Typology and Success Factors of Collaboration for Sustainable Growth in the IT Service Industry
Bibliographic record
Abstract
Recently, innovative changes in information technology (IT) trends, such as cloud computing and deep learning, have led IT companies to focus on collaboration for sustainable growth. This paper investigates collaboration strategies and success factors for IT service companies via a survey-based empirical study of Korean leading IT firms. Four types of collaboration were identified by considering the types of customer relationship and the target market: offshore, joint venture, collaboration with small and medium-sized enterprises (SMEs), and partnership with major local firms. Then, based on a Plan-Do-See management activity process, this paper considers success factors in the planning process and collaboration process, and analyzes an impact of these factors on collaboration performance such as financial performance, process innovation, improving competitiveness, and technology acquisition. As a result, the success factors differ according to the types of performance measures as well as the collaboration types. In particular, the characteristics of partners positively influence competitiveness in captive and global markets, while they improve process innovation in open and domestic markets. This study attempts to provide insight for companies in the IT service industry about how collaboration activities could enhance performance, depending on the alliance types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".