The effects of IT use intensity and innovation culture on organizational performance: the mediating role of innovation intensity
Bibliographic record
Abstract
Purpose This study aims to investigate innovation intensity by exploring the roles of internally focused and externally focused information technology (IT) use intensity and innovation culture on innovation intensity and organizational performance. Design/methodology/approach A model exploring the effects of internally and externally focused IT use, plus two key dimensions of innovation culture – collaborative and entrepreneurial – on innovation intensity and organizational performance is tested via a structural equation model using partial least squares with data collected from 395 top executives. Findings The results indicate that intense use of internally and externally focused IT and the collaborative dimension of culture positively affect innovation intensity, which, in turn, increases operational and financial performance. Practical implications Innovation is an important driver of performance, for both internal efficiency and competitiveness. The role of IT in the innovation process is key: it allows information, knowledge and idea sharing. Top managers should make a wide array of IT tools available to increase internal and external information exchanges. They should also develop an organizational context that stimulates innovativeness and promotes collaboration. Originality/value IT helps employees acquire and use the knowledge needed to innovate within and outside organizational boundaries. To be innovative, employees need to work in an organization with a strong innovation culture, a primary determinant of innovation intensity. This study is one of the first to examine the effects of an organization’s innovation culture and its use of IT on innovation intensity and organizational performance. In addition, constructs of innovation intensity and internally and externally focused IT use are developed and tested.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".