Human resources perspectives on the management of conferences as events in Kenya.
Bibliographic record
Abstract
This chapter explores the nature of the Kenyan event industry, and in particular its human resource management practices. Interviews were conducted of two event organizers and, owing to a request for anonymity, Company A and Company B shall be used to discuss the results. Findings show that Kenya does not yet have a mature and extensive event industry. However, there is evidence that the industry is being developed at a high rate. Research into human resource management and other aspects of events management is also lacking. The market is not saturated as yet, and hence the future of the market depends on matching the needs of the organizers with the provision of appropriate venue facilities and services. It is argued that a review of literature points to need for a comprehensive and integrated human resource management strategy, encompassing innovative recruitment, selection, training, development and performance management techniques, which will effectively contribute to the events industry productivity and profitability. The event industry in Kenya appears to be characterized by: a culture of 'casualization', like its international competitors; significant gender imbalances; and a largely transient workforce, which is relatively underpaid and underrepresented by industry unions. As a highly labour-intensive industry, it is also disproportionately expensive, at times less productive or profitable than its counterparts. Human resource management practices remain fragmented and short-term oriented, and without direct significance to overall events productivity. Recent developments in the establishment of a plethora of tourism training institutions and colleges, coupled with portable national competency standards for a variety of hospitality and tourism occupations, will no doubt ensure a skilled pool of potential employees in the area of concern. Linking such training to individual occupational criteria and supplementing it with thorough orientation programmes, on-the-job training modules and appropriate supervision will no doubt fill this previous gap in human resource management for the event management industry. Owing to the continued growth of conference and meeting events in Kenya, the organizing clientele will have to become more experienced and, as a result, demand more specific responses to their needs. The quality of provision and value for money are, therefore, key factors that event organizers in Kenya must recognize if they are to sustain and increase their market share.
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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 | 0.001 | 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) | 0.001 | 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 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".