Technological collaboration in the korean electronic parts industry: patterns and key success factors
Bibliographic record
Abstract
This study empirically examines different patterns of collaborative technological development projects and the key success factors (KSFs), using data from 82 projects in the Korean electronic parts industry. The patterns of technological collaboration were categorized into 4 types by two contingency variables: development motive (technology–push or market–pull) and source of initiation (focal firm– or partner–initiated). The bivariate relationships revealed that project characteristics (technological complexity, demand certainty, and financial support of the government), partner characteristics (the level of trust with partners), and collaborative management practices (commitment of the focal firm, and information sharing in the collaboration process) appear to be different depending on the two contingency variables. Each type of collaborative R&D project also had different KSFs. While diverse characteristics like strategic importance, goal compatibility and information sharing with partners are critical for the success of technology–push projects, just only specificity of collaboration process and outcome appears important to the success of demand–pull projects. For focal firm–initiated projects, project characteristics are KSFs, whilst partner characteristics and collaboration management practice are found to be more critical for the success of partner–initiated projects. The findings suggest that the different contingencies such as development motive and source of initiation bring about different patterns of collaborative technology development projects, which in turn lead to a different set of KSFs, since different information, roles of partners and the focal firms, and collaboration management practices are needed to successfully implement the different types of projects. Based on empirical results, this study discusses managerial, policy, and theoretical implications for the collaborative R&D activities taking place in the Korean electronic parts industry.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".