Managing human resources in South Africa: A multinational firm focus
Bibliographic record
Abstract
Two key developments exert an important influence on the nature of human resource management (HRM) in South Africa (SA). The first is two seemingly conflicting imperatives, sometimes and arguably wrongly juxtaposed: that of developing a high-growth, globally competitive economy with fuller employment and the sociopolitical imperative of redressing past structural inequalities of access to skilled, professional, and managerial positions, as well as ownership opportunities. The first development is the related influences of globalization and multinational corporations (MNCs), information technology, and increased competition, which have become very prominent in postapartheid SA. South Africa has a dual labor market, with a well-developed formal sector employing some 8.5 million workers in standard or typical work and a growing informal labor market. In the case of the formal, knowledge-based economy, the World Wide Web, and increasing communication that the Internet has made possible, has influenced changes at the organizational level. A second development is that these changes and changing patterns of employment are having a dramatic impact on HR policies within organizations. In a knowledge-based economy, organizations rely on knowledge that is embedded deeply in the individual and in the collective subconscious. It is the property of an individual and cannot be taken away from that person (Harrison & Kessels, 2004). He or she would agree to put it in the service of the collective whole, which is known as organizational citizenship behavior (OCB). In technology-driven advanced firms in SA, there are several themes among the various models of citizenship behavior: helping behavior, sportsmanship, organizational loyalty, organizational compliance, initiative, civic virtue, and self-development (Podsakoff, MacKenzie, Paine, & Bachrach, 2000). Many of these themes overlap with the common competencies demanded by advanced MNCs. Thus OCBs rest upon a recognition of mutuality of interest and of responsibility between the organization and the individuals. Increasing globalization and worldwide competition and the knowledge-based economy have their greatest impact on business strategies, process, and practice involving, among others, management of human resources. In this chapter we examine factors influencing the management of human resources in SA and their impact on human resource practices in organizations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".