Comportement organisationnel | EPFL Graph Search

Le comportement organisationnel est une discipline carrefour qui regroupe l'étude et le management du comportement humain au sein des organisations ainsi que l'étude et le management de ces organisations. Il reprend, aux niveaux de l'individu, du groupe et de l'organisation, les concepts de la psychologie individuelle (et plus particulièrement la psychologie du travail devenue la psychologie organisationnelle), la psychologie sociale au niveau du groupe, la sociologie et plus particulièrement la sociologie des organisations et la sociologie du conflit, l'anthropologie et les sciences politiques. Une discipline carrefour hybride pour managers polymathes
Il s'agit d'un domaine au champ large, dans lequel on cherche à comprendre le comportement des individus et des groupes, les processus interpersonnels et les dynamiques organisationnelles. Ce domaine permet de placer l'individu au cœur d'un groupe (équipe) et d'analyser les interactions s'y attachant tout en analysant les

Computational Methods for Audio-Visual Analysis of Emergent Leadership in Teams

Dairazalia Sanchez Cortes

Face-to-face interactions are part of everyday life, ranging from family to working in teams and to global communities. Social psychologists have long studied these interactions with the aim of understanding behavior, motivations, and emergence of interaction patterns. An organization is environment rich in daily interactions including structured periodic meetings, planning, brainstorming, negotiations, decision-making and informal gatherings and leaders play a key role in many of them. Leader face problems, propose solutions, make decisions, and often are the main source of inspiration of the employees. Identifying emergent leaders at early stages in organizations is a key issue in organizational behavioral research, and a new problem in social computing. The study of this phenomenon requires sensing of natural face-to-face interactions, automatic extraction of behavioral cues and reliable machine learning algorithms to identify emergent leaders. In this thesis we present a computational approach to analyze emergence of leadership in small groups using multimodal audio and visual features. In the computational framework, we first present an analysis on how an emergent leader is perceived in newly formed, small groups. We present the ELEA (Emergent LEadership Analysis) corpus collected with the aim of analyzing emergence of leaders. We propose to analyze emergent leaders, using a variety of nonverbal cues studied in social psychology and automatically extracted from audio and video streams. Our analysis address how the emergent leader is perceived by his/her peers in terms of speaking and visual active, and its relation with the most dominant person (including external observers’ perception). We then propose to investigate which individual nonverbal channel (or combination of features from different channels) provides better inferences of the emergent leader and related concepts using unsupervised and supervised methods. We use a supervised collective approach which adds relational information to the nonverbal cues and compare its performance, with the performance of supervised (non-collective) and unsupervised methods. We also propose to capture the social visual attention patterns from automatically extracted features from video, in order to analyze who receives or gives the largest amount of visual attention in the group. Finally, with the aim of understanding who receives the largest amount of visual attention while speaking and who has the highest dominance ratio (i.e., many occurrences of looking at others while speaking and few occurrences of looking at others while not speaking). We synchronize the audio and video streams to capture the speaking and attention activity patterns. We end our analysis exploring the impact of the verbal content (language style) in the interactions and its influence in the perception of emergent leaders. For the language style analysis, we propose to compute word categories extracted from manual transcriptions of the discussions as well as from automatically detected keywords. We propose to use a supervised method to obtain the relevant features, and to use only the top word categories to predict the emergent leader and related concepts in each group. We then propose to differentiate word categories, between highly context-related and context-free, to explore the feasibility to infer the emergent leader in a fully automatic approach from the context-free language style. This dissertation address an audio and visual analysis of the ubiquitous phenomenon of emergent leadership in a fully automatic computational approach from face-to-face interactions. The nonverbal behavioral analysis is inspired in previous works on social psychology in the context of emergent leadership and related concepts. The automatically extracted nonverbal features are modeled to feed state-of-the-art machine learning techniques in order to infer emergent leaders.

EPFL2013

A Digital Platform connecting Academia and Small Businesses: An opportunity for Knowledge Management Experiments

Quentin Adrien Cavillier

Digital transformation is one of the biggest phenomena of this decade. One enabler of this revolution was cloud computing. Today, software engineers can deliver software as a service (SaaS), which is accessible to users through the Internet. Slack, Shopify, and Dropbox are examples of successful SaaS. We are experiencing an emergence of numerous small web applications with easy-to-use features for specific domains such as project management, accounting, resources management, customer relationships, and so forth. Thus, SaaS has been recognised as particularly well suited for the digital journey of small enterprises. However, surveys conducted by governmental agencies reveal a low adoption rate of SaaS by small enterprises in traditional sectors such as construction, services, and dealership. Previous studies have highlighted a lack of top management support, a weak relative advantage perceived, and a lack of knowledge as the key reasons for this low level of adoption. Whilst academic research defines these critical factors, it has not found potential solutions to mitigate them. Thus, this thesis presents two exploratory studies to enhance the adoption of digital practices by small traditional enterprises (STEs). A first study explored the value of leading collaborative projects between students and STEs about the assessment of SaaS solutions. A secondary objective of this initiative was the design of a platform acting as an open innovation platform. A three-year study allowed the completion of thirty-nine projects and the testing of two different platform designs. The feedback from students demonstrated the value of a well-structured platform composed of different management models for each project phase. Furthermore, 67 per cent of the STEs involved on the platform acknowledged the value of such collaborative projects. However, the collaborations did not improve top management support, and projects tended to stagnate because of various organisational factors. A second study, using a mixed-methods research design, was performed to explore the relations amongst the organisational factors acting as enablers and barriers to the digitalisation of STEs. The objective was to improve understanding of TSEs' situation and devise a proposition to enhance top management support. An academic contribution of this study was the design and application of a conceptual framework combining organisational factors from the literature on dynamic capabilities, organisational inertia, and intellectual capital. The combination of the qualitative observations and quantitative results highlighted the importance of the sensing and leadership capabilities for STE directors. They also revealed the mediator effect of the relational capability on the development of these latter. Therefore, this thesis makes important contributions to the literature on open innovation by defining a new field for leading knowledge management experiments. It also contributes to develop a comprehensive overview of the organisational factors affecting STEs' adoption. A further major contribution is the potential value of the digital platform to innovate the services of practitioners, such as governmental agencies and consultants, that support STEs on their digital transformation journey. In conclusion, this research is valuable in opening the discussion about potential innovations that might reshape the relationship academia-business.

EPFL2019