An Empirical Taxonomy of Smartphone Users in Their Daily Distributed Decision Making
Bibliographic record
Abstract
To date, there are few published articles relating to the taxonomy of smartphone users, thus leading to the lack of understanding of the specific factors on why different users continued to use smartphones in their daily activities and tasks, including distributed decision making. To address this issue and to fill the gap in the literature, we conducted an exploratory study to derive an empirically-based taxonomy of smartphone users. Drawing on data from a sample of 201 smartphone users', we use a combination of a hierarchical and a non-hierarchical cluster analysis procedure that reveal three well-separated clusters with regard to smartphone functionality, usefulness, continuance, reliability and accessibility, as well as smartphone cognitive trust. The three clusters are labeled as enthusiastic users (50% of the sample), average users (37%), and unenthusiastic users (13%). Functionality, continuance, and cognitive trust were significantly different across the three clusters while perceived usefulness and reliability & accessibility were not. The current study contributes to the literature on smartphone usage by providing an empirically-based taxonomy of three users groups. The findings in our study have significant implications for theory and practice.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".