As described in a previous article, existing computational textual analysis approaches that take words as the fundamental unit of analysis (e.g., word counts or word-correlation measures, such as topic models), are limited in their ability to capture nuanced relationships conveyed with sentences, nor the multiple layers of structure in documents. This article explains some of the opportunities arising from being able to systemically capture within-sentence relationships, by transforming raw text into consistent textual structures.
While qualitative research has given significant consideration to nuanced relationships in text (e.g., Gamson and Modigliani, 1989; Boje, 1991; Gioia and Chittipeddi, 1991; Martens, Jennings, and Jennings, 2007), the ability to capture these relationships for systematic large-scale analysis has been limited. Given the volume of material produced in corporate governance filings (annual reports and proxy filings can are typically tens, or hundreds of pages in length - taking many hours to read each document) it is unfeasible to read and synthesize the evolution in conveyed relationships across a field of firms, with existing text analysis approaches that take the word as the unit of analysis limited in their ability to capture multi-dimensional constructs (e.g., justification for particular decisions).
By allowing the text to be transformed into consistent representations, our textual structures enable inquiry of the evolution of the discussion: rather than analyzing how the frequency of particular word (or clustering of word-usage) has changed, our textual structures make it is possible to capture and examine the evolution of the relationships, justification, and argumentation structure, as well as the multiple layers of information structuring. Moreover, the textual structures help ensure that the relationship between constructs and the underlying material can be clearly made and conveyed, while allowing more nuanced theorizing than feasible with weakly connected measures. Specific opportunities enabled with our textual structures include:
Capturing nuanced textual constructs with direct theoretical connections Our textual structures have been developed to be sufficiently flexible to capture the underlying material, including justification or the basis on which decisions were made, while abstracting surface-level variation (e.g., acronyms and synonyms) that makes sentences hard to directly compare. The connections between the sentence and the textual-structures are transparent: on a sentence-by-sentence basis it is possible to directly see how the text is transformed into the textual structures. Not only do the textual structures facilitate aggregating and comparison of the material, but they enable a large range of theoretically motivated constructs to be captured, including being able to identify and focus on very specific relationships from much larger documents (e.g., the justification for particular decisions). Direct comparisons on specific dimensions of interest By first representing the information in a consistent manner, it becomes feasible to capture specific changes of interest, including differences that may be hard to manually code, even on a relatively small scale. For example the textual structures allow blocks of text to be ‘subtracted’ from one another, allowing consideration of how particular sentences, in much larger documents, change from year to year (even if the order of the sentences in the documents differ). Being able to capture such specific differences also helps enable comparisons between texts, despite the presence of extraneous variation unrelated to the dimension of interest. Often texts vary in ways unconnected to the specific research question; being able to zone-in on the specific relationships avoids introducing extraneous noise to any comparisons. Examining multiple layers of information structuring Being able to systematically and consistently represent textual information in textual structures also facilitates research considering multiple layers of information structuring. By preserving the order of information within sentences, and the overall order of sentences, it is possible to characterize the multiple layers of structuring, including how sub-structures combine to overall meta-structures. For example, in the context of managerial backgrounds, the multiple layers of textual structuring can be captured and examined, allowing separate consideration of how information is structured within a managerial background, and how the backgrounds of multiple managers combine at the top management team level. The textual structures make direct examination of the evolution of the different levels of structuring feasible, including consideration of subtle changes that may be hard to manually code, such as adjustments over time to the ordering of information. Identification of gradual societal change Being able to systematically characterize entire populations of text also allows greater identification and awareness of the occurrence of gradual societal changes. A large proportion of organizational theories are ‘mid-range’ in nature (Merton, 1957), grounded in the real world phenomenon that they help explain (e.g., research on poison pills: Davis, 1991; acquisitions: Haunschild, 1993; adoption of TQM practices: Westphal, Gulati, and Shortell, 1997; or the difficulties faced by females and racial minorities in organizations: Eagly and Carli, 2007). Although theory may guide our understanding of these areas, theoretical development rarely occurs in a vacuum, and studying and explaining organizational change requires at least an awareness of the occurrence of the phenomenon itself (Eisenhardt and Graebner, 2007). While certain organizational changes may be readily apparent to researchers due to the suddenness of the change (e.g., the rapid diffusion of poison pills) or societal-level discussion of the issue (e.g., discrimination in the work-place), much societal change occurs gradually, garnering little main-stream attention. This may be particularly true for changes to corporate management practices, where the sheer volume of textual information in corporate filings, external communications, and conference calls, make it inherently difficult to for any individual to synthesize. Indeed, the ‘black-box’ of corporate governance (e.g., Daily, Dalton, and Cannella, 2003) may be as much a factor of the excessive volume of available information, rather than an absence. Systematically standardizing textual information allows gradual and nuanced changes in corporate leadership to be more easily identified, providing new contexts for theoretical development. Summary Menai Insight was founded because of limitations in existing approaches in capturing meaning from textual information, and in turn the limitations with using word-based approaches to capture theoretically meaningful concepts. The textual structures that we are developing for capturing the information in management backgrounds and corporate oversight are designed to capture a much more detailed representation of the nature of corporate governance. In addition to enabling specific constructs to be directly and transparently, captured from text, we enable examination of a new layer of field level theorizing, that is unfeasible to capture via hand coding of the vast volumes of governance materials, nor possible via coarse word-based measures.
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References
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