The Impact of Emerging Technologies on Knowledge Management in Organizations
Bibliographic record
Abstract
Man versus the machine, it is a new and growing controversy that is irking those who criticize the increased use of technology, while considered to be extremely exciting news for supporters and advocators of the growing technologies in western countries. Supporters of the rapid technological advancement have argued that it has definitely increased productivity, and improved the economy. On the other hand, the opposing side has argued that such technologies are destroying jobs more than it is generating new ones. This paper examines the impact of growing technologies as depicted by the introduction of Watson the supercomputer, which represents the epitome of technological advancement, on knowledge management in organizations, by focusing on three important components that shape any business; people as identified by managers and employees, organizational processes, and the emerging technologies. The paper proposes a framework for managing knowledge and knowledge workers as well as proposing an adapted prototype design of how the future digitized organizational structure should be, and offers recommendations in this regard.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".