More Than Words: Managerial Tone, Communication Modalities, and Multimodal Measures in Earnings Calls

Earnings conference calls are inherently multimodal communication events, yet research on managerial tone has developed predominantly through text-centered measurement strategies. This study addresses three research questions concerning how tone is measured, which communication modalities are examined, and what opportunities exist for multimodal corporate communication research within top-tier accounting and finance venues. A hybrid systematic literature review and bibliometric analysis of 178 peerreviewed publications (166 journal articles and 12 conference papers) was conducted, in accordance with PRISMA 2020 guidelines, spanning 2010–2026. Our bibliometric tools include keyword co-occurrence networks, bibliographic coupling, fractional authorship attribution, and international co-authorship mapping. Furthermore, an algorithmic binary coding across a 31-dimensional thematic taxonomy was applied, validated via dual-coder review on a stratified 60-paper subsample (macro precision = 0.852, recall = 0.739, F1 = 0.777). A significant structural shift toward transformer-based architectures emerges after 2023 (Cochran–Armitage pBH = 0.018), expanding alongside, rather than displacing, established lexicon-based and deep learning baselines. Although textual analysis still anchors the literature, vocal and interactional modeling remain nascent, video analytics is virtually untapped in premier venues, and international collaboration remains heavily concentrated within the US–Canada corridor. To bridge these gaps, a six-part research roadmap targeting multimodal fusion architectures is proposed: domain-adapted vocal models, cross-linguistic generalizability, withincall dynamics analysis, video call analysis, and multimodal deception detection. Received: 4 June 2026 | Revised: 27 July 2026 | Accepted: 7 September 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data are available from the corresponding author upon reasonable request. Author Contribution Statement Rodrigo dos Reis Maia: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review & editing, Visualization. Jorge Miguel Bravo: Validation, Resources, Writing – review & editing, Supervision, Project Administration, Funding Acquisition.