Exploring the Consumer Impulse Buying Behaviour from a Range of Consumer and Product Related Factors
Bibliographic record
Abstract
The main purpose of this study is to explore the consumer impulse buying behaviour from a range of consumer and product related factors. To achieve this purpose, the study was guided by five research questions in the area of product physical quality, product price, product attractiveness, product origin, and purchase location. The study employed quantitative method. A sample of 179 respondents (consumers) that visited the Accra Shopping Mall was employed using convenient selection method. A self-completed 5 point Likert structured questionnaire survey was the data collection instrument used. The data collected were computed and analysed with reliability statistics, Cramer’s V statistics under a crosstabulation statistical technique test to determine the association between the variables involved in this study. Overall, findings indicate that, the association between consumer impulse buying behaviour and product physical quality, product price, product attractiveness, product origin and purchase location was not strong. Consequently, each of the five products related factors shows a weak association with consumer impulse buying behaviour. It is recommended that manufacturers and other stakeholders support retail shops in diverse methods to improve upon their selling techniques and new ways to appeal to consumers.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".