Budget Target Setting and Effective Performance Measurement in Nigerian Hospitality Industry
Bibliographic record
Abstract
This paper assessed roles budget target setting plays in effective performance measurement in Nigerian hotel industry. The survey research method was adopted for this study. The study population consisted of all the managers, Accountants, Account and Finance, personnel and other hoteliers of hotels located in Kaduna state. The sample size consisted of fifty respondents drawn from ten selected hotels using convenient sampling method whereby only those hotels whose managements were willing to participate in the study were chosen. The primary method of data collection used for this study was the questionnaire administration. A total of fifty (50) sets of questionnaire were distributed to the respondents out of which only forty six (46) were completed and returned. The method of data analysis used was the simple percentages while the research hypotheses were tested using chi-square statistic. The paper found that the budget target setting procedure in the hotel industry in Kaduna state is not well articulated and focused whereas budget target setting is an effective tool for effective performance evaluation of individuals and units in the hospitality industry. It is, therefore, recommended that hotels management should make the necessary efforts to strengthen their budget formulation process viz- a- viz target setting to meet achievable set goals
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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 | 0.014 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".