Relation between Consumer Innovativeness Behavior and Purchasing Adoption Process: A Study with Electronics Sold Online
Bibliographic record
Abstract
This paper aims at analyzing the influence of consumer innovativeness behavior on the purchasing adoption process of products sold on the internet. Through a theoretical framework, the Domain Specific Innovativeness (DSI) and New Involvement Profile (NIP) scales were used in the study. The research approach has a mixed methodology, having both qualitative and quantitative approaches. In the qualitative phase, two focus group were conducted, with the objective of aligning the scales with the main research focus, and in the quantitative phase, an online survey with 448 respondents was applied. Data processing was based on multiple linear regression and results shows that the construct of the consumer innovativeness behavior has an explanatory power (R²) of 59.8% relative to the purchasing adoption process by the consumer. The consumers with a stronger innovativeness behavior showed to have similar characteristics when it came to purchasing innovative electronic products, making it easier to lead them to consumption.
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How this classification was reachedexpand
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".