The Effect of Reverse Intergenerational Influence on purchase and Brand Equity of Durable Goods
Bibliographic record
Abstract
The Intergenerational Influence (IGI) is the transmission of beliefs, perceptions, cognition, attitudes and behaviors from one generation to another. It is a fundamental mechanism by which culture is sustained over time. Its key elements are embedded within socialization theory. Reverse IGI indicates the transmission of beliefs and perception from child to parent. In this study the influences of children on their parent’s decision making for buying durable goods such as cars, mobile phones, laptops, air conditioners, and televisions have been studied. Parents are of age group between 45 and above. The questionnaire was designed and given to 50 respondents for the pilot study. Based upon the findings of the pilot study, the questionnaire was finalized and has been distributed to respondents in Mumbai area. In Preliminary results of a pilot study, respondents were asked to report on a variety of product category of durable goods and their brands. To test for (reverse) IGI, it is investigated if one party’s brand image perceptions, brand consideration, brand preference and loyalty (behavioral and attitudinal) have a significant influence on the other party’s brand awareness, image, consideration, preference and loyalty. In addition to these main effects, gender (of the child) and family communication patterns are expected to influence (reverse) IGI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".