Determinants for adoption of new products: an empirical study on smart phone customers in Delhi NCR
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it