Pre-EGOS workshop: Digital Methods for Studying Organizations and Work

CFP for a pre-Egos workshop on

Digital Methods for Studying Organizations and Work

July 2nd 2024, 9am-4pm
University of Milan central campus

The purpose of the workshop is to introduce, review, and discuss the principles and potential of researching organizations and work using digital data and digital methods. The notion of digital methods refers here to examining phenomena online either by conducting “digitally native” research that ”thinks along” with devices and services (Rogers, 2013) or by engaging in methods of computational social sciences (Lindgren, 2020; Nelimarkka 2023; Lazer et al., 2009), often in combination with more traditional methods in a mixed setting (e.g., Laaksonen et al., 2017; Tikka et al., 2023). Digital methods projects can be conducted either by using ready-made tools available (e.g., DMI Tools, Voyant, Tableau) or coding with Python or R.

Such methods have been extensively used in fields of social sciences such as communication and politics, which typically work with social media data. Uses within organization and management studies are rare (cf. Hannigan et al., 2019; Aranda, 2021; Blaschke et al., 2012; Mohr, 2015). Yet, also studies of organization and organizing are increasingly working with large datasets that often require some digital or computational approaches to explore, filter, or analyze them.

The workshop aims to develop the participants’ digital research thinking and mixed-method reasoning to create meaningful and feasible research strategies for digital research in organization studies. The workshop will begin with a set of reflective lightning talks and demos on existing projects that have utilized digital methods. We will introduce a variety of tools and provide pathways to extend skills in engaging with them, including for participants with existing experience in computing. Furthermore, using participants’ data and collective analysis approaches we will engage in a collective, critical discussion about the methods and their feasibility and limitations in different contexts, exploring potential tensions both with regards to workplaces and modes of working as well as concerning ethical approaches to digital data collection and management. Finally, we hope to bring together scholars working in these fields with an interest in studying the digital and fostering future research collaboration to advance this combination.

Submissions:

This workshop submission period has ended.  Submissions will be discussed in a round table format with small presentations only.

Convenors:
Salla-Maaria Laaksonen, University of Helsinki, salla.laaksonen@helsinki.fi
Jukka Huhtamaki, Tampere University
Alessandro Gandini, University of Milan
Sophie Del Fa, UCLouvain
François Lambotte, UCLouvain, francois.lambotte@uclouvain.be
Damien Renard, UCLouvain

References:

Aranda, A. M., Sele, K., Etchanchu, H., Guyt, J. Y., & Vaara, E. (2021). From Big Data to Rich Theory: Integrating Critical Discourse Analysis with Structural Topic Modeling. European Management Review, emre.12452. https://doi.org/10.1111/emre.12452

Blaschke, S., Schoeneborn, D., & Seidl, D. (2012). Organizations as Networks of Communication Episodes: Turning the Network Perspective Inside Out. Organization Studies, 33(7), 879–906. https://doi.org/10.1177/0170840612443459

Hannigan, T. R., Haan, R. F. J., Vakili, K., Tchalian, H., Glaser, V. L., Wang, M. S., Kaplan, S., & Jennings, P. D. (2019). Topic modeling in management research: Rendering new theory from textual data. Academy of Management Annals, 13(2), 586–632. https://doi.org/10.5465/annals.2017.0099

Laaksonen, S. M., Nelimarkka, M., Tuokko, M., Marttila, M., Kekkonen, A., & Villi, M. (2017). Working the fields of big data: Using big-data-augmented online ethnography to study candidate–candidate interaction at election time. Journal of Information Technology and Politics, 14(2), 110–131. https://doi.org/10.1080/19331681.2016.1266981

Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., & van Alstyne, M. (2009). Social science: Computational social science. Science, 323(5915), 721–723. https://doi.org/10.1126/science.1167742b

Lindgren, S. (2020) Data Theory. Polity.

Mohr, J. W., Wagner-Pacifici, R., & Breiger, R. L. (2015). Toward a computational hermeneutics. Big Data & Society, 2(2), 2053951715613809. https://doi.org/10.1177/2053951715613809

Nelimarkka, M. (2022). Computational Thinking and Social Science: Combining Programming, Methodologies and Fundamental Concepts. Sage publications.

Tikka, M., Huhtamäki, J., Harju, A. A., & Sumiala, J. (2023). Developing digital team ethnography of global media events on social media. New Media & Society, 0(0). https://doi.org/10.1177/14614448231207784

Wagner, C., Strohmaier, M., Olteanu, A. et al. Measuring algorithmically infused societies. Nature 595, 197–204 (2021). https://doi.org/10.1038/s41586-021-03666-1