The Role of Customer Innovativeness in the New Products Adoption Intentions：An Empirical Study on Mobile Phone Customers of the Egyptian Universities Students
Bibliographic record
Abstract
This research aims to investigate the relationship between customers’ innovativeness and their intentions to adopt new mobile phones from the standpoint of Egyptian university students. The research studies the direct effects of the five dimensions of customers’ innovativeness on their intentions of new products adoption, which are measured through the mediating effect of two factors: the risks to mobile phones perceived by the customers and customer involvement. The research also aims to identify the so-called “initiators” segment; customers who have the highest probability for purchase the product early. A quantitative method with deductive approach is chosen in this research. Four hypotheses have been designed to determine: whether there is a significant difference in customers’ perception of risks to new mobile phones, innovativeness, involvement, and adoption intentions according to demographic variables (gender, place of residence, income); whether there is a significant positive effect of customers’ innovativeness on customers involvements with new mobile phones; whether there is a significant negative effect of customers’ innovativeness on the perceived risks to new mobile phones; and whether there is a significant positive effect of customers’ innovativeness on their intentions to adopt new mobile phones. A significant impact of the five dimensions of customers’ innovativeness is found on the adoption intentions of new mobile phones. Also a significant effect of the five dimensions of customers’ innovativeness is found on the perceived risks and customer involvement factors. The research develops a new model of the relationship between the customers’ innovativeness and their intentions to adopt new products. In practice, the research results contribute to help marketing managers for better market fragmentation and identify customer segments with high innovativeness; which helps organizations prepare appropriate marketing campaigns and thus leads to the success of new products deployment.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".