Construct Validity in Cross Cultural Management Research: Classical Test Theory and Latent Trait Theory Approaches
Bibliographic record
Abstract
As businesses become increasingly global, the field of management stands to benefit from theories, best practices, tools, and techniques that can be used in different cultures. By providing guidelines for theory development and testing, this type of research can enhance the validity and generalizability of management theories and concepts. Cross cultural research can also be used by scholars and policy makers to better understand the comparative implications of theories that have originated in unicultural settings. Despite its importance, there is a paucity of research on the use of appropriate tools and techniques for measuring and comparing constructs across cultures. To address this gap, this paper highlights the importance of investigating conceptual, functional, and measurement equivalence of constructs as a prerequisite for cross cultural comparisons. This study also discusses how two measurement approaches, i.e., classical test theory (CTT) and item response theory (IRT) can be used in conjunction to gainfully investigate equivalence. The use of CTT and IRT models is illustrated via an empirical investigation of the Supplier Reputation Display (SRD) construct by analyzing data collected from US and Canadian automotive service managers. Implications of this research for management theory and practice, and the scope for further research are also discussed.
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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 | 0.110 | 0.242 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".