Marketing and technology strategies for innovative performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine organizational project management (OPM) as an integrative mechanism to mediate marketing and technology strategies for innovative performance. In addition, the moderating effects of firm size and turbulence on the relationships between marketing strategy, technology strategy, OPM, and innovative performance are examined. Design/methodology/approach The authors used empirical data derived from a survey of 5,000 firms worldwide in fast‐paced R&D intensive sectors. Respondents were typically chief technology officers or senior R&D managers. Fisher test and moderated regression analysis were applied on 715 usable questionnaires. Findings Evidence is found that OPM has a positive effect on innovative performance; and intervenes in the relationship between both strategies and innovative performance. The results also show some moderating effects of turbulence. Practical implications Marketing and technology strategies impact innovative performance, but part of this influence is established through OPM. Thus, OPM appears to be a good vehicle to translate strategies into concrete results. Project management can no longer be viewed as just a tool. Instead, OPM should be viewed as a decentralized, distributed function that is not innovative as such, but which supports innovation. Originality/value To date, the research has not explored OPM as an alternative whereby firms can integrate marketing and technology strategies to drive innovative performance, even if the firm's ability to generate a stream of innovations has become increasingly important. Therefore, probing the OPM links become an interesting search.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".