Managing Religious Practices and Purchases: An Exploratory Research on Christian Consumers in Burkina Faso
Bibliographic record
Abstract
Many researchers have shown that religious factors are some motives for consumption. While a lot has been covered on the topic, some aspects are still to be discovered. A little is known on religious practices and purhase in Africa and particularly in the context of Burkina Faso. The purpose of this paper is to bring out the link between religious practices and the motives for beverages purchase by consumers. Burkina Faso is characterised by an extraordinary religious practice and an important purchase of beverages. It is important to know the reasons that lead some believers to purchase non-alcoholic industrial drinks, rather than alcohol industrial drinks. This complexity of consumer behavior and the lack of human knowledge of marketing, and of the explanatory factors of consumer behavior constitute barriers that become some management challenges for the marketing of bevrages. The methodology adopted in this research includes a qualitative approach. Qualitative data is collected from consumers. The number of semi-structured interviews considered in this research is 12. The collected data is transcribed by hand, and then it is analysed. First, the results show that high religious practices favors purchases of non-alcoholic beverages in the context. Second, the results show that low religious practices reduces purchases of non-alcoholic beverages in the same context.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".