Factors leading to decentralization of ICT companies: The case of Multimedia Super Corridor, Malaysia
Bibliographic record
Abstract
Technological development in the information and communication technologies (ICT) sector is essential to attain sustainability in today’s era. Cities have developed satellite towns at the periphery with hi-fidelity digital and physical infrastructure which converts a single cantered city into a multi cantered one. In case of Kuala Lumpur Metropolitan Area (KLMA) the shift of civic services to Putrajaya and development of Multimedia Super Corridor (MSC) which offers incentives to local and foreign companies to develop a super block of research and development based economic sector. This development spearheaded the Malaysian Vision 2020 of knowledge based economy and society and has become an attraction to the business community across Malaysia. The purpose of this paper is to discuss the key factors that have attracted the companies to physically move from KLMA to MSC. To achieve the study objectives, a questionnaire survey and interviews were carried out to collect pertinent information from companies focusing on businesses in finance, insurance and real-estate. The data collected was analyzed to identify the ranking of variables of Bill of Guarantees offered in MSC policy. The study findings suggest that in addition to good infrastructure and good working environment, the tax exemption offered by the government has been the driving force for companies to decentralize towards MSC. The results suggest that the better infrastructure, connectivity, low taxes, low telecommunication tariffs, and land cost were considered as the most important factors for decentralization of ICT companies in Malaysia. The other factors that were highlighted in this study include low cost of doing business, and competitive conditions for attracting companies to avail MSC status. The study also presents the initial hindrances faced by the ICT companies i.e., accessibility issue for city clients and workers, high rental rates of the property and slow development of supportive public amenities in MSC
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".