Enabling innovation in information technology outsourcing : an empirical study
Bibliographic record
Abstract
Information technology outsourcing has become a pervasive and important phenomenon in business organizations and there is substantial evidence about its benefits and pitfalls. Initially, firms used outsourcing as a way to lower costs, gain access to expertise and focus on core activities. Recently, there is a shift in focus and more firms are outsourcing to attain innovative products and services. However, current research is still unclear about how innovation can be achieved through outsourcing. Drawing predominantly from the dynamic capability theory, the objective of this paper is to explore how absorptive capacity unfolds as a process within and between firms when client firms outsource their information technology services with expectations of innovation generation. In this paper, we propose a research model that links absorptive capacity to innovation generation. We draw on three case studies to focus on how absorptive capacity, as a process, impacts innovation generation. Results show that assimilation and transformation stages are critical in generating radical innovation while acquisition and exploitation play a key role in incremental innovation. The implications of these findings for both researchers and practitioners are discussed.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".