Exploring Companies’ Innovation Policies in the Industrial Sector in Central and Eastern Europe
Bibliographic record
Abstract
Purpose: Despite the importance of innovation, the full innovation potential of companies operating in the industrial sector of Central and Eastern Europe (CEE) seems not to have been unlocked yet.Thus, the primary purpose of the study was to explore the key elements of company innovation policies applied on the way to successful innovation. Methodology:The study is based on qualitative methods.The aim of the study has been achieved through 24 semi-structured interviews conducted with senior management, project leaders, and R&D specialists employed at companies operating in the industrial sector in CEE.The time frame covers the period of the fourth quarter of 2016 and the first quarter of 2017.Findings: Managing disruption consists of focusing on innovation development stage and following market imperatives by making the innovation try to address the market needs.Balancing portfolio requires considering product and process innovation jointly.Furthermore, 62% of the interviewees say that breakthrough innovation results ultimately from numerous incremental advancements.As far as policy integration is concerned, achieving competitive advantage through internal research is common amongst technological leaders, while market contenders turn to external cooperation.Moreover, incorporating CSV principles into the concept of innovation policy appears to be a necessity.Managing intangibilities comes down to patents. Research limitations:The research was burdened with such limitations as respondents experiencing time pressure and the use of only one source of information (the interviewees).Originality: Despite much general evidence, the study attempts to complement the rare quali tative studies on innovation in CEE.It was carried out as a response to the lack of an in-depth study covering such recurrent challenges in the field of company innovation policies as disruption, portfolio balancing, integration, intangibilities' management, and play.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".