Assessment of Business Development Strategies in the Nigerian Construction Industry
Bibliographic record
Abstract
Business continuity is bravery for the survival of any business. This paper examines the business development strategies in the Nigerian construction with a view to enhance the sustainability of firms of professionals and contracting organizations in the industry. Set of a well structured questionnaire was distributed to professionals in both the consulting and contracting organizations in Abuja, the Nigeria federal capital territory. Data collected were analysed using mean score to rank the responses of the professionals on the usage and level of effectiveness of business strategies and One-Way Analysis of Variance (ANOVA) was employed to test the level of significance of strategies identified by this study. The study established that the widely used strategies by the professionals for business development in Nigeria included market penetration and organization/internal development. Moreover, the most effective strategies was organization/internal development followed by products/services branding and packaging, financial partnership, market penetration and merger and acquisition. The most significant strategies was market penetration followed by products/services branding and packaging, people/staff/personnel development, financial partnership, diversification and strategies alliance which were equally ranked high. The study concluded that market penetration, firm’s internal development, financial partnership, diversification and strategic alliance are significant and essential strategies for firms of professionals and organizations to survive in the Nigeria competitive construction markets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".