Bibliographic record
Abstract
Purpose It is widely agreed that usefulness of new products is one of the most considered factors by innovators to justify the adoption of new devices. However, the fun aspect of the product is rarely considered as a predictor of innovation adoption. The current study intends, therefore, to examine the effect of the fun aspect on consumers' adoption of technological products. Design/methodology/approach Three competing models mainly derived from the technology adoption model (TAM) were tested in two markets (Canada and France) that present two different maturity stages. A survey of 367 actual users of mobile devices was used and analyzed by a structural equation model. Findings The results show that fun is an important antecedent of the attitude toward the act (use of mobile devices for surfing the internet). Fun was also found to mediate the effect of usefulness on attitude. This implies that the impact of emotions goes beyond the consumption of hedonic products and extends to the adoption of technological ones. Research limitations/implications The small sample size of the current study did not allow deeper statistical analyses. A larger sample could allow testing the model separately in each market. Also, the current study focused only on the use of mobile devices to surf the internet. Further studies might apply the model to other products/services/industries. Practical implications The results suggest that product designers should develop interfaces and products that not only satisfy utilitarian needs but also hedonic and enjoyment motivations. Originality The present study finds its originality at two levels: first, it tests the technology adoption model using a sample from two countries (France and French Canada) which are different in terms of mobile market maturity stage. This may allow further generalisation of the TAM model. Second, in comparison to previous research on the adoption of mobile devices, the present study uses a non student sample. This is important especially when studying innovation adoption because students are thought to lean to the adoption of new habits.
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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 | 0.021 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.017 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".