Task–Technology Fit Leads to Conflict: The Double-Edged-Sword Effect of Generative Artificial Intelligence on Scientific Creative Performance in Humanities and Social Sciences Research
Bibliographic record
Abstract
This study examines the double-edged sword effect of task-technology fit (TTF) on scientific creative performance in the humanities and social sciences, utilizing a mixed-methods approach. Analyzing data from 405 Chinese HSS scholars through structural equation modeling and conducting thematic analysis with 12 in-depth interviews revealed that TTF generates a paradox by enhancing AI literacy while increasing AI dependence. AI literacy promotes augmentation interactions, boosting scientific creativity, whereas dependence leads to automation patterns that hinder it. Demographic variations show female and senior scholars exhibit higher levels of dependence, with AI usage frequency demonstrating a non-linear relationship with dependence. Qualitative insights highlight distinct methodological orientations toward GAI among subdisciplines, raising concerns about ethical implications, dependency, and academic inequality. This research challenges the assumption that technology-task fit always yields positive outcomes and introduces the “AI Empowerment-Inhibition Paradox” as a framework for understanding these dynamics.
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.
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 | 0.017 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".