Risks and Challenges Associated with NEOM Project in Saudi Arabia: A Marketing Perspective
Bibliographic record
Abstract
Saudi Arabia has proposed a new project, NEOM city, planned on the coast of the Red Sea with various unique and challenging features as a part of its vision 2030 to transform itself from an oil-dependent economy to knowledge-based economy. However, there are various risks and challenges associated with the project, the study of which is essential to effectively design and implement marketing and promotional strategies. Considering the large scale and scope of the project, the purpose of this study is to identify and evaluate the major contexts and associated risks in accordance with the planned city’s objectives. An online questionnaire-based survey was used to collecting data related to the severity of the risks identified and classified in a literature review. A purposive sampling approach was adopted to select experts from various governmental institutions to participate in the study. A final sample of 417 expert participants was achieved from various ministries and departments in Saudi Arabia. Eleven risk factors and challenges were identified, including design challenges, as well as legal, contractual, operational, force majeure, human resources, financial, technological, political, environmental, and sociocultural risks. Risks related to human resources (mean impact = 4) and technology factors (mean impact = 4), as well as contractual risks (mean impact = 3.9), were identified to be very high, whereas environmental (mean impact = 2.7), legal (mean impact = 2.5), and force majeure (mean impact = 2.2) risks were identified to be of low severity. Managing mega projects requires effective planning and implementation, along with risk identification and mitigation mechanisms. In addition, it is essential to manage various influencing factors (especially government decisions) in the process of implementation to achieve success.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".