Foreign direct investment attraction and impacts in Oman
Bibliographic record
Abstract
A crucial element of Oman Vision 2020 is to enhance Oman’s economic development by accelerating the diversification of its economy in order to further reduce its economic dependency on oil. The Omani government has established legal and institutional frameworks to promote Foreign Direct Investment (FDI) as a means for the diversification of various economic activities and established FDI policies intended to sensitize the public and foreign investors. These concerted efforts by the Omani Government have borne fruit, as the Oman National Centre for Statistics and Information (NCSI, 2016) reports that the accumulation of FDI to date has attracted more than $27 billion to the Omani economy, albeit with the oil and gas industry contributing the lion’s share of this. \n \nGlobal competition among developing countries to attract FDI is at a level that international business has not previously witnessed. Governments in developing economies see FDI as a source of economic development and potential foreign investors are lured by a number of factors. \n \nHowever, despite its undisputable importance, FDI Attraction and Impact is not yet a perfect science but a long-term “trial-and-error” approach which requires a rigorous feedback loop to interpret and build on what has been learnt in a continuous quest to improve FDI Attraction and Impact performance. In this regard, the literature on FDI Attraction and Impact in the Gulf Cooperation Council (GCC), specifically in Oman is, to say the least, bleak and inaccurate. \n \nThis empirical study focused on assessing the performance of 39 FDI Attraction and Impact Factors within the Sultanate of Oman, which were specially derived for this study. The aims were to establish a cause-and-effect relationship and generate a set of FDI performance improvement recommendations. To achieve this, the researcher adopted a relativist ontological and interpretivist epistemological position, employing a mixed-use method that incorporated an inductive approach to support the predominantly qualitative methods (with, in limited instances, a positivist philosophy that incorporated a deductive approach to support quantitative methods). Finally, a descriptive research design as part of a case study research strategy was implemented to uncover the factual aspects of the FDI Attraction and Impact phenomenon in Oman. \n \nIn total, the study generated ten prioritised improvement recommendations to enhance FDI Attraction and Impact in the Sultanate of Oman. In order of priority (highest to lowest), these are as follows: \n \nCreate and fund private vocational training centres, and offer to subsidise the salaries of Omani employees to fast-track their onboarding by investors and continue their accelerated development through on job training under the “Talent Development” \n \nCreate a dedicated fund for “early stage” SME funding, along with Government incentives in the form of subsidies to promote MNC and SME collaboration, and establish a Centre for Excellence for reliable SMEs to support the offerings of MNCs, all under the “SMEs Ecosystem”. \n \nReinforce the role of a central policy making body, mandate the Central Bank to create an ecosystem that is conducive for competitive FDI attraction, and activate reliable data and information centres, all under “Economic Development Policies” \n \nEncourage Possible Public-Private-Partnership (PPP) models to enhance the scope and quality of delivery of Public Services and Public Healthcare under “Social Infrastructure” \n \nThese recommendations for improvement are as comprehensive as they are pragmatic and practical. They will ensure engagement of all concerned stakeholders and be well paced over time to ensure a rapid, yet sustainable improvement in FDI Attraction and Impact performance in the Sultanate of Oman. The researcher will make it his professional life mission to ensure this happens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".