Challenges in Conducting Empirical Work Using Structuration Theory: Learning from IT Research
Bibliographic record
Abstract
Giddens’s structuration theory is increasingly used as an alternative approach to studying numerous organizational phenomena. However, the applicability of Giddens’s concepts is not without difficulties because of two main challenges. First, structuration theory is complex, involving concepts and general propositions that operate at a high level of abstraction. Second, structuration theory is not easily coupled to any specific research method or methodological approach, and it is difficult to apply empirically. Arguing that structuration theory is a valuable framework for a rich understanding of management, organization and related subjects of inquiry, this paper aims to improve the application of structuration theory in empirical work by drawing on the experience in information technology (IT) research. It identifies patterns of use of Giddens’s theory in publications in the domain of IT, and then describes how IT researchers have attempted to address its major empirical challenges. The paper presents a repertoire of research strategies that might guide students of organization in dealing with three elements that are central to structuration theory: duality of structure, time/space and actors’ knowledgeability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.434 | 0.623 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.029 | 0.057 |
| Open science | 0.011 | 0.015 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".