Corporate foresight for strategic innovation management: the case of a Russian service company
Bibliographic record
Abstract
Purpose This paper aims to provide a detailed case study of a corporate foresight for innovation (CFI) project done by the Higher School of Economics’ (HSE) (Moscow, Russia) corporate foresight (CF) unit for a large state-owned Russian service company. It demonstrates how CFI methods lead to recommendations and how these recommendations result in decisions. Design/methodology/approach Drawing from being part of the project team, review of the project documents and interviews, the case describes a multi-phased CFI project which incorporated several CF methods. Techniques used for the project itself included grand challenges and trend analysis, analysis of best practices through use of benchmarking and horizon scanning, interviews, expert panels, wild card and weak signals analysis, cross impact analysis, SWOT and backcasting. The project used a broad-base of secondary information, expert panels consisting of company experts and HSE CF team personnel, interviews with senior management and an extensive literature review using HSE’s propriety iFORA system. Findings In all 17 CFI recommendation and over 100 implementation recommendations were made; 94 per cent of the CFI recommendations were accepted with most implemented at the time this case was written. The case also identifies five enabling factors that collectively both helped the CFI project and led to a high rate of recommendation acceptance and one factor that hindered CFI project success. Practical implications The case study provides detailed information and insight that can help others in conducting CF for innovation projects and establishes a link between CF methods and innovation-based recommendations and subsequent decisions. Originality/value In-depth case studies that show academe and practitioners how CFI leads to recommendations and is linked to subsequent decisions have been identified as a gap in the literature. This paper therefore seeks to address this need by presenting a detailed CF case for a corporate innovation project.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".