Determinants for adoption of new products: an empirical study on smart phone customers in Delhi NCR
Bibliographic record
Abstract
Innovation is the key to satisfy consumer demand for new and better products. Hence it is pertinent to examine driving factors that affect consumers' purchase decisions for technologically advanced new products. This research aims to investigate factors influencing adoption of new products, particularly smartphones. Descriptive as well as causal methods of research have been adopted for this research. Researchers have used a self-administered survey for collecting data of customers who have recently purchased smartphones in Delhi National Capital Region (NCR). For this study, with a sample size of 254, convenient sampling has been used due to nature of the research. Key factors have been explained through intention to adopt (12.9%), motivated customer innovativeness (11.1%), financial risk (7.6%), functional innovativeness (7.5%), hedonic innovativeness (7.7%) and customer involvement (7%). In practice, findings of the present study would allow marketing managers in a deeper differentiation of the market and help them recognise highly creative consumer segments; this can, in turn, allow companies to plan effective marketing strategies, thereby leading to success of new products. Results of the study indicate that the eight factors used in the study have considerable influence (69% of total variance) on new product adoption in Delhi NCR.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".