{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/67756"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/67756","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Sensor-based organizational design and engineering","abstract":"We propose a sensor-based organizational design and engineering approach that combines behavioral sensor data with other sources of information such as e-mail, surveys, and performance data in order to design interventions aimed at improving organizational outcomes. The proposed system combines sensor measurements, pattern recognition algorithms, simulation and optimization techniques, social network analysis, and feedback mechanisms that aim at continuously monitoring and improving individual and group performance. We describe the system's general specifications and discuss several studies that we conducted in different organizations using the sociometric badge experimental sensing platform. We have deployed such system under naturalistic settings in more than ten organizations up to this date. We show that it is possible to automatically capture group dynamics, and analyze the relationship between organizational behaviors and both subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured by sensors, in order to optimize group configurations that maximize individual and group outcomes, both in terms of job quality characteristics and organizational performance.","abstract_html":"We propose a sensor-based organizational design and engineering approach that combines behavioral sensor data with other sources of information such as e-mail, surveys, and performance data in order to design interventions aimed at improving organizational outcomes. The proposed system combines sensor measurements, pattern recognition algorithms, simulation and optimization techniques, social network analysis, and feedback mechanisms that aim at continuously monitoring and improving individual and group performance. We describe the system's general specifications and discuss several studies that we conducted in different organizations using the sociometric badge experimental sensing platform. We have deployed such system under naturalistic settings in more than ten organizations up to this date. We show that it is possible to automatically capture group dynamics, and analyze the relationship between organizational behaviors and both subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured by sensors, in order to optimize group configurations that maximize individual and group outcomes, both in terms of job quality characteristics and organizational performance.","abstract_has_math":false,"creators":["Olguín Olguín, Daniel"],"institution":"Massachusetts Institute of Technology","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.","school":null,"contributors":[],"advisors":["Alex P. Pentland."],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011","date_published":"2011","updated_at":"2026-07-22T22:21:56Z","subjects":["Architecture. Program in Media Arts and Sciences."],"languages":["eng"],"rights":["M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission."],"rights_urls":["http://dspace.mit.edu/handle/1721.1/7582"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1721.1/67756","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Alex P. Pentland."]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. Dept. of Architecture. 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We have deployed such system under naturalistic settings in more than ten organizations up to this date. We show that it is possible to automatically capture group dynamics, and analyze the relationship between organizational behaviors and both subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured by sensors, in order to optimize group configurations that maximize individual and group outcomes, both in terms of job quality characteristics and organizational performance."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Sensor-based organizational design and engineering"]}]}],"canonical_facts":{"dc:contributor.advisor":["Alex P. Pentland."],"dc:contributor.department":["Massachusetts Institute of Technology. Dept. of Architecture. 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