Activity Based Costing System and Nigeria’s March towards VISION 20: 2020
Bibliographic record
Abstract
The paper examines the need to develop Activity Based Costing Systems (ABC) in accounting practices among manufacturing firms in Nigeria as a tool for product costing as Nigeria marches to the top 20 economics of the World come 2020. With the aid of a structured questionnaire, a total of 50 copies of questionnaires were administered to a cross-section of Accountants, Managers and Auditors in the manufacturing sector but only 45 copies were returned. T-test of difference between means was used to statistically test hypotheses one, two and three. Based on these, the study found among other things that there is extreme low adoption of ABC among manufacturing firms in Nigeria, possibly because of low level of ICT. Secondly, ABC improves efficiency, reduces operational costs, and properly cost products better than traditional cost accounting systems. The implication of these on the study is that in this era of Advanced Manufacturing Technology (AMT) and ICT development, traditional cost accounting systems used decades ago when the manufacturing sector was labour intensive and less automated may no longer give the required result. This should give way to Activity Based Costing system, an offshoot of the new manufacturing innovation with capabilities to cost product properly, recognizing causality and transactions involved. Consequent upon these, the study recommends that with expectations of the country to march towards a vision of attaining the height of top 20 economies of the world, Activity Based Costing systems are the challenges we need to face now. The system is in tandem with progressive ideas and new way of thinking in accounting in the manufacturing sector.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".