Information Processing and Firm‐Internal Environment Contingencies: Performance Impact on Global New Product Development
Bibliographic record
Abstract
Innovation in its essence is an information processing activity. Thus, a major factor impacting the success of new product development (NPD) programs, especially those responding to global markets, is the firm's ability to access, share and apply NPD information, which is often widely dispersed, functionally, geographically and culturally. To this end, an IT‐communication strength is essential, one that is nested in an internal organizational environment that ensures its effective functioning. Using organizational information processing (OIP) theory as a framework, superior global NPD program performance is shown to result from an effective IT/Communication strength and the commitment components of the firm's internal environment, which are hypothesized to moderate this relationship. IT/Communication strength is identified in this study in terms of two components including the IT/Comm Infrastructure and IT/Comm Capability of the firm, whereas the moderating internal environment of the firm incorporates Resource Commitment and Senior Management Involvement. Data from a major empirical study of international NPD programs (382 SBUs) are used to develop and test this model. Based on a hierarchical regression analysis, the results are substantially supportive, with some unexpected findings. These shed light on the complex relationships of the firm's internal environment, OIP competency, and global NPD program performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".