Intellectual capital, knowledge management and social capital within the ICT sector in Jordan
Bibliographic record
Abstract
Purpose The purpose of this paper is to empirically investigate the mediating effect of social capital (SC) on knowledge management (KM) and intellectual capital (IC). Design/methodology/approach A conceptual model of the connections between IC, KM, and SC was developed and the posited hypotheses were tested using a survey data set of 281 questionnaires collected from knowledge workers working in 72 information and communications technology companies operating in Jordan. Findings The findings show that knowledge documentation and knowledge transfer emerged as having the strongest effects on IC, followed by knowledge acquisition and knowledge creation, while knowledge application was found to have an insignificant effect. Also, knowledge transfer and knowledge acquisition emerged as the only two significant processes for the development of SC. Moreover, SC was found to partially and significantly mediate the effects of all processes on IC. Practical implications To promote the development of IC, particularly, in a knowledge-intensive business service (KIBS) sector, documentation, transfer, acquisition, and creation of knowledge are especially effective processes. Furthermore, SC can be significantly enhanced through ensuring effective internal knowledge transfer and acquisition practices. Nurturing IC in a knowledge-intensive context can also be significantly enhanced through looking at the firm as a cooperative knowledge-sharing entity, i.e. investing in SC. Originality/value This is the first empirical study that has examined the links among KM processes, SC, and IC in a KIBS sector within an “oil-poor,” “human resource-rich” Arab developing country context.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".