Interrogating user experiences with mobile phone technologies: a multi-method qualitative study of user experiences with the iPhone
Bibliographic record
Abstract
This study sets out to analyze the technology acceptance decisions by the users of one smartphone (iPhone). Variations of the Technology Acceptance Model (TAM), after first proposed by Davis (1986), have been widely used in the field of information systems research. This paper proposes extensions to TAM from user experience perspective. Hassenzahl (2003), in his user experience model, finds product attributes as important in forming the character of the product and influencing the user’s behavior. Using the extended version of TAM, I find that manipulation function of pragmatic attributes influences usefulness. I also find stimulation and identification functions of hedonic attributes to impact attitude and actual use. However, evocation has not been found important for either attitude or actual use. This research also develops an ethnographic decision tree model (EDTM) to predict the iPhone acceptance decisions for the sample and the model provides an acceptable success rate. Both the studies (extension of TAM and EDTM) utilize qualitative procedures as the research approach.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".