Testing the Technology-to-Performance Chain Model
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
Goodhue and Thompson proposed the technology-to-performance chain (TPC) model in 1995 to help end users and organizations understand and make more effective use of information technology. The TPC model combines insights from research on user attitudes as predictors of utilization and insights from research on task-technology fit as a predictor of performance. In this article, the TPC model was tested in two settings - voluntary use and mandatory use. In both settings, strong support was found for the impact of task-technology fit on performance, as well as on attitudes and beliefs about use. Social norms also had a significant impact on utilization in the mandatory use setting. Beliefs about use only had a significant impact on utilization in the voluntary use setting. Overall, the results found support for the predictive power of the TPC model; however, the results show that the relationships among the constructs in the model will vary depending on if the users have a choice to use the system or not.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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