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Record W2486490392 · doi:10.5539/ijef.v8n8p124

The Negative Impacts of Commercial Concealment on the Performance of Small Businesses in Jeddah Province in Saudi Arabia

2016· article· en· W2486490392 on OpenAlexvenueno aff
Nayef Al-Ghamri

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenonResidenceCeremonyBusinessHajjUnemploymentWork (physics)ImmigrationNewspaperPolitical scienceLawEconomic growthAdvertisingDemographic economicsEconomicsIslamHistory

Abstract

fetched live from OpenAlex

The commercial concealment phenomenon leads to negative economic, security and social consequences. It has been linked to several other phenomena such as money laundering operations and the violation of residence and work regulations by foreign workers who have been deported several times from the country due to their illegal entry. The trading practice by foreigners has been widespread for two main reasons: first, due to the availability of jobs; second, the Kingdom of Saudi Arabia is the shrine for many Muslims, especially after the Umrah (mini religious pilgrimage ceremony) or Hajj seasons. These reasons have contributed to the presence of a large number of foreigners who engage in commercial concealment activities with the help of a Saudi citizen. Such practices have contributed negatively towards both, the economy and the society, noting that the number of those foreigners is on the rise. Aggravating this situation, foreigners conceal each other’s practices by hiring other foreigners in their businesses and not Saudi citizens. More light has been shed on the commercial concealment issue, whereby, citizens, the media and authorities have altogether started to combat this phenomenon. It is gaining more attention especially after the spread of many security-related crimes that are constantly published in daily newspapers. Moreover, commercial concealment played a role in the increase of the unemployment rate among young people and the control of foreigners of many commercial and services businesses in which small businesses occupy a large portion. The importance of this research study is demonstrated in its focus on the commercial concealment phenomenon and its negative impacts on the State. This particular phenomenon gravely harms the economy and inflicts damage on the society through the spread of drugs, theft and other crimes. The current research addresses this phenomenon relying on the application of a positivistic (scientific) approach to study, with a deductive approach by analyzing and reaching appropriate solutions or answers to this phenomenon. The research explores the reasons behind commercial concealment, its economic impact and defines the means to combat it by identifying the best local, international methods and regulations to combat the commercial concealment crime. To identify the various aspects related to the reasons behind commercial concealment, information and data relevant to the research topic was collected from the private sector, official bodies, citizens and other related sectors of the society. Thus, this research study followed an explanatory nonexperimental research design (Belli, 2008; Cook and Cook, 2008; Johnson, 2001) via a survey which was distributed to a 100 randomly selected sample. The IBM SPSS Statistics 23 Package for the Social Sciences (SPSS®) software was used to analyze the collected data. The research drew up some proposed recommendations to combat the commercial concealment phenomenon based on the results of the analysis of questionnaires and the study of laws, regulations and relevant references.

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 score0.429
Threshold uncertainty score0.221

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 communication0.0000.000
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 head0.023
GPT teacher head0.266
Teacher spread0.243 · 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

Citations1
Published2016
Admission routes1
Has abstractyes

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