Knowledge Management Practices in the Nigerian Telecommunications Industry
Bibliographic record
Abstract
The main objective of the study was to examine how Nigerian telecommunications organizations leverage knowledge in achieving organizational performance and competitive advantage. Forty organizations were selected by using stratified random sampling from the 150 organizations in the Nigerian telecommunications industry. Twenty‐nine of the selected organizations agreed to participate in the study, and questionnaires were then distributed to 14 senior executives in each of these organizations. Four hundred and six questionnaires were returned, but only 329 complete ones were used for analysis. The results from the study showed the following: that there is poor management of human capital in the Nigerian telecommunications industry; that lack of effective communication appears to be the bane of structural capital management in the industry; that most of the telecommunications companies in Nigeria have had a long‐term relationship with their customers; and that Nigerian telecommunications organizations are familiar with knowledge management as a concept. The results also showed slight differences among the six groups of organizations in their management of intellectual capital with the Local Exchange operators and National Carrier as the best and worst performers, respectively. In conclusion, it is suggested that Nigerian telecommunications organizations should strive to provide a conducive and an enabling working environment, where people can share ideas about work without being shut down by bosses and bureaucrats, and that they should try harder to implement their customers' suggestions, especially when such suggestions have to do with meeting the customers' needs. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".