Research Report: Better Theory Through Measurement—Developing a Scale to Capture Consensus on Appropriation
Bibliographic record
Abstract
Proper measurement is critical to the advancement of theory (Blalock 1979). Adaptive Structuration Theory (AST) is rapidly becoming an important theoretical paradigm for comprehending the impacts of advanced information technologies (DeSanctis and Poole 1994). Intended as a complement to the faithfulness of appropriation scale developed by Chin et al. (1997), this research note describes the development of an instrument to capture the AST construct of consensus on appropriation. Consensus on appropriation (COA) is the extent to which group participants perceive that they have agreed on how to adopt and use a technology. While consensus on appropriation is an important component of AST, no scale is currently available to capture this construct. This research note develops a COA instrument in the context of electronic meeting systems use. Initial item development, statistical analyses, and validity assessment (convergent, discriminant, and nomological) are described here in detail. The contribution of this effort is twofold: First, a scale is provided for an important construct from AST. Second, this report serves as an example of rigorous scale development using structural equation modeling. Employing rigorous procedures in the development of instruments to capture AST constructs is critical if the sound theoretical base provided by AST is to be fully exploited in understanding phenomena related to the use of advanced information technologies.
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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 | 0.071 | 0.203 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".