Open Innovation Implementation in a Public University: Administrator Design, Management, and Evaluation of Participatory Platforms and Programs
Bibliographic record
Abstract
abstract: Public organizations have been interested in tapping into the creativity and passion of the public through the use of open innovation, which emphasizes bottom-up ideation and collaboration. A challenge for organizational adoption of open innovation is that the quick-start, bottom-up, iterative nature of open innovation does not integrate easily into the hierarchical, stability-oriented structure of most organizations. In order to realize the potential of open innovation, organizations must be willing to change the way they operate. This dissertation is a case study of how Arizona State University (ASU), has adapted its organizational structure and created unique programming to incorporate open innovation. ASU has made innovation, inclusion, access, and real world impact organizational priorities in its mission to be the New American University. The primarily focus of the case study is the experiential knowledge of administrative leaders and administrative intermediaries who have managed open innovation programming at the university over the past five years. Using theoretical pattern matching, administrator insights on open innovation adoption are illustrated in terms of design stages, teamwork, and ASU's culture of innovation. It is found that administrators view iterative experimentation with goals of impact as organizational priorities. Institutional support for iterative, experimental programming, along with the assumption that not every effort will be successful, empowers administrators to push to be bolder in their implementation of open innovation. Theoretical pattern matching also enabled a detailed study of administrator alignment regarding one particular open innovation program, the hybrid participatory platform 10,000 Solutions. Creating a successful and meaningful hybrid platform is much more complex than administrators anticipated at the outset. This chapter provides administrator insights in the design, management, and evaluation of participatory platforms. Next, demographic assessment of student participation in open innovation programming is presented. Demographics are found to be reflective of the university population and provide indicators for how to improve existing programming. This dissertation expands understanding of the task facing administrators in an organization seeking to integrate open innovation into their work.
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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 | 0.065 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".