Insegnamento a.a. 2022-2023

30600 - SOCIAL NETWORKS IN ORGANIZATIONS

Department of Management and Technology

Course taught in English
Go to class group/s: 31
CLEAM (6 credits - II sem. - OP  |  SECS-P/10) - CLEF (6 credits - II sem. - OP  |  SECS-P/10) - CLEACC (6 credits - II sem. - OP  |  SECS-P/10) - BESS-CLES (6 credits - II sem. - OP  |  SECS-P/10) - WBB (6 credits - II sem. - OP  |  SECS-P/10) - BIEF (6 credits - II sem. - OP  |  SECS-P/10) - BIEM (6 credits - II sem. - OP  |  SECS-P/10) - BIG (6 credits - II sem. - OP  |  SECS-P/10) - BEMACS (6 credits - II sem. - OP  |  SECS-P/10)
Course Director:
ALESSANDRO IORIO

Classes: 31 (II sem.)
Instructors:
Class 31: ALESSANDRO IORIO


Lezioni della classe erogate in presenza

Mission & Content Summary

MISSION

We are increasingly living in a small world, where everything is connected to everything else: From economic markets to disease outbreaks, from the Internet to our group of friends. Relationships and flows of information among people and organizations form complex systems that are the fundamental structures governing our world, yet they defy easy understanding. To analyze these interconnected systems, we must turn to social network analysis. This course provides an intensive introduction to the field of network analysis with a particular emphasis on organizational settings. The course is divided into traditional lecture sessions and hands-on laboratory sessions in which students will have a chance to play with real-world data. The overarching goal of the course is to familiarize students with the theory, research, methodological issues, and practical implications connected with the analysis of relational data within organizations. Upon completion of the course, students should have a good grasp of social network concepts and methods, and be able to use them. The approach is very practical and it involves concrete use of social network data during the laboratory sessions. This means mastering not only software tools but also statistical and analytical methods. Students need to bring their own laptops to effectively participate in the laboratory sessions. This perspective is integrated with a practitioner approach by using examples from consulting projects.

CONTENT SUMMARY

  • Social network theories, concepts, and terminology (e.g., structural holes, social capital, social influence, origins and evolutions of network structures).
  • Using matrices and graphs to represent social relationships (e.g., one-mode and two-mode networks, layout algorithms, network visualizations).
  • Methods and measures to understand network data (e.g., centrality algorithms, cliques and communities, positions and roles, scale-free networks).
  • Applications of social network analysis (e.g., strategic alliances, organizational change, key-player detection).

Intended Learning Outcomes (ILO)

KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Explain the most important social network theories and identify their application to practical managerial problems and contexts.
  • Recall the main terminology and define concepts associated with the analysis of social networks.
  • Illustrate the main social network measures and statistical techniques that can be used to analyze relational data.
  • Contrast different ways of visualizing social networks and illustrate the implications of their use.
  • Articulate the strengths and limitations of the social network approach.

APPLYING KNOWLEDGE AND UNDERSTANDING

At the end of the course student will be able to...
  • Apply social network concepts to aid practical managerial decisions.
  • Examine a business situation through a social network perspective to determine management needs.
  • Improve their ability to establish and maintain effective social networks.
  • Design social network surveys to collect and analyze relational data.
  • Employ statistical techniques and social network software to calculate different social network measures.
  • Create detailed social network reports to communicate results in an effective way, including compelling and powerful network visualizations.

Teaching methods

  • Face-to-face lectures
  • Guest speaker's talks (in class or in distance)
  • Exercises (exercises, database, software etc.)
  • Case studies /Incidents (traditional, online)
  • Group assignments
  • Interactive class activities (role playing, business game, simulation, online forum, instant polls)

DETAILS

The course leverages a blend of methods aimed at complementing each other and optimizing the learning experience.

 

  • Lectures are used to discuss social network theories and concepts as well as the technical aspects associated with the collection and analysis of social network data. During such lectures, students also have the chance to work with case studies, interactive class activities, and short individual exercises that help them understand the peculiarities associated with these types of data.
  • Lab sessions provide students with a hands-on experience of the topics and methods discussed in class. These practice sessions focus on issues related to both research design and data analysis, and they require the use of personal computers. The specific software that will be used is UCINET, which includes the standard tools used in social network analysis. All students are required to download on their PC the latest version of UCINET (https://sites.google.com/site/ucinetsoftware/home).
  • Finally, students also put their knowledge into practice by participating in a group project. Putting on their “network consulting” hat, students will analyze and present network data in class to the instructor and other students toward the end of the course. This activity will allow them to experience first-hand the challenges associated with analyzing and presenting network data to different stakeholders.

Assessment methods

  Continuous assessment Partial exams General exam
  • Written individual exam (traditional/online)
    x
  • Group assignment (report, exercise, presentation, project work etc.)
    x
  • Active class participation (virtual, attendance)
x    

ATTENDING STUDENTS

Class attendance is strongly encouraged. Attending students are evaluated based on the following three criteria:

 

  • In-class contribution (10% of the final grade) aimed to test the students’ ability to interact in a constructive way and present their points of view in an effective way in both face-to-face lectures and lab sessions.
  • Group project (30% of the final grade) aimed to test the students' critical application of the network concepts and methodologies learned during the course. Moreover, the group project allows for testing students' ability to present their results in an effective way in both written and oral form.
  • Final written exam (60% of the final grade) that includes both open- and close-ended questions, aimed to test students' knowledge of the main theories, terminology, and concepts associated with the study of social networks, as well as the statistical techniques and software used to analyze different types relational data.

NOT ATTENDING STUDENTS

Non-attending students are evaluated only on the basis of a final written exam that includes both open- and close-ended questions aimed at testing students' knowledge of the main theories, terminology, and concepts associated with the study of social networks, as well as the statistical techniques and software used to analyze different types relational data.


Teaching materials


ATTENDING AND NOT ATTENDING STUDENTS

  • Lecture slides and references to articles will be indicated at the end of the lecture slides.
  • Hanneman, R. A., & Riddle, M. (2005). Introduction to Social Network Methods. Available online free of charge at http://faculty.ucr.edu/~hanneman/nettext/.
  • Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2018). Analyzing Social Networks (2nd edition). SAGE Publications Limited.
  • In addition to lectures, the course has also some lab-exercise sessions. Problem sets and their solutions will be posted on the Bboard platform of the course. Required software: UCINET (https://sites.google.com/site/ucinetsoftware/home).
Last change 31/05/2022 18:16