The Effect of Knowledge Management Systems on Measuring Success Indicators for Saudi Arabia 2030
Bibliographic record
Abstract
Vision 2030 is designed to place the Kingdom of Saudi Arabia (KSA) as a trading and financial hub in the Middle East. Ninety-six strategic objectives are framed for Vision 2010. Whilst these objectives are very inspiring, challenges are seen in integrating them under a single unifying framework. Unless the diverse objectives are integrated, knowledge and learning of team members are brought on a common platform to measure the success indicators, achieving the vision would be difficult. Objective of the paper is to develop a KM model that will help to measure the success indicators of Vision 2030. A literature review helped to understand the barriers, processes, and methodology of KM frameworks. The findings indicate that Vision 2030 is wide in scope with 96 loosely connected strategic objectives. An overarching framework that links all these objectives and places them on a common platform is not evident. These inputs were used to design the KM Vision 2030 model that links all the objectives and helps to gather metrics from the objectives, and measure the success of the project. Some of the metrics that can be considered are linking objectives, milestone achievement, adhering to schedule and budget, economic and social impact on people and businesses, progress in positioning KSA as the leader of Middle East, and others. Some of these measures are qualitative, whilst others are quantitative, implying that a multimodal data collection and analysis method is needed. The model suggests institution of Knowledge Champions, Communities of Practice, big data analytics, knowledge assets development and sharing, and brings all the objectives on a transparent and usable platform. A pilot study in the form of a semi-structured interview and survey was administered to five experts in the field of KM and IT systems. Their findings indicate that big data analytics can play a major role in decision-making and in measuring the project success. The findings also speak of the need to connect the strategic objectives. Recommendations are made to refine the model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".