Marie T. Dasborough , Neal M. Ashkanasy , Ronald H. Humphrey , P.D. Harms , Marcus Credé , Dustin Wood
{"title":"Does leadership still not need emotional intelligence? Continuing “The Great EI Debate”","authors":"Marie T. Dasborough , Neal M. Ashkanasy , Ronald H. Humphrey , P.D. Harms , Marcus Credé , Dustin Wood","doi":"10.1016/j.leaqua.2021.101539","DOIUrl":"https://doi.org/10.1016/j.leaqua.2021.101539","url":null,"abstract":"<div><p>The study of emotional intelligence (EI) in the field of leadership, and in the organizational sciences in general, has often been characterized by controversy and criticism. But the study of EI has nonetheless persisted by developing new measures and models to address these concerns. In a prior letter exchange by Antonakis, Ashkanasy, and Dasborough (2009), two author teams debated the role of EI in the leadership literature, but also set an agenda for research and reconciliation for the future. The present exchange revisits these arguments using evidence accumulated over the past decade. Specifically, the authors debate not only the evidence for the predictive power of EI for workplace outcomes, but also the validity of EI as a construct, the measurement of EI, and the appropriateness of analytical tests for establishing the value of EI. Although the author teams agree on the value of the study of emotions and the need for rigorous research in this area, they nonetheless propose alternative agendas and priorities for the future. Further, they conclude that the issues identified in this exchange are not unique to the study of EI; but should also serve to inform the study of other personality factors and leadership more broadly.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 6","pages":"Article 101539"},"PeriodicalIF":7.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.leaqua.2021.101539","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91711331","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Leadership with Imperfect Monitoring","authors":"Gerald Eisenkopf , Christian Walter","doi":"10.1016/j.leaqua.2021.101589","DOIUrl":"https://doi.org/10.1016/j.leaqua.2021.101589","url":null,"abstract":"<div><p>This paper provides experimental evidence on how monitoring intensity shapes the impact of leadership instruments like leading-by-example and punishment. The results show that, with low monitoring intensity, neither leading-by-example nor punishment increases cooperation if the leader can already send nonbinding signals about desired behavior. We identify two different reasons for this effect. Regarding leading-by-example, it is the cautiousness of the leader. Leaders are reluctant to recommend cooperative behavior and act accordingly, even though followers are particularly reciprocal in this context. Regarding punishment, it is the level of monitoring that matters. Monitoring of individual follower behavior increases the cooperation of leaders and followers across all treatments, but in particular, if the leader can punish uncooperative behavior. This result implies that monitoring in itself does not have a negative impact on the inclination to cooperate. It suggests that any motivational crowding out effect derives from a leader’s choice of monitoring, as it signals low trust in the followers. The paper concludes with a discussion of the implications.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 6","pages":"Article 101589"},"PeriodicalIF":7.5,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90124706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brittany A. Ernst , George C. Banks , Andrew C. Loignon , Katherine A. Frear , Courtney E. Williams , Luis M. Arciniega , Roopak K. Gupta , Georg Kodydek , Dilip Subramanian
{"title":"Virtual charismatic leadership and signaling theory: A prospective meta-analysis in five countries","authors":"Brittany A. Ernst , George C. Banks , Andrew C. Loignon , Katherine A. Frear , Courtney E. Williams , Luis M. Arciniega , Roopak K. Gupta , Georg Kodydek , Dilip Subramanian","doi":"10.1016/j.leaqua.2021.101541","DOIUrl":"10.1016/j.leaqua.2021.101541","url":null,"abstract":"<div><p><span>Drawing upon signaling theory, charismatic leadership tactics (CLTs) have been identified as a trainable set of skills. Although organizations rely on technology-mediated communication, the effects of CLTs have not been examined in a virtual context. Preregistered experiments were conducted in face-to-face (Study 1; </span><em>n</em> = 121) and virtual settings (Study 2; <em>n</em> = 128) in the United States. In Study 3, we conducted virtual replications in Austria (<em>n</em> = 134), France (<em>n</em> = 137), India (<em>n</em> = 128), and Mexico (<em>n</em> = 124). Combined with past experiments, the meta-analytic effect of CLTs on performance (Cohen’s <em>d</em> = 0.52 in-person, <em>k</em> = 4; Cohen’s <em>d</em> = 0.21 overall, <em>k</em> = 10) and engagement in an extra-role task (Cohen’s <em>d</em> = 0.19 overall; <em>k</em> = 6) indicate large to moderate effects. Yet, for performance in a virtual context Cohen’s <em>d</em> ranged from −0.25 to 0.17 (Cohen’s <em>d</em> = 0.01 overall; <em>k</em> = 6). Study 4 (<em>n</em> = 129) provided mixed support for signaling theory in a virtual context, linking CLTs to some positive evaluations. We conclude with guidance for future research on charismatic leadership and signaling theory.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101541"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.leaqua.2021.101541","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74482525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Brian M. Doornenbal , Brian R. Spisak , Paul A. van der Laken
{"title":"Opening the black box: Uncovering the leader trait paradigm through machine learning","authors":"Brian M. Doornenbal , Brian R. Spisak , Paul A. van der Laken","doi":"10.1016/j.leaqua.2021.101515","DOIUrl":"10.1016/j.leaqua.2021.101515","url":null,"abstract":"<div><p>Understanding the traits that define a leader is a perennial quest. An ongoing debate surrounds the complexity required to unravel the leader trait paradigm. With the advancement of machine learning, scholars are now better equipped to model leadership as an outcome of complex patterns in traits. However, interpreting those models is often harder. In this paper, we guide researchers in the application of machine learning techniques to uncover complex relationships. Specifically, we demonstrate how applying machine learning can help to assess the complexity of a relationship and show techniques that help interpret the outcomes of “black box” machine learning algorithms. While demonstrating techniques to uncover complex relationships, we are using the Big Five Inventory and need for cognition to predict leadership role occupancy. Among our sample (n = 3385), we find that the leader trait paradigm can benefit from modeling complexity beyond linear effects and generate several interpretable results.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101515"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.leaqua.2021.101515","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46491591","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Allan Lee , Ilke Inceoglu , Oliver Hauser , Michael Greene
{"title":"Determining causal relationships in leadership research using Machine Learning: The powerful synergy of experiments and data science","authors":"Allan Lee , Ilke Inceoglu , Oliver Hauser , Michael Greene","doi":"10.1016/j.leaqua.2020.101426","DOIUrl":"10.1016/j.leaqua.2020.101426","url":null,"abstract":"<div><p>Machine Learning (ML) techniques offer exciting new avenues for leadership research. In this paper we discuss how ML techniques can be used to inform predictive and causal models of leadership effects and clarify why both types of model are important for leadership research. We propose combining ML and experimental designs to draw causal inferences by introducing a recently developed technique to isolate “heterogeneous treatment effects.” We provide a step-by-step guide on how to design studies that combine field experiments with the application of ML to establish causal relationships with maximal predictive power. Drawing on examples in the leadership literature, we illustrate how the suggested approach can be applied to examine the impact of, for example, leadership behavior on follower outcomes. We also discuss how ML can be used to advance leadership research from theoretical, methodological and practical perspectives and consider limitations.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101426"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.leaqua.2020.101426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47152295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Effect of charismatic signaling in social media settings: Evidence from TED and Twitter","authors":"Benjamin Tur , Jennifer Harstad , John Antonakis","doi":"10.1016/j.leaqua.2020.101476","DOIUrl":"10.1016/j.leaqua.2020.101476","url":null,"abstract":"<div><p>Informal leaders in social media currently characterize a large part of political and economic communication on various challenges societies face, whether localized or transborder (e.g., COVID-19 pandemic, global warming). Scholars have theorized that charismatic signaling is effective in informal leadership settings; yet empirical evidence remains scarce in understanding a ubiquitous phenomenon that marks our times and plays an important role in shaping public opinion. In this article, we used two unique data sets extracted from social media to investigate the success of charisma for informal leaders, leaders who signal their beliefs and preferences to others but having no formal authority over them. Social media offers us a standardized medium as well as a natural environment to test our predictions. Using a sample of TED talks and tweets, we coded for objective markers of charisma and found that using more verbal charismatic signals predicted (a) higher views for TED talks as well as higher ratings for the extent to which the talk was found to be inspiring—beyond attractiveness and nonverbal behavior—and (b) more retweets. We discuss the implications of such results for both theory and practice in the media age.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101476"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.leaqua.2020.101476","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49356036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Alina Lungeanu, Leslie A. DeChurch, Noshir S. Contractor
{"title":"Leading teams over time through space: Computational experiments on leadership network archetypes","authors":"Alina Lungeanu, Leslie A. DeChurch, Noshir S. Contractor","doi":"10.1016/j.leaqua.2021.101595","DOIUrl":"10.1016/j.leaqua.2021.101595","url":null,"abstract":"<div><p>A key function of team leadership is building and sustaining shared mental models. Topological approaches to leadership identify structural patterns, such as decentralized and shared leadership that empower members to collectively lead themselves toward important goals, but an open question is the particular form of leadership that best promotes team mental models. We explored 8 leadership archetypes using a computational model fit on data from a unique sample of NASA analog space crews. Data from 4, 4-member crews living and working together for 45-days were used to parameterize the model which then accurately predicted mental models for the next set of 4-member crews. The validated model was used to conduct virtual experiments exploring the effects of leadership structures on mental models. We found shared leadership has the largest effect on shared mental models, followed by hierarchical and coordinated leadership. These findings extend shared leadership theory leveraging computational methods to examine leadership archetypes and suggest propositions about how they shape team functioning over time.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101595"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73084324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sudeep Bhatia , Christopher Y. Olivola , Nazlı Bhatia , Amnah Ameen
{"title":"Predicting leadership perception with large-scale natural language data","authors":"Sudeep Bhatia , Christopher Y. Olivola , Nazlı Bhatia , Amnah Ameen","doi":"10.1016/j.leaqua.2021.101535","DOIUrl":"10.1016/j.leaqua.2021.101535","url":null,"abstract":"<div><p>We present a computational method for predicting, and identifying the correlates of, leadership perceptions for prominent individuals. Our approach proxies knowledge representations for these individuals using high-dimensional semantic vectors derived from large-scale news media datasets. It then applies machine learning techniques to build a model that maps these vectors onto participant ratings of leadership effectiveness. This method greatly outperforms other approaches and achieves accuracy rates comparable to human participants in predicting leadership effectiveness judgments. Crucially, it relies on attributes and associations identified by established theories of leadership perception—notably implicit leadership theories—as guiding lay leadership perception. Thus, our model appears to have learnt the same implicit leadership cues as our human participants. In addition, we show that our approach can be used to not only predict leadership effectiveness judgments, but also to identify dimensions that people associate with effective leadership, as well as quantify the extent of this association for each dimension. We illustrate the broad applicability of our method by using it to predict leadership perceptions for over 6000 individuals in the public sphere, and to algorithmically uncover the particular traits, concepts, and attributes that people most strongly associate with effective leaders.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101535"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.leaqua.2021.101535","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77500502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
George C. Banks , Shelley D. Dionne , Marianne Schmid Mast , Hiroki Sayama
{"title":"Leadership in the digital era: A review of who, what, when, where, and why","authors":"George C. Banks , Shelley D. Dionne , Marianne Schmid Mast , Hiroki Sayama","doi":"10.1016/j.leaqua.2022.101634","DOIUrl":"10.1016/j.leaqua.2022.101634","url":null,"abstract":"<div><p>Leadership as a social influence process has always involved a complex set of phenomena that demands an interdisciplinary lens. Leadership scholarship has now entered into a digital era. In a digital era, the overall phenomenon is changing, as are the tools through which we study it, demanding a new “lens” through which we view leadership. Yet, this raises the question, to what extent is leadership different in a digital era? In acknowledgement of this trend, a special issue was commissioned at The Leadership Quarterly that sought to stimulate the imagination of leadership scholars and practitioners. In the current work, we begin with a brief review of who, what, when, where and why of digital leadership. We cover leadership in informal contexts (e.g., social media), generalization from face-to-face to virtual contexts, computational modeling, the leveraging of technology (e.g., machine learning; Big Data), as well methodological how-to guides. We then plot a path forward for leadership scholars in the dawn of the digital era.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101634"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1048984322000376/pdfft?md5=b794b741ecdf822c79558e8cbed40b80&pid=1-s2.0-S1048984322000376-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50166254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Scott Tonidandel , Karoline M. Summerville , William A. Gentry , Stephen F. Young
{"title":"Using structural topic modeling to gain insight into challenges faced by leaders","authors":"Scott Tonidandel , Karoline M. Summerville , William A. Gentry , Stephen F. Young","doi":"10.1016/j.leaqua.2021.101576","DOIUrl":"10.1016/j.leaqua.2021.101576","url":null,"abstract":"<div><p>This paper leverages technological and methodological advances in natural language processing to advance our understanding and approaches to leadership research by introducing structural topic models (STM) to researchers wanting to inductively code massive amounts of unstructured texts. Specifically, we illustrate the application of STM applied to a large corpus (N ≈ 8000) of unstructured text responses from a diverse sample of leaders to inductively generate a classification system of leader challenges and simultaneously examine whether the challenges being experienced by leaders covary with leader characteristics. Overall, we identify nine central leader challenges. Results indicate that certain leader challenges are more prevalent depending on the leader’s characteristics (e.g., gender), and that two challenges, Daily Management Activities and Communication, were significantly related to boss’ ratings of performance. We also highlight additional applications of this technique to aid leadership researchers who wish to inductively derive meaning from large amounts of unstructured texts.</p></div>","PeriodicalId":48434,"journal":{"name":"Leadership Quarterly","volume":"33 5","pages":"Article 101576"},"PeriodicalIF":7.5,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77146654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}