Sub-theme 53: Organizations, Social Relations, and their Meanings: Exploring the Potentials of Natural-language and Image-processing Tools for Big Data Analyses
Call for Papers
A long tradition in organizational research has pointed to the relevance of studying verbal and visual texts. In the contemporary
era, organizational scholars have access to unprecedented amounts of text and visual data. Organizations release their own
data in the form of annual reports, shareholder letters and calls, internal emails, organizational logos, etc., and these
data also exist in the environment around organizations in the form of, newspaper articles, interviews, or photographs, among
other forms. Verbal and visual texts have the potential to provide valuable insights, for example, into organizational values
and identities, organizational culture and social relations, and institutional and cultural processes in society more broadly
(Berger & Luckmann, 1967; Evans & Aceves, 2016; Gioia et al., 2014; Goldberg et al,, 2016; Oberg et al., 2017; Pollach,
The methodological movement towards big data (Evans & Aceves, 2016; George et al., 2016) promises that instead of being restricted to studying comparably small pieces of information, organizational scientists can consider large amounts of verbal and visual texts in their full complexity and nuance. Recently introduced methodological frameworks suggest contemporary machine-learning tools enable scaling inductively grounded close reading, mapping meaning, or transforming meaning into variables for statistical analyses, to advance organizational theory (Drori et al., 2016; Edelmann & Mohr, 2018; Goldenstein & Poschmann, 2019; Goldenstein et al., 2019; Nelson, 2017; Nelson et al., 2018; Poschmann & Goldenstein, 2019; Powell & Oberg, 2017). As text and image analysis methods are often described as quantitative methods with a qualitative logic, these methods are particularly well suited for inductively exploring large amounts of data, enabling the discovery of unnoticed, surprising, and often latent patterns that may facilitate new forms of theorization (Hannigan et al., 2019). In addition, the application of machine-learning tools provides opportunities for constructing innovative quantitative variables from unstructured (text or image) data. In particular, natural-language and image-processing methods such as grammatical parsing, topic modeling, text and image classification, visual similarity analysis, convolutional neural networks, word embeddings, or semantic vector spaces provide enormous potentials to scale studies concerning key concepts in organizational theory, and are well suited to extracting knowledge from rich, complex data.
However, recent scientific debates (for example, see volume 49 of Sociological Methodology ) suggest that, even if scholars have proceeded in exploring and professionalizing the application of machine-learning tools to questions in organizational science, we have yet to agree on a common quality criteria and research methodologies for incorporating computer and information science tools into our research and epistemology. Therefore, we believe that further discussions are needed to, first, enable organizational scholars to uncover how methods from other disciplines, such as computer science, complement and advance established methodological traditions and tool kits. Second, open communication may yield insights into when and how these methods are reasonably applied to advance social scientific epistemology. And third, as these methods and tools continue to develop at a rapid pace, we hope to advance shared and general criteria to quickly incorporate future tools, whatever they may be, for use in the social sciences.
In this sub-theme, we encourage papers to, first, connect with existing machine-leaning approaches, including vector space models, neural networks, and transfer learning. Second, we encourage papers to suggest novel ways of bringing big data analyses and machine-leaning tools into organization sciences. Third, we also appreciate efforts to develop collective criteria for how these approaches can advance theorizations on processes in and around organizations. We therefore invite papers that:
apply existing computational methodological frameworks and machine-learning tools to research questions on organizational theories;
suggest novel ways to scale inductively grounded close reading, or to transform verbal and visual texts into variables for statistical analyses of theoretical concepts.
- Berger, P.L., & Luckmann, T. (1967): The Social Construction of Reality: A Treatise in The Sociology of Knowledge. London: Penguin Press.
- Drori, G.S., Delmestri, G., & Oberg, A. (2016): “The iconography of universities as institutional narratives.” Higher Education, 71 (2), 163–180.
- Edelmann, A., & Mohr, J.W. (2018): “Formal studies of culture: Issues, challenges, and current trends.” Poetics, 68 (1), 1–9.
- Evans, J.A., & Aceves, P. (2016): “Machine translation: Mining text for social theory.” Annual Review of Sociology, 42 (1), 21–50.
- George, G., Osinga, E.C., Lavie, D., & Scott, B.A. (2016): “Big data and data science methods for management research.” Academy of Management Journal, 59 (5), 1493–1507.
- Gioia, D.A., Hamilton, A.L., & Patvardhan, S.D. (2014): “Image is everything: Reflections on the dominance of image in modern organizational life.” Research in Organizational Behavior, 34 (1), 129–154.
- Goldberg, A., Srivastava, S.B., Manian, V.G., Monroe, W., & Potts, C. (2016): “Fitting in or standing out? The tradeoffs of structural and cultural embeddedness.” American Sociological Review, 81 (6), 1190–1222.
- Goldenstein, J., & Poschmann, P. (2019): “Analyzing meaning in big data: Performing a map analysis using grammatical parsing and topic modeling.” Sociological Methodology, 49 (1), 83–131.
- Goldenstein, J., Poschmann, P., Händschke, S.G.M., & Walgenbach, P. (2019): “Global and local orientation in organizational actorhood: A comparative study of large corporations from Germany, the United Kingdom, and the United States.” European Journal of Cultural and Political Sociology, 6 (2), 201–236.
- Hannigan, T.R., Haans, 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.
- Nelson, L.K. (2017): “Computational grounded theory: A methodological framework.” Sociological Methods & Research, 4 9(1), 3-42.
- Nelson, L.K., Burk, D., Knudsen, M., & McCall, L. (2018): “The future of coding: A comparison of hand-coding and three types of computer-assisted text analysis methods.” Sociological Methods & Research, first published online on May 27, 2018, https://journals.sagepub.com/doi/abs/10.1177/0049124118769114.
- Oberg, A., Korff, V.P., & Powell, W.W. (2017): “Culture and connectivity intertwined: Visualizing organizational fields as relational structures and meaning systems.” Research in the Sociology of Organizations, 53 (1), 17–47.
- Pollach, I. (2012): “Taming textual data: The contribution of corpus linguistics to computer-aided text analysis.” Organizational Research Methods, 15 (2), 263–287.
- Poschmann, P., & Goldenstein, J. (2019): “Disambiguating and specifying social actors in big data: Using Wikipedia as a data source for demographic information.” Sociological Methods & Research, first published online on November 11, 2019, https://journals.sagepub.com/doi/10.1177/0049124119882481.
- Powell, W.W., & Oberg, A. (2017): “Networks and institutions.” In: R. Greenwood, C. Oliver, T.B. Lawrence & R.E. Meyer (eds.): The SAGE Handbook of Organizational Institutionalism. Los Angeles: SAGE Publications, 446–476.