Developing Optimal Distinctive Open Innovation in Private Universities: Antecedents and Consequences on Innovative Work Behavior and Employee Performance
Bibliographic record
Abstract
The purpose of this study is to analyze the effect of the concept of optimal distinctive open innovation as mediating variable in relationship between Person-Job Fit and Person-Organization Fit and work innovation behavior and lecturer performance. The method used in this study are through Structural Equation Modeling (SEM) analysis with the object of the study conducted on 193 lecturers determined by purposive random sampling technique at private universities in Central Java. The findings showed significant effects of person-organization fit on the optimal distinctive open innovation and on innovative work behavior. Moreover, person-job fit is of significant on optimal distinctive open innovation, and on innovative work behavior. In testing the effect of mediating variables, optimal distinctive open innovation is of significant on innovative work behavior which in turn affecting the significant influence of innovative work behavior on lecturer performance. The findings emphasize that the success-oriented way of thinking requires the expertise of employees to always create creative, superior and unique ideas. Private universities must always pay attention to the principles of industrial management and professionalism in human resource management, in order to survive and develop. Superior skills will produce superior performance, and superior skills are distinctive competence that supports the company to achieve positional advantage.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".