Leadership = Communication? The relations of leaders' communication styles with leadership styles, knowledge sharing and leadership outcomes

Purpose: The purpose of this study was to investigate the relations between leaders' communication styles and charismatic leadership, human-oriented leadership (leader's consideration), task-oriented leadership (leader's initiating structure), and leadership outcomes. Methodology: A survey was conducted among 279 employees of a governmental organization. The following six main communication styles were operationalized: verbal aggressiveness, expressiveness, preciseness, assuredness, supportiveness, and argumentativeness. Regression analyses were employed to test three main hypotheses. Findings: In line with expectations, the study showed that charismatic and human-oriented leadership are mainly communicative, while task-oriented leadership is significantly less communicative. The communication styles were strongly and differentially related to knowledge sharing behaviors, perceived leader performance, satisfaction with the leader, and subordinate's team commitment. Multiple regression analyses showed that the leadership styles mediated the relations between the communication styles and leadership outcomes. However, leader's preciseness explained variance in perceived leader performance and satisfaction with the leader above and beyond the leadership style variables. Implications: This study offers potentially invaluable input for leadership training programs by showing the importance of leader's supportiveness, assuredness, and preciseness when communicating with subordinates. Originality/value: Although one of the core elements of leadership is interpersonal communication, this study is one of the first to use a comprehensive communication styles instrument in the study of leadership. © 2009 The Author(s)

Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Accuracy versus precision in boosted top tagging with the ATLAS detector

Abstract The identification of top quark decays where the top quark has a large momentum transverse to the beam axis, known as top tagging, is a crucial component in many measurements of Standard Model processes and searches for beyond the Standard Model physics at the Large Hadron Collider. Machine learning techniques have improved the performance of top tagging algorithms, but the size of the systematic uncertainties for all proposed algorithms has not been systematically studied. This paper presents the performance of several machine learning based top tagging algorithms on a dataset constructed from simulated proton-proton collision events measured with the ATLAS detector at √ s = 13 TeV. The systematic uncertainties associated with these algorithms are estimated through an approximate procedure that is not meant to be used in a physics analysis, but is appropriate for the level of precision required for this study. The most performant algorithms are found to have the largest uncertainties, motivating the development of methods to reduce these uncertainties without compromising performance. To enable such efforts in the wider scientific community, the datasets used in this paper are made publicly available.</jats:p