Identifying tangible costs, benefits and risks of an investment in intellectual capital
Bibliographic record
Abstract
Purpose The use of contingent knowledge workers may be an efficient means of investing in an organization's intellectual capital. However, exposing contingent workers to private, key competitive knowledge is considered risky. A study was undertaken to collect the costs, benefits and losses experienced by organizations that had contracted contingent knowledge workers to develop intellectual capital. Design/methodology/approach A purposive cross‐section of senior managers of knowledge‐intensive organizations were interviewed regarding the tangible benefits, costs, perceived risks, and experienced losses from contingent knowledge worker arrangements. The constant comparison method of analysis was used. Findings The data revealed perceived increases in flexibility, expertise, creative stimuli, and knowledge bank development. These benefits were believed to have bottom‐line impact through product and process improvements and innovations, and operational efficiencies. The managers did not perceive much risk or experience material losses as a result of the contingent knowledge worker arrangements. Research limitations/implications These findings are based on interviews with a small group of organizations. Although not generalizable, they present an interesting contrast to previous researchers’ conclusions regarding the use of contingent knowledge workers. Further empirical work is needed to test the degree to which this study's findings can be generalized. Practical implications Contrary to recent literature, this study suggests that contracting contingent knowledge workers to develop in‐house intellectual capital is worth the risk. Originality/value The study presents a divergent viewpoint on the contracting of contingent knowledge workers. It also initiates research on rational evaluation of investments in intellectual capital, which constitutes an important contribution to the area of knowledge management. It also contributes to the ongoing research on intellectual capital valuation.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".