MétaCan
Menu
Back to cohort
Record W2044686833 · doi:10.12735/jfe.v1i3p39

Budget Target Setting and Effective Performance Measurement in Nigerian Hospitality Industry

2013· article· en· W2044686833 on OpenAlexvenueno aff
Joshua Okpanachi

Bibliographic record

VenueJournal of Finance & Economics · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsnot available
Fundersnot available
KeywordsHospitality industryHospitalityBusinessOperations managementProcess managementMarketingIndustrial organizationEconomicsTourismPolitical science

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.524

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.166
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Finance & EconomicsSame topicAccounting and Organizational ManagementFrench-language works237,207