Mixed Methods Utilisation in Innovation Management Research: A Systematic Literature Review and Meta-Summary
Bibliographic record
Abstract
The main purpose of this article is to explore the application of mixed methods research in the innovation management sub-discipline utilizing a systematic literature review and meta-summary analysis. Regardless of the growing number of studies in innovation management there is still a lack of research that integrates and synthesizes this body of knowledge. Our review of 93 articles from Web of Science and Scopus databases, including content analysis, presents trends and research background in innovation management that use the mixed methods approach. This study addresses the inconsistencies in the literature and presents a holistic picture of what existing empirical studies have found to date. In addition, we have developed an innovation management model based on selected theoretical lenses to enable future researchers in a given area to choose the appropriate method. The results of the meta-summary show that 50.54% articles from our dataset are related to partially mixed dominant sequential methods, 12.90% fully mixed dominant sequential methods and 11.83% fully mixed dominant concurrent methods. We identified several research gaps and provided a future research avenue in the context of innovation management. The article analyzes empirical papers, enables identification of problems in the current research and identifies trends in the area of the studied phenomenon. The results on the topic of mixed methods in innovation management and used tools have indicated that this issue is still in a premature phase but with an upward trend of research development.
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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 | 0.009 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".