From Prediction to Explanation: Reconceptualizing and Extending the Perceived Characteristics of Innovating
Bibliographic record
Abstract
Individual adoption and use of technology remains a critical concern for both managers and professionals. Despite the widespread integration of technology into work and organizations, there remain many opportunities for individuals to either extend or limit their use of IT at work. This paper extends work on the Perceived Characteristics of Innovating (PCI), as defined by Moore and Benbasat in 1991. Building on studies over the past ten years as well as on additional empirical research, we provide two contributions ?a reconceptualization and refinement of the PCI constructs, and an extended theoretical model of their influence on users?behavior. The construct refinements aim to provide greater theoretical clarity and to address challenges in the measurement of the constructs. The extended theoretical model provides a more complete picture of the influence of the PCIs, by considering the complex web of relationships among them in addition to their potential direct effects on usage.
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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 | 0.026 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".