{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T06:31:39Z","timestamp":1771914699428,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T00:00:00Z","timestamp":1702512000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Within organizational settings, communication dynamics are influenced by various factors, such as email content, historical interactions, and interpersonal relationships. We introduce the Email MultiModal Architecture (EMMA) to model these dynamics and predict future communication behavior. EMMA uses data related to an email sender\u2019s social network, performance metrics, and peer endorsements to predict the probability of receiving an email response. Our primary analysis is based on a dataset of 0.6 million corporate emails from 4320 employees between 2012 and 2014. By integrating features that capture a sender\u2019s organizational influence and likability within a multimodal structure, EMMA offers improved performance over models that rely solely on linguistic attributes. Our findings indicate that EMMA enhances email reply prediction accuracy by up to 12.5% compared to leading text-centric models. EMMA also demonstrates high accuracy on other email datasets, reinforcing its utility and generalizability in diverse contexts. Our findings recommend the need for multimodal approaches to better model communication patterns within organizations and teams and to better understand how relationships and histories shape communication trajectories.<\/jats:p>","DOI":"10.3390\/info14120661","type":"journal-article","created":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T08:12:57Z","timestamp":1702627977000},"page":"661","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Building a Multimodal Classifier of Email Behavior: Towards a Social Network Understanding of Organizational Communication"],"prefix":"10.3390","volume":"14","author":[{"given":"Harsh","family":"Shah","sequence":"first","affiliation":[{"name":"Department of Electrical & Electronics Engineering, Birla Institute of Technology Pilani, Rajasthan 333031, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8127-1157","authenticated-orcid":false,"given":"Kokil","family":"Jaidka","sequence":"additional","affiliation":[{"name":"Department of Communications and New Media, National University of Singapore, Singapore 119077, Singapore"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2047-1443","authenticated-orcid":false,"given":"Lyle","family":"Ungar","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jesse","family":"Fagan","sequence":"additional","affiliation":[{"name":"The Business School, University of Exeter, Exeter EX4 4PY, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Travis","family":"Grosser","sequence":"additional","affiliation":[{"name":"The School of Business, University of Connecticut, Storrs, CT 06269, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,14]]},"reference":[{"key":"ref_1","unstructured":"Grammarly, B. 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