Interaction design for open innovation platforms: A social exchange perspective
Bibliographic record
Abstract
Abstract We investigate the interaction design preferences of solution seekers and problem solvers on open innovation (crowdsourcing) platforms. Drawing on social exchange theory (SET), we hypothesize that seekers and solvers have different preferences for the configuration of four central interaction design features of a crowdsourcing platform: communication channels, collaboration options, selection of winning submissions, and feedback mechanisms. Based on a conjoint study with 842 respondents, we show conflicting preferences for the configuration of these features, but also find a surprisingly consistent “best” configuration that can balance the individual preferences of both seekers and solvers. In addition, we identify social trust, risk aversion, and the need for cognition as three personal characteristics of individuals in seeker organizations and solvers that influence their preferred configuration of platform design. Our findings help intermediaries operating a crowdsourcing platform to offer nuanced platform interactions that align how individuals in seeker organizations (e.g., project managers) and individual solvers create and capture value in crowdsourcing. Furthermore, we contribute to the micro‐foundations of open innovation by proposing SET as a novel perspective to examine how the expectations and value drivers of all parties involved in a crowdsourcing project can be balanced.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".