The Impact of Experience and Technology Change on Task-Technology Fit of a Collaborative Technology
Bibliographic record
Abstract
This study continues a long running effort to examine collaborative writing and editing tools and the factors that impact Task-Technology Fit and Technology Acceptance. Previous studies found that MS Word/email performed better than technologies such as Twiki, Google Docs, and Office Live. The current study seeks to examine specifically the impact of experience on these outcomes. Fortuitously, during the course of the study, Office Live was redesigned and renamed as Office 365, thus also allowing the examination of tool characteristics. In contrast to previous studies, Google Docs and Office 365 now performed significantly better than MS Word/email on the outcome measures despite users having significantly more experience with the latter. We speculate that as users gain experience with tools that perform the collaborative writing and editing task differently than did past tools, they perceive that the old technology no longer fits the task as well and thus it performs lower on outcome measures. We also observed that even though the Word/email technology did not change during the time of the study, its scores went down, possibly due to users’ experience with newer technologies more suited to the task.
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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 | 0.008 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".