Choosing a Fit Technology: Understanding Mindfulness in Technology Adoption and Continuance
Bibliographic record
Abstract
Mindfulness is an important emerging concept in society. This research posits that a user’s mindful state when adopting a technology is a crucial factor that determines how the technology will fit the task context at the post-adoption stage and, thus, has profound influence on user adoption and continued use of technology. Based on the mindfulness literature, we conceive of a new concept (mindfulness of technology adoption (MTA)) as a multi-faceted reflective high-order factor. We develop a MTA-TTF (task-technology fit) framework and integrate it into the cognitive change model to develop a research model that delineates the mechanisms through which MTA influences user adoption and continued use of technology. We examined the model via a longitudinal study of students’ use of wiki systems. The results suggest that mindful adopters will more likely perceive a technology as useful and choose a technology that turns out to fit their tasks. Hence, mindful adopters are likely to have high disconfirmation, perceived usefulness, and satisfaction at the post-adoption stage. The findings have significant implications for IS research and practices.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".