiveness (Kauffeld and LehmannWillenbrock2012). Future research could expand the findings of this article by exploring patterns that start with taskoriented or counterproductive communication. Finally, this research expects teams to improve their performance over time, in line with general team learning principles 51 (de Leeuw, Schippers, and Hoogervorst2015; Marks, Mathieu, and Zaccaro2001). However, this may not hold true for all types of crossfunctional teams or situations in supply chain management. For example, timesensitive circumstances, such as when teams need to initiate a mitigation plan under tight deadlines (Macdonald and Corsi2013), may fall outside the scope of this research, which focuses on team learning and performance improvement over time. Future studies could explore how communication dynamics unfold in teams where the goal is to achieve immediate results rather than sustained performance improvements. 6 | Conclusions This research advances our understanding of crossfunctional teams by examining how the temporal and interactional complexities of communication affect team performance. Focusing on clear, constructive communication can help crossfunctional teams avoid performance setbacks and offer new pathways for research into improving team dynamics. Methodologically, this study demonstrates that employing an interaction coding approach can not only effectively capture actual communication interactions but also provide a promising approach to uncovering the dynamic nature of team processes. This novel methodological approach can enrich the repertoire of supply chain management research and open promising avenues for future studies on crossfunctional teams. In an era of unprecedented supply chain pressures fueled by global crises or rapid technological advancement, optimizing crossfunctional team dynamics is more critical than ever. Members' diverse expertise and perspectives constitute the greatest strength of crossfunctional supply chain teams, but they also make these teams particularly susceptible to communication breakdowns. To tackle this challenge, our research points to a deeper understanding of relational communication— how team members convey and respond to interpersonal signals—and its profound impact on supply chain performance. By addressing these relational communication dynamics, organizations can better harness the potential of crossfunctional teams, ensuring that they remain resilient and effective in navigating the uncertainties and demands of modern supply chains. Endnotes 1 In an earlier version of this research, we included an additional competing hypothesis (H2competing) that proposed a nonsignificant relationship between patterns of relational communication followed by taskoriented communication on performance improvement. Based on reviewer comments and to strengthen our conceptual grounding, we decided to remove this competing hypothesis. 2 While this study specifically focused on verbal behavior, nonor paraverbal behaviors were also partially considered in those cases where the mere verbal behaviors were inconclusive or did not fully represent the content conveyed by the speaker. For example, when identifying relational behaviors of active listening, nonverbal cues like nodding and eye contact were used to adequately code the behavior. 3 See the link to our aggregated data and analysis code: https:// osf. io/ jgk7n/ 4 We conducted an ad hoc qualitative study with 19 supply chain professionals (see AppendixS6 for the demographic details of the participants). 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Supporting Information Additional supporting information can be found online in the Supporting Information section.