Mixed Methods Utilisation in Innovation Management Research: A Systematic Literature Review and Meta-Summary

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.

Exchangeable Generative Models with Flow Scans

In this work, we develop a new approach to generative density estimation for exchangeable, non-i.i.d. data. The proposed framework, FlowScan, combines invertible flow transformations with a sorted scan to flexibly model the data while preserving exchangeability. Unlike most existing methods, FlowScan exploits the intradependencies within sets to learn both global and local structure. FlowScan represents the first approach that is able to apply sequential methods to exchangeable density estimation without resorting to averaging over all possible permutations. We achieve new state-of-the-art performance on point cloud and image set modeling.

Газофазная эпитаксия слоев AlN на темплейте AlN/Si(111), синтезированном методом реактивного магнетронного распыления

Epitaxial layers of AlN were grown on a Si(111) substrate using several sequential methods: reactive magnetron sputtering (up to a thickness of 20 nm), MOCVD (up to a thickness of 450 nm), and HVPE (up to a thickness of 2 microns).The formation of AlN by this combined method provides a significant reduction in layer deformation and suppression of crack formation.