Something borrowed, something new: Challenges in using qualitative methods to study under-researched international business phenomena
Bibliographic record
Abstract
Abstract This article responds to calls for IB researchers to study a greater diversity of international business (IB) phenomena in order to generate theoretical insights about empirical settings that are under-represented in the scholarly IB literature. While this objective is consistent with the strengths of qualitative research methods, novel empirical settings are not always well aligned with methods that have been developed in better-researched and thus more familiar settings. In this article, we explore three methods-related challenges of studying under-researched empirical settings, in terms of gathering and analyzing qualitative data. The challenges are: managing researcher identities, navigating unfamiliar data gathering conditions, and theorizing the uniqueness of novel empirical settings. These challenges are integral to the process of contextualization, which involves linking observations from an empirical setting to the categories of the theoretical research context. We provide a toolkit of recommended practices to manage them, by drawing on published accounts of research by others, and on our own experiences in the field.
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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 | 0.535 | 0.471 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.016 | 0.064 |
| Scholarly communication | 0.031 | 0.029 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".