Profiling business support provision for small, medium and micro-sized enterprises in London’s fashion sector

The primary aim of this paper is to build a profile of the business support landscape that exists for fashion SMEs (small and medium-sized enterprises) and MSEs (micro-sized enterprises) in London. In the face of multiple challenges, fashion sector SME/MSEs benefit from the services provided by business support organisations. We have identified 21 fashion support organisations that exist in London. They can be broadly divided into two types of business support organisations: fashion incubators and partial-support organisations, both of which play an equally important role in the sector

Hackathons: Why Co-Location?

This research was supported by the Arts and Humanities Research Council [grant Number AH/J005142/1].This research was supported by the Arts and Humanities Research Council [grant Number AH/J005142/1].This research was supported by the Arts and Humanities Research Council [grant Number AH/J005142/1].This research was supported by the Arts and Humanities Research Council [grant Number AH/J005142/1].In this position paper we outline and discuss co-location as a significant catalyst to knowledge exchange between participants for innovation at hackathon events. We draw on surveys and empirical evidence from participation in such events to conclude that the main incentives for participants are peer-to-peer learning and meaningful networking. We then consider why co-location provides an appropriate framework for these processes to occur, and emphasize the needs for future research in this area

influence.ME: tools for detecting influential data in mixed effects models

influence.ME provides tools for detecting influential data in mixed effects models. The application of these models has become common practice, but the development of diagnostic tools has lagged behind. influence.ME calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook’s distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while accounting for the nesting structure of the data. The package and measures of influential data\ud are introduced, a practical example is given, and strategies for dealing with influential data are suggested