ASSESSING THE RELATIONSHIP BETWEEN SERVICE QUALITY AND CUSTOMER LOYALTY IN DELTA STATE'S PETROLEUM SERVICE STATIONS
Bibliographic record
Abstract
Nigeria’s economy is heavily reliant on refined petroleum products, which serve as a primary fuel source for power generation, industrial plants, automobiles, and various agro-allied and petrochemical industries. The market for these products is crucial to the nation's economic stability and growth, significantly influencing the socio-economic wellbeing of the country. Key players in the market include depot operators (wholesalers), retail outlets (retailers), and both domestic and industrial consumers. The market is highly competitive due to the large number of depots and retail outlets, with products and services often having similar performance characteristics and qualities. The recent passing of the Petroleum Industry Bill, which led to the removal of fuel subsidies, has ushered in transformative changes in the sector. This regulatory shift, coupled with increased investment in the marketing of petroleum products, has intensified competition within the industry. Additionally, the classification of petroleum product retail outlets and depot operators has undergone significant evolution, impacting their operations and the broader market landscape. This paper explores the competitive dynamics of the Nigerian refined petroleum products market, examining the roles of various stakeholders and the effects of recent legislative changes on industry practices and competition
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".