Building trust in multi-stakeholder collaborations for new product development in the digital transformation era
Bibliographic record
Abstract
Purpose The study aims to explore the critical approaches adopted by innovative organizations and to build an environment of trust between the multiple stakeholders collaborating for new product development (NPD). Design/methodology/approach A qualitative research approach is adopted in this study. Fifteen semi-structured interviews were conducted with experts from the wood product industry in Quebec, Canada. These organizations have successfully adopted the latest technological developments and have developed a strong collaboration with their stakeholders. Findings The study identified eleven strategies for the innovative organizations that included early involvement of the stakeholders in the design process, developing long-term relationships and fostering a transparent environment using Industry 4.0 technologies. A novel framework for using this strategy is presented with three stages of application, namely, planning, enactment and review. Practical implications Inter-organizational collaborations in NPD are more successful when imbued with trust. The NPD strategies must allow innovative organizations to achieve a balanced ecosystem in which value created through the adaption of new technology can be thoroughly captured through commercialization on time with no field failure. Originality/value The study adds to the body of knowledge in stakeholder theory and NPD research and practice.
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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 | 0.049 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.014 | 0.014 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".