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The notion of creative clusters has become the focus of a growing number of policy initiatives aimed at revitalizing economies by means of creativity. However, despite their prominence in policy discourse, creative clusters are still a 'fuzzy' concept, defined and treated differently in different strands of research. To address these disparities, this paper presents a systematic literature review of creative cluster research (CCR), with the aim of: (1) exploring the state of the art in the field, (2) pointing out some important limitations, and (3) outlining a future research agenda. A total of 355 articles published between 1986 and 2019 were analysed, drawing upon a combination of manual coding, bibliometric analysis, and text mining techniques. This multi-method approach allowed us to provide both a meta-analysis of CCR and an exploration of its thematic content. In so doing, our paper contributes to a comprehensive understanding of how creative clusters have been studied over time, both broadly and in relation to different creative sectors and geographical contexts. Moreover, through the identification of research gaps and boundaries of knowledge in the field, it points to key methodological and conceptual development issues to be addressed in future studies. [ABSTRACT FROM AUTHOR]

This study aims to compare the drivers of clustering of rural and urban creative industries in England. We use pre‐pandemic web‐scraped data from 154,618 creative industry organizations in England, and use a novel technique to identify 71 distinct rural creative "microclusters" of geographically proximate creative firms. We then consider the role of place‐based assets and agglomeration in the presence of microclusters at a micro‐level geography and find that the determinants of microclustering are generally consistent between rural and urban areas. On that basis we argue that policies to support creative clusters may drive rural regional development. [ABSTRACT FROM AUTHOR]

Although ex post evaluation of impact is increasingly common, the extent to which research impacts emerge largely as anticipated by researchers, or as the result of serendipitous and unpredictable processes, is not well understood. In this article, we explore whether predictions of impact made at the funding stage align with realized impact, using data from the UK's Research Excellence Framework (REF). We exploit REF impact cases traced back to research funding applications, as a dataset of 2,194 case–grant pairs, to compare impact topics with funder remits. For 209 of those pairs, we directly compare their descriptions of ex ante and ex post impact. We find that impact claims in these case–grant pairs are often congruent with each other, with 76% showing alignment between anticipated impact at funding stage and the eventual claimed impact in the REF. Co-production of research, often perceived as a model for impactful research, was a feature of just over half of our cases. Our results show that, contrary to other preliminary studies of the REF, impact appears to be broadly predictable, although unpredictability remains important. We suggest that co-production is a reasonably good mechanism for addressing the balance of predictable and unpredictable impact outcomes. [ABSTRACT FROM AUTHOR]

This article investigates whether high-growth firms grow in different ways from other firms. Specifically, we analyze how firms grow along several dimensions (growth of sales, employment, assets, and operating profits) using Structural Vector Autoregressions. Causal relations are identified by using information contained in the (non-Gaussian) growth rate distributions. For most firms, the growth process starts with employment growth, which is then followed by sales growth, then growth of operating profits, and finally growth of assets. In contrast, high growth firms put more emphasis on growth of operating profits driving other dimensions of growth, with employment growth occurring at the end. [ABSTRACT FROM AUTHOR]

Previous studies have identified the factors affecting successful technology commercialization as outcomes of R&D projects. However, most of them have used cross‐sectional data, whereas there is a dearth of literature using longitudinal data analysis. Longitudinal analysis is essential for investigating the characteristics of early‐stage innovative projects due to the inherent time lag between project evaluation and commercialization. Therefore, this study examines the early‐stage project characteristics that can be used as meaningful evaluation criteria for predicting success, particularly in technology commercialization. We collected data on the ex‐ante evaluation results and ex‐post commercialization results of R&D projects pursued by entrepreneurial firms. We then conducted a logistic regression analysis and identified three market‐related factors as significant in driving technology commercialization success in the early stages of technology development: market potential, commercialization plan, and market condition. [ABSTRACT FROM AUTHOR]

This paper seeks to explore whether firms that have received VC investment are subject to the same recessionary pressures as other small firms, or if receipt of VC incurs resilience to macroeconomic turbulence. It examines the survival and exit patterns of UK venture capital investments during the Great Recession of 2008-9, and compares these patterns to the dot-com boom and bust of 1999-2001. Using a dataset of over 5000 investments made by UK Venture Capital Trusts (VCT) from 1995-2009, the paper uses using Cox survival models to examine the impact of macroeconomic turbulence on duration of investment. The paper finds that in contrast to high exit rates during the dot-com period, firms receiving VCT investment were significantly been less likely to exit by failure or other means during the recession of 2008-9. [ABSTRACT FROM AUTHOR]

Decision makers considering policy or strategy related to the development of emerging technologies expect high quality data on the support for different technological options in order to track trends and allocate resources. A natural starting point would be R&D funding statistics. This paper explores the limitations of such aggregated data in relation to the substance and quantification of funding for emerging technologies. Using biotechnology as an illustrative case, we test the utility of a novel taxonomy to demonstrate the endemic weaknesses in the availability and quality of data from public and private sources. Using the same taxonomy, we consider the extent to which tech-mining presents an alternative, or potentially complementary, way to determine support for emerging technologies using proxy measures such as patents and scientific publications. We find that using proxy measures provides additional visibility of technological emergence and suggest these are a useful complement to financial data. [ABSTRACT FROM AUTHOR]

The article focuses on the differences in economic performance can be explained by the extent to which different economies generate the high performance that enables firms to grow and how high performing firms identify opportunities and allocate resources.

An abstract of the article "Make or Break: The Distributions of Causal Impacts of Venture Capital on Firm Performance" by Alex Coad and Josh Siepel is presented.