IDEAS home Printed from https://ideas.repec.org/a/gam/jscscx/v8y2019i11p306-d285186.html
   My bibliography  Save this article

Email Based Institutional Network Analysis: Applications and Risks

Author

Listed:
  • Panayotis Christidis

    (European Commission, Joint Research Centre, c/Inca Garcilaso 3, E-41092 Sevilla, Spain)

  • Álvaro Gomez Losada

    (European Commission, Joint Research Centre, c/Inca Garcilaso 3, E-41092 Sevilla, Spain)

Abstract

Social Network Analysis can be applied to describe the patterns of communication within an organisation. We explore how extending standard methods, by accounting for the direction and volume of emails, can reveal information regarding the roles of individual members. We propose an approach that models certain operational aspects of the organization, based on directional and weighted indicators. The approach is transferable to other types of social network with asymmetrical connections among its members. However, its applicability is limited by privacy concerns, the existence of multiple alternative communication channels that evolve over time, the difficulty of establishing clear links between organisational structure and efficiency and, most importantly, the challenge of setting up a system that measures the impact of communication behavior without influencing the communication behaviour itself.

Suggested Citation

  • Panayotis Christidis & Álvaro Gomez Losada, 2019. "Email Based Institutional Network Analysis: Applications and Risks," Social Sciences, MDPI, vol. 8(11), pages 1-14, November.
    • Handle: RePEc:gam:jscscx:v:8:y:2019:i:11:p:306-:d:285186
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2076-0760/8/11/306/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2076-0760/8/11/306/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ingo Scholtes & Nicolas Wider & René Pfitzner & Antonios Garas & Claudio J. Tessone & Frank Schweitzer, 2014. "Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks," Nature Communications, Nature, vol. 5(1), pages 1-9, December.
    2. Clemente, G.P. & Grassi, R., 2018. "Directed clustering in weighted networks: A new perspective," Chaos, Solitons & Fractals, Elsevier, vol. 107(C), pages 26-38.
    3. Ze Li & Duoyong Sun & Renqi Zhu & Zihan Lin, 2017. "Detecting event-related changes in organizational networks using optimized neural network models," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-21, November.
    4. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
    5. Panayotis Christidis & Caralampo Focas, 2019. "Factors Affecting the Uptake of Hybrid and Electric Vehicles in the European Union," Energies, MDPI, vol. 12(18), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Mingwu & Dankowicz, Harry, 2019. "Impact of temporal network structures on the speed of consensus formation in opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1355-1370.
    2. Andrew Mellor, 2019. "Event Graphs: Advances And Applications Of Second-Order Time-Unfolded Temporal Network Models," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(03), pages 1-26, May.
    3. Aming Li & Yang-Yu Liu, 2020. "Controlling Network Dynamics," Advances in Complex Systems (ACS), World Scientific Publishing Co. Pte. Ltd., vol. 22(07n08), pages 1-19, February.
    4. Vikram Bharti & Thendiyath Roshni & Madan Kumar Jha & Mohammad Ali Ghorbani & Osama Ragab Abdelaziz Ibrahim, 2024. "Complex network analysis of groundwater level in Sina Basin, Maharashtra, India," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 18017-18032, July.
    5. Christophe Chorro & Emmanuelle Jay & Philippe De Peretti & Thibault Soler, 2021. "Frequency causality measures and Vector AutoRegressive (VAR) models: An improved subset selection method suited to parsimonious systems," Documents de travail du Centre d'Economie de la Sorbonne 21013, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    6. Roy Cerqueti & Francesca Pampurini & Annagiulia Pezzola & Anna Grazia Quaranta, 2022. "Dangerous liasons and hot customers for banks," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 65-89, July.
    7. Dantsuji, Takao & Sugishita, Kashin & Fukuda, Daisuke, 2023. "Understanding changes in travel patterns during the COVID-19 outbreak in the three major metropolitan areas of Japan," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    8. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    9. Nadia von Jacobi & Vito Amendolagine, 2021. "What Feeds on What? Networks of Interdependencies between Culture and Institutions," DEM Working Papers 2021/13, Department of Economics and Management.
    10. Peng, Ruoqing & Tang, Justin Hayse Chiwing G. & Yang, Xiong & Meng, Meng & Zhang, Jie & Zhuge, Chengxiang, 2024. "Investigating the factors influencing the electric vehicle market share: A comparative study of the European Union and United States," Applied Energy, Elsevier, vol. 355(C).
    11. Pietro DeLellis & Anna DiMeglio & Franco Garofalo & Francesco Lo Iudice, 2017. "The evolving cobweb of relations among partially rational investors," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-21, February.
    12. Karan, Rituraj & Biswal, Bibhu, 2017. "A model for evolution of overlapping community networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 474(C), pages 380-390.
    13. Fabrizio Fusillo & Sandro Montresor & Giuseppe Vittucci Marzetti, 2021. "The global network of embodied R&D flows," Discussion Paper series in Regional Science & Economic Geography 2021-05, Gran Sasso Science Institute, Social Sciences, revised Apr 2021.
    14. Wojciech Lewicki & Wojciech Drozdz & Piotr Wroblewski & Krzysztof Zarna, 2021. "The Road to Electromobility in Poland: Consumer Attitude Assessment," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 1), pages 28-39.
    15. Sindhuja Ranganathan & Mikko Kivelä & Juho Kanniainen, 2018. "Dynamics of investor spanning trees around dot-com bubble," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    16. Joana Lage & Ana d’Espiney & Nuno Canha & Vítor Manteigas & José Luís Alexandre & Karla Gonçalves & Ricardo Chacartegui & Jesus Lizana & Yolanda Lechón & Ana Rosa Gamarra & Amaia Fernandez & Patrice B, 2022. "Mobility Patterns of Scholar Communities in Southwestern European Countries," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
    17. Jose Esteves & Daniel Alonso-Martínez & Guillermo de Haro, 2021. "Profiling Spanish Prospective Buyers of Electric Vehicles Based on Demographics," Sustainability, MDPI, vol. 13(16), pages 1-22, August.
    18. Wang, Tao & Xiao, Shiying & Yan, Jun & Zhang, Panpan, 2021. "Regional and sectoral structures of the Chinese economy: A network perspective from multi-regional input–output tables," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    19. Katarzyna Kubiak-Wójcicka & Filip Polak & Leszek Szczęch, 2022. "Water Power Plants Possibilities in Powering Electric Cars—Case Study: Poland," Energies, MDPI, vol. 15(4), pages 1-17, February.
    20. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jscscx:v:8:y:2019:i:11:p:306-:d:285186. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.