Effects of knowledge management and financial attraction on new technology-based firm performance
Bibliographic record
Abstract
Purpose This study aims to examines how knowledge management (KM) within new technology-based firms (NTBFs) affects venture teams’ financial attraction (FA) and performance (P), contributing to NTBFs’ survival in highly competitive environments. The large socio-economic impact of NTBFs contrasts with high mortality rates, prompting interest in factors influencing long-term success. Design/methodology/approach This study employed a PLS-SEM model using data from the Global Accelerator Learning Initiative (GALI) of Emory University (2013–2019) to analyze 103 NTBFs from Australia, Canada, France, Germany and the USA. Findings The results confirm significant relationships between KM, FA and NTBF performance, underscoring social capital as a key aspect of KM. Practical implications NTBF venture teams should prioritize building strong networks while acquiring business debt to enhance KM and attract funding. Policymakers must promote KM, encourage collaboration and facilitate access to business credit lines for these firms. Originality/value The findings extend the understanding of these relationships by focusing on venture teams within NTBFs, demonstrating the signaling role of debt financing in attracting investment. The study highlights the crucial role of social capital in KM while indicating that debt may be a stronger signal of viability in specific contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".