## 1 Introduction  
Virtual reality (VR) technology has seen widespread application in various fields such as communication, tourism, education, and entertainment as it continues to evolve [1, 2]. During the COVID-19 outbreak, social VR platforms (e.g., VR Chat, AltspaceVR, and Mozilla Hubs) emerged as alternatives to face-to-face interactions, enabling people to stay connected in virtual spaces while mitigating the risk of virus transmission from in-person meetings. Compared to traditional video communication tools, VR has gained widespread recognition for its heightened sense of immersion and co-presence [3, 4, 5]. However, the quality of VR interactions remains far from matching that of real-world experiences due to technological limitations. For instance, VR interactions primarily rely on avatars, which often lack precise facial expression control and detailed posture tracking, making it difficult to convey rich non-verbal cues [6]. Moreover, the field of view in VR environments is generally narrower than that of human vision, constraining users' ability to perceive their surroundings through visual channels [7]. These technical constraints lead to the loss or misinterpretation of non-verbal signals, complicating multi-party interactions in VR [8, 9, 10]. As a result, users often struggle to accurately perceive others' intentions, leading to confusion about when to speak or interrupt, which in turn causes issues such as overlapping speech or \"awkward silences\" [10, 11]. Previous research has explored the potential of using behavioral tracking data to predict speech behavior, demonstrating the feasibility of real-time conversational interventions in social VR [9, 12, 13, 14, 15]. However, systematic studies on how and when such models can effectively intervene in conversations and improve turn-taking among users remain limited. In particular, identifying the key factors contributing to communication breakdowns in VR and understanding users' communication needs across different contexts are critical for designing effective VR interaction support systems. Therefore, this study focuses on addressing the challenges of turn-taking coordination in VR environments under different interaction states. It employs a quantitative analysis approach to explore the underlying causes and mechanisms, aiming to provide critical insights for the development of context-aware VR interaction support systems. Specifically, we address the following research questions:  
* **R1**: What interaction factors significantly influence participants' ability to successfully acquire speaking turns in VR communication? * **R2**: How do the challenges participants face in acquiring speaking turns vary across different interaction scenarios? To answer these questions, we analyzed a VR communication dataset of six groups (four participants each) engaging in a 15 min social game. Participants annotated their speaking intentions post-experiment, and logistic regression analysis was conducted to examine the effects of individual status, spatial relationships, interaction proactivity, and communication quality on turn-taking coordination. Our contributions include identifying three key dimensions that influence the acquisition of speaking rights: (1) Individual Status: Higher-status participants encounter fewer coordination failures. (2) Interaction Proactivity: The proactive participation of participants increases overlap and turn-taking failures. (3) Communication Quality: While initial speech overlap causes confusion, it later facilitates smoother turn transitions through spontaneous adjustments. Building on these findings, we clustered five typical interaction scenarios for self-initiated turn-taking: high-competition, intense interaction, prolonged single-turn, high-status role, and low-activity contexts. As another contribution of our work, we analyzed these scenarios to compare their distinct turn-taking challenges and discussed targeted support strategies.