The purpose of this experimental study was to examine the role of a Transformational, Transactional, and Laissez-faire leader in the perceived likelihood of employees exhibiting Organizational Citizenship Behaviors (OCBs) at work. The extent to which employees identify with their leaders was also examined as a mediator on the relationship between leadership style and OCB performance. OCBs are behaviors that are not directly required of an employee, but that benefit the overall organization by promoting excellence while allowing employees to go beyond the job requirements. Previous research suggests that Transformational Leaders inspire and instill values in employees through empowerment and positive relationships (Bass, 2007; Bass & Riggio, 2006; Carter, Mossholder, Feild, & Armenakis, 2014; Eagly, Johannesen-Schmidt, & van Engen, 2003), and that employees that perform OCBs greatly benefit organizations (Akinbode, 2011; Finkelstein & Penner, 2004; Organ & Ryan, 1995; N. P. Podsakoff, Whiting, Podsakoff, & Blume, 2009; Shaffer, Li, & Bagger, 2015). One of three vignettes describing one of the three leadership styles (e.g., Transformational, Transactional, Laissez-faire) was shown to 200 employed participants working at least part time (i.e., 20 hours per week) under a supervisor via an online survey using MTurk. After rating their respective leader (as described in the vignette) on the Global Transformational Leadership (GTL) scale, participants completed an identification with leader inventory and an OCB-checklist, indicating likelihood of OCB performance under their particular leader. Multivariate analysis of variance was utilized to examine the effect of leadership style on OCB performance. Further, correlational analyses were used to examine the relationship between GTL scores and OCB-Checklist scores. Finally, a mediation analysis with identification with the leader mediating the relationship between leadership style and OCB performance was conducted. Results showed individuals in the Transformational Leader Condition reported the highest likelihood of performing OCBs, followed by Transactional Leader and finally Laissez-faire Leader. Identification with the leader significantly mediated the relationship between Transformational leadership and OCB performance. Finally, individuals that perceived their leader as more Transformational were also more likely to report performing OCBs. Implications of these findings for OCBs in the workplace are discussed.

Dans le contexte des réseaux complexes, cette thèse s’inscrit dans deux axes : (1) Méthodologiede la détection de communautés et (2) Evaluation de la qualité des algorithmes de détection de communautés. Dans le premier axe, nous nous intéressons en particulier aux approches fondées sur les Leaders (sommets autour desquels s’agrègent les communautés). Premièrement, nous proposons un enrichissement de la méthodologie LICOD qui permet d’évaluer les différentes stratégies des algorithmes fondés sur les leaders, en intégrant différentes mesures dans toutes les étapes de l’algorithme. Deuxièmement, nous proposons une extension de LICOD, appelée it-LICOD. Cette extension introduit une étape d’auto-validation de l’ensemble des leaders. Les résultats expérimentaux de it-LICOD sur les réseaux réels et artificiels sont bons par rapport à LICOD et compétitifs par rapport aux autres méthodes. Troisièmement, nous proposons une mesure de centralité semi-locale, appelée TopoCent, pour remédier au problème de la non-pertinence des mesures locales et de la complexité de calcul élevée des mesures globales. Nous montrons expérimentalement que LICOD est souvent plus performant avec TopoCent qu’avec les autres mesures de centralité. Dans le deuxième axe, nous proposons deux méthodes orientées-tâche, CLE et PLE, afin d’évaluer les algorithmes de détection de communautés. Nous supposons que la qualité de la solution des algorithmes peut être estimée en les confrontant à d’autres tâches que la détection de communautés en elle-même. Dans la méthode CLE nous utilisons comme tâche la classification non-supervisée et les algorithmes sont évalués sur des graphes générés à partir des jeux de données numériques. On bénéficie dans ce cas de la disponibilité de la vérité de terrain (les regroupements) de plusieurs jeux de données numériques. En ce qui concerne la méthode PLE, la qualité des algorithmes est mesurée par rapport à leurs contributions dans une tâche de prévision de liens. L’expérimentation des méthodes CLE et PLE donne de nouveaux éclairages sur les performances des algorithmes de détection de communautés / In this thesis we focus, on one hand, on community detection in complex networks, and on the other hand, on the evaluation of community detection algorithms. In the first axis, we are particularly interested in Leaders driven community detection algorithms. First, we propose an enrichment of LICOD : a framework for building different leaders-driven algorithms. We instantiate different implementations of the provided hotspots. Second, we propose an extension of LICOD, we call it-LICOD. This extension introduces a self-validation step of all identified leaders. Experimental results of it-LICOD on real and artificial networks show that it outperform the initial LICOD approach. Obtained results are also competitive with those of other state-of-the art methods. Thirdly, we propose a semi-local centrality measure, called TopoCent, that address the problem of the irrelevance of local measures and high computational complexity of globalmeasures. We experimentally show that LICOD is often more efficient with TopoCent than with the other classical centrality measures. In the second axis, we propose two task-based community evaluation methods : CLE and PLE. We examine he hypothesis that the quality of community detection algorithms can be estimated by comparing obtained results in the context of other relevent tasks. The CLE approach, we use a data clustering task as a reference. The PLE method apply a link prediction task. We show that the experimentation of CLE and PLE methods gives new insights into the performance of community detection algorithms.