A Meta-Analysis of Enjoyment Effect on Technology Acceptance: The Moderating Role of Technology Conventionality
Bibliographic record
Abstract
Recent advancements in Information and Communication Technology lead to the development of affordable, novel, out of the ordinary, and unconventional information technology artifacts. Such innovative technologies including virtual reality, wearable technology, and robots; feature unique human-computer interfaces, untraditional hardware designs, enable unique and atypical affordances, and provide their users with unprecedented experiences. As these artifacts become more pervasive, it is important to understand whether established Information Systems theories apply to this new paradigm. This meta-analysis introduces the definition of technology conventionality and investigates its moderating role on the effect of perceived enjoyment on users’ behavioural intention to use the technology with the aim of contrasting the effect sizes across conventional and unconventional technologies. Findings indicate that perceived enjoyment plays an important role in shaping users’ behavioural intention for both conventional and unconventional technologies. Implications for practice and future research are discussed.
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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 | 0.039 | 0.113 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.045 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".