Knowledge creation processes in small innovative hi‐tech firms
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore knowledge creation processes in small innovative hi‐tech firms operating in the software industry. Design/methodology/approach The research framework examines specific action and interaction processes aiming at creating knowledge. This exploratory research is constituted by five case studies, each of them being represented by a small Canadian software firm. Analysis draws upon four sources of data. A total of 15 interviews (three per case) had been conducted and subsequently transcribed and coded using qualitative software – Nvivo 07 . Findings The results of the study reveal that interaction processes permitting the creation of knowledge in small hi‐tech firms can take place via: formal meetings; informal communities; project teams; external interaction; and information technology‐tools. Rapid prototyping represents the kernel activity of knowledge creation through action. Details of the results, implications of the findings, and conclusions are presented and discussed. Research limitations/implications This paper is based on a limited number of case studies, therefore empirical results cannot be generalized. Future research on larger samples of small Canadian software firms is needed, using the same eligibility criteria and comparing the same knowledge creation processes as those explored in this study. Other promising avenues of inquiry include such questions as the way small knowledge‐based firms operating in turbulent environments organize internally to create knowledge, the conditions enabling the generation of knowledge, and the particular “spaces” in which knowledge creation occurs in these firms. Practical implications The systematic description and comparison of knowledge creation processes in each explored company contribute to the better understanding of specific “interaction” and “action” processes through which knowledge is generated, enabling practitioners in small innovative hi‐tech firms to design appropriate policies and procedures for enhancing knowledge creation behaviors of their employees. Originality/value This research is among the first and most exhaustive exploratory and comparative studies carried out in the Canadian context of small firms operating in the software industry.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".