Exploring user adaptation behaviors toward mobile technology: a higher education perspective
Bibliographic record
Abstract
Purpose Grounded in coping model of user adaptation (CMUA), this research aims to provide insights into understanding the social mechanism influencing faculty’s adoption and adaptation of mobile technology (MT). An extensive review of the literature highlights a significant gap in empirical research regarding the adoption of MT and faculty adaptation when viewed through the lens of group dynamics. Design/methodology/approach This study is based on an exploratory study conducted at two engineering universities in France. A qualitative method enabled a comprehensive examination of faculty members using multiple field-based observations and semi-directive interviews at both University A and University B. Findings While extant research tends to prioritize individual-level approaches, the integration of MT within higher education inherently involves social dynamics. Our results reveal that faculty’s perceived control and their initial perception over adoption of MT play an important role in shaping their adaptation behavior. The findings suggest that the adoption of MT among faculty members is influenced by various organizational factors. Specifically, the organizational logic of adoption affects their primary appraisal of MT, while group norms and social influence shape their adaptation acts. Furthermore, the organization’s continuous commitment to supporting faculty members also impacts their coping activities, ultimately influencing their overall adoption and utilization of MT. Originality/value This study builds upon the limited yet growing body of literature on a theme highly relevant for practitioners, scholars as well as MT users in a higher education environment. The paper extends the CMUA by exploring the relationship between MT adoption and continuous user adaptation at both group and organizational levels. Our proposed framework assists universities in articulating their MT adoption and implementation strategy in harmony with a clear vision of their users’ adaptation activities before, during and after the implementation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".