Social product development: a systematic review and reference model
Bibliographic record
Abstract
Social Product Development (SPD) uses social technologies to engage diverse stakeholders, yet the current literature on the topic remains fragmented, lacking a comprehensive model to guide its implementation. This study aims to address this gap by conducting a systematic literature review to develop a comprehensive SPD reference model. A total of 72 articles from the Web of Science database (2014–2023) were analysed. The analysis identifies the core components of the SPD ecosystem (i.e. activities, tenants, actors, and mechanisms) and sixteen key influencing factors. These factors are ranked by frequency to highlight their relative importance, with user knowledge and social network ties emerging as the most prominent. To the best of the authors' knowledge, this is the first study to systematically derive these elements and synthesise them into a cohesive model. The resulting framework provides a robust theoretical foundation for future research and offers those involved in product development with a practical roadmap, with clear guidance on leveraging the most critical factors to enhance collaborative innovation and improve produ`ct development outcomes.
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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 | 0.057 | 0.157 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.080 | 0.054 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".