Using Repertory Grids to Conduct Cross-Cultural Information Systems Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As more business is being conducted internationally and corporations establishthemselves globally, the impact of cross-cultural aspects becomes an important research issue. The need to conduct cross-cultural research is perhaps even more important in the relatively newly emerging and quickly changing information systems (IS)field. This article presents issues relating to qualitative research, emic versus etic approaches, and describes a structured, yet flexible, qualitative research interviewing technique, which decreases the potential for bias on the part of the researcher. The grounded theory technique presented in this article is based on Kelly's Repertory Grid (RepGrid), which concentrates on “laddering,” or the further elaboration of elicited constructs, to obtain detailed researchparticipant comments about an aspect within the domain of discourse. The technique provides structure to a “one-to-one” interview. But, at the same time, RepGrids allow sufficient flexibility for the research participants to be able to express their own interpretation about a particular topic. This article includes a brief outline of a series of research projects that employed the RepGrid technique to examine similarities and differences in the way in which “excellent” systems analysts are viewed in two different cultures. Also included is a discussion of the technique's applicability for qualitative researchin general and cross-cultural studies specifically. The article concludes by suggesting ways in which the RepGrid technique addresses some of the major methodological issues in cross-cultural research.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.044 |
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 it