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In the last few decades network organizations and Social Network Analysis (SNA) have risen as a key field for research in Management and Business studies. Several sub-disciplines, such as human resource and talent management, knowledge and innovation management have benefited from SNA theory and methods. This 7th EDEN seminar is going to introduce SNA theory, methods and techniques for doctoral students who aim collecting, analyzing and visualizing network data for their research in diverse organizational settings and application areas. SNA and visualization software such as Ucinet and Netdraw is going to be deployed in the seminar. Examples from several research projects in doctoral and post-doctoral levels from both sides of the Atlantic are going to illustrate many of the qualitative and quantitative issues related to ‘network theory’ and SNA of relational data at community, inter-organizational and inter-personal levels of analysis. Particular attention is going to be paid in the dynamic analysis of longitudinal data in the last one and a half days of the seminar using the Stata software. In addition students are going to be allocated time in each and every day of the seminar for reflecting on what they will have learned the previous day and also presenting their own ongoing doctoral projects for getting advice and feedback from faculty and other participants during and after the seminar, see draft schedule below. The primary aim here it is to create an intimate environment conducive for learning for about 20 doctoral students and 4 faculty members from top EU / US Business Schools to share best practices and learning in the state of the art in SNA theory and methods across boundaries.
Dimitris Assimakopoulos, EMLYON Business School, France
Preliminary Outline of the Seminar Our seminar is going to combine lectures covering a broad range of issues, in depth tutorial discussions, and hands on training for SNA and dynamic analysis. Four modules are planned to be delivered as they are outlined below, including several required or/and recommended readings, that the students should ideally review and prepare before the seminar. Module 1: An Introduction to SNA theory and methods The main objective of this first module is to introduce you to the field of SNA research methods, with particular reference to the emergence of new technological communities and analysis of new ‘distributed’ product development teams. We will also discuss the rationale for SNA in module 1, how to collect and organize network data, plus the main theoretical concepts of centrality at node level and cohesion at the network level. We will also illustrate these concepts and methods through examples stemming from half a dozen research projects carried out with various collaborators in the EU and US over the past two decades. Module 2: Social networks: ties or structure? The objective of this module is to use the content of the papers assigned to explore and discuss current debates in social network research as applied to organizations. We will examine primarily theoretical issues and focus on their relationships with the methods and measures used to critically evaluate past work in the area as well as to pinpoint future directions and fruitful new line of inquiry in the area of social networks and knowledge management/innovation The substance of the module, in seminar format, is based on a close reading of primary works from leading researchers in each area. These works are not so much an exhaustive literature review as they are an opportunity for exploring the genre of social network theory and its application to organizational research. Module 3: Advanced Topics in Social Network Analysis Social network analysis focuses on the relationships between actors who are interdependent, and on social structure that emerges from regularities in this interdependence. The main objective of this module is to introduce you to several advanced topics in the field of SNA. Different types of networks:
Module 4: Analyzing Events over Time The main objective of this module is to introduce you to the various concepts and models available for studying change in variables of qualitative nature. This methodology is called Event History Analysis (henceforth, EHA), a term that refers to the group of techniques used to study events. Event history analysis is used to study longitudinal data when the social process to study is the occurrence of an event. An “event” is a change from one state to another; and states are best represented by a categorical variable. Thus, such an event is measured using a categorical dependent variable. EHA has also been called survival analysis because biologists and epidemiologists were the first to use and develop this methodology in order to study the survival of organisms after certain treatments. EHA analyzes longitudinal data available for a sample of individual cases or units during a period of time when a series of events may occur. EHA allows the researcher to examine the determinants or factors behind the occurrence of any type of social event over time and can consequently help answer questions that previously could not be answered using the classic linear regression or the logit / probit models. This module is structured as follows: Session 1: the two main models for modeling binary outcomes, the logit and probit models Session 2: unique language used in the discussion of events and the EHA methodology available to analyze events over time; the most commonly used EHA techniques,
Full attendance is required to obtain the certificate. 4 ECTS will be assigned upon completion of the seminar.
APPLICATIONS
PARTICIPATION FEE EIASM SCHOLARSHIPS
TIME AND LOCATION HOTEL ACCOMMODATION APPLICATIONS The EDEN Team EIASM - Rue FOSSÉ AUX LOUPS - 38 - BOX 3 - 1000 BRUSSELS - BELGIUM Tel: +32 2 226 66 69 Email: eden@eiasm.be
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