Chou-Yu Tsai , Jason D. Marshall , Anwesha Choudhury , Andra Serban , YoYo Tsung-Yu Hou , Malte F. Jung , Shelley D. Dionne , Francis J. Yammarino
{"title":"Human-robot collaboration: A multilevel and integrated leadership framework","authors":"Chou-Yu Tsai , Jason D. Marshall , Anwesha Choudhury , Andra Serban , YoYo Tsung-Yu Hou , Malte F. Jung , Shelley D. Dionne , Francis J. Yammarino","doi":"10.1016/j.leaqua.2021.101594","DOIUrl":null,"url":null,"abstract":"<div><p>In an era of rapid advances in artificial intelligence, the deployment of robots in organizations is accelerating. Further, robotic capabilities are expanding to serve a broader range of leadership behaviors related to task accomplishment and relationship support. Despite the increasing use of robots in various roles across different industries, research on human-robot collaboration in the workplace is lagging behind. As such, the current research aims to provide a state-of-the-science review and directions for future work in this underdeveloped area. Drawing on current leadership paradigms, we review human-robot collaboration studies from four academic disciplines with a history of publishing such work (i.e., management, economics, psychology, engineering) and propose that the research trajectory of human-robot collaboration parallels the evolution of leadership research paradigms (i.e., leader centric, relational view, and follower centric). Given that leadership is an inherently multilevel phenomenon, we apply a levels-of-analysis framework to integrate and synthesize human-robot collaboration studies from cross-disciplinary research areas. Based on our findings, we offer suggestions for future research in terms of conceptualization, theory building and testing, practical implications, and ethical considerations.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 1","pages":"Article 101594"},"PeriodicalIF":9.1000,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Leadership Quarterly","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1048984321000990","RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 13
Abstract
In an era of rapid advances in artificial intelligence, the deployment of robots in organizations is accelerating. Further, robotic capabilities are expanding to serve a broader range of leadership behaviors related to task accomplishment and relationship support. Despite the increasing use of robots in various roles across different industries, research on human-robot collaboration in the workplace is lagging behind. As such, the current research aims to provide a state-of-the-science review and directions for future work in this underdeveloped area. Drawing on current leadership paradigms, we review human-robot collaboration studies from four academic disciplines with a history of publishing such work (i.e., management, economics, psychology, engineering) and propose that the research trajectory of human-robot collaboration parallels the evolution of leadership research paradigms (i.e., leader centric, relational view, and follower centric). Given that leadership is an inherently multilevel phenomenon, we apply a levels-of-analysis framework to integrate and synthesize human-robot collaboration studies from cross-disciplinary research areas. Based on our findings, we offer suggestions for future research in terms of conceptualization, theory building and testing, practical implications, and ethical considerations.
期刊介绍:
The Leadership Quarterly is a social-science journal dedicated to advancing our understanding of leadership as a phenomenon, how to study it, as well as its practical implications.
Leadership Quarterly seeks contributions from various disciplinary perspectives, including psychology broadly defined (i.e., industrial-organizational, social, evolutionary, biological, differential), management (i.e., organizational behavior, strategy, organizational theory), political science, sociology, economics (i.e., personnel, behavioral, labor), anthropology, history, and methodology.Equally desirable are contributions from multidisciplinary perspectives.