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Record W1973821166 · doi:10.1300/j156v05n01_06

Impact of Staff Monitored Program on Firms' Market Performance

2003· article· en· W1973821166 on OpenAlexaff
Satyendra Singh

Bibliographic record

VenueJournal of African Business · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsChinaGovernment (linguistics)Economic reformInvestment (military)BusinessForeign direct investmentTransition economyEconomicsEconomic growthMarket economyPoliticsPolitical scienceMacroeconomics

Abstract

fetched live from OpenAlex

ABSTRACT The purpose of the sudy was to ascertain the extent to which Staff Monitored Program (SMP), initiated by the Government of Angola, has affected the performance of firms based in Angola. This study focused on Angola because of several reasons: first, it appears that there is no academic study that examined the proposed relationship in Angola; second, the vast majority of studies have concentrated on Common Independent States (CIS), China, and India, but a few in the African Continent; and finally, Angola is one of the newest countries embracing the concept of economic reform. Certainly, the study provides insight into the role of free-market policies on firms' market performance in the transition economy. Based on the data obtained from 180 firms representing domestic and foreign businesses with most of their headquarters in Portugal, Brazil, Spain, Sao Tome, the USA and the UK, multiple regression analysis indicated that foreign exchange reform policy and international trade reform policy were the major determinants of firms' performance. Further, the study found little support for the hypotheses that investment, and infrastructure development reform policies have led to improved firm performance. Finally, managerial implications of the findings and the limitations of the study are discussed along with future research directions.

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)0.000
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 score0.345
Threshold uncertainty score0.584

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.252
Teacher spread0.239 · 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

Citations2
Published2003
Admission routes1
Has abstractyes

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