Research Report: Better Theory Through Measurement—Developing a Scale to Capture Consensus on Appropriation
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
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.
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.
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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.010 |
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