Appraisal of Information Technology Requirements in Quantity Surveying Firms in Northern Nigeria
Bibliographic record
Abstract
A worrisome trend in the adoption of emerging technologies for competitive advantages and improved productivities by QSs have been observed in that there is overwhelming evidence that there is an increasing usage of IT in quantity surveying firms but little or no noticeable benefits on the practice. It however becomes pertinent to ask then, that despite the increases adoption of computing and telecommunication technologies, why isn’t there any noticeable change due to the impacts of IT in the Nigerian Construction Industry with particular reference to Quantity Surveying practices. The study appraised the requirement of IT in quantity surveying practice in Nigeria. It examined the position of IT in quantity surveying firms in terms of its requirement and analyzed IT requirements-based problem constraining quantity surveying firms. The study is a survey research designed to obtain information on information technology in QS firms. A self-administered questionnaire was employed to quantity surveyors in consulting firms for responses. Responses were returned and analyzed using severity index and later ranked in order of importance. The results indicate a high level of computerization of professional services among the firms with greater number of their computers being networked. However, despite high level of networking of available computers, sharing of printers and other scarce tools/technologies are not established. Compared to developed countries such as Canada, Australia, United Kingdom and USA, the position of IT requirements in QS firms in Nigeria can be said to be at basic level with organization possessing and applying basic IT tools and technologies (e.g. scanners, digital cameras, dvd/cd-rom, general purpose software, QS application software, chat programs etc.) in their daily operation and processes. While most firms are progressing toward intermediate level by possessing intermediate IT tools and technologies (e.g. teleconferencing, DBMS software, mobile data networks, voice output devices etc.) but these technologies are not currently utilized in their operation. Also, advanced technologies (e.g. data conferencing, digital information services, extranet, remote databases, white boarding, voice recognizers etc.) that would make QS firms reap the full benefit of IT and make difference in the industry are hardly available. It is recommended that for the quantity surveying firms in developing countries to maximize their returns on investments on IT, it must go beyond the possession of IT tools to harnessing same to reap the full benefit of technology.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.011 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".