Building social media theory from case studies: A new frontier for IS research
Bibliographic record
Abstract
This paper concentrates on a major concern in the IS field – theory building – and couples it with a major development in our field, social media-related environments, to consider how we might build theory, using digital texts, within the case study methodology. The growing popularity and constant innovations of social media platforms and applications have transformed ways of interacting, working, creating value and innovating. There is a need-to theorize these new environments, and the intriguing social and technical dynamics they make possible. We elaborate upon how building theory from case studies should be adapted to the opportunities and challenges of social media environments. We delve into key challenges of the research process: case study design, data analysis, and engaging in multi methods. Doing so, we identify some key considerations that can help IS researchers navigate the still new and not yet fully understood characteristics of these environments for theory building purposes.
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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 | 0.070 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.007 | 0.034 |
| Scholarly communication | 0.025 | 0.051 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".