Negative aspects of counter-knowledge on absorptive capacity and human capital
Bibliographic record
Abstract
Purpose – People live and work in a world where they do not have complete knowledge and, as a result, they make use of rumours, beliefs and assumptions about relevant areas of concern. The term counter-knowledge has been used to refer to knowledge created from unverified sources. The purpose of this paper is to examine the relationship between counter-knowledge and human capital (HC) as well as investigating interactions between absorptive capacity (ACAP) and HC. Design/methodology/approach – A model is tested to examine the relationship between counter-knowledge, HC and the financial performance of 112 companies listed on the Spanish Stock Exchange. Findings – The results are calculated using structural equation modelling. This leads to the main conclusion that while the increasing presence of counter-knowledge leads to a reduction of ACAP and, by extension with HC. However, in the context of the sample, HC has positive effects on firms’ performance. Therefore, consideration must be given to the evaluation of the real cost of counter-knowledge or inappropriate assumptions on HC. Practical implications – The key managerial implication of this paper is that management should actively develop an organizational culture which questions the source of any knowledge and favours evidence-based reasoning over reasoning based on “gut instinct”, what has worked in the past and reasoning based on rumours and gossip. Originality/value – This paper provides empirical support for the argument that the all so-called “knowledge” generated from the sharing of unverified news is not necessarily good knowledge. Rumours or gossip shared thanks to unverified sources are some examples that illustrate people possibility to create inappropriate or false beliefs via unsupported explanations and justifications.
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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 | 0.004 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".