Using Repertory Grids to Conduct Cross-Cultural Information Systems Research
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.033 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".