Location decision-making of 'creative' industries: the media and computer games sectors in Dublin, Ireland

There has been significant research undertaken examining the “creative class” thesis within the context of the locational preferences of creative workers. However, relatively little attention has been given to the locational preferences of creative companies within the same context. This paper reports on research conducted to qualitatively analyse the location decision making of companies in two creative sectors: media and computer games. We address the role of the so-called “hard” and “soft” factors in company location decision making within the context of the creative class thesis, which suggests that company location is primarily determined by “soft” factors rather than “hard” factors. The study focuses upon “core” creative industries in the media and computer game sectors and utilises interview data with company managers and key elite actors in the sector to investigate the foregoing questions. The results show that “hard” factors are of primary importance for the location decision making in the sectors analysed, but that “soft” factors play quite an important role when “hard” factors are satisfactory in more than one competing city-region.

Towards a knowledge-based approach for effective decision-making in railway safety

Purpose – This paper aims to contribute towards understanding how safety knowledge can be elicited from railway experts for the purposes of supporting effective decision-making. Design/methodology/approach – A consortium of safety experts from across the British railway industry is formed. Collaborative modelling of the knowledge domain is used as an approach to the elicitation of safety knowledge from experts. From this, a series of knowledge models is derived to inform decision-making. This is achieved by using Bayesian networks as a knowledge modelling scheme, underpinning a Safety Prognosis tool to serve meaningful prognostics information and visualise such information to predict safety violations. Findings – Collaborative modelling of safety-critical knowledge is a valid approach to knowledge elicitation and its sharing across the railway industry. This approach overcomes some of the key limitations of existing approaches to knowledge elicitation. Such models become an effective tool for prediction of safety cases by using railway data. This is demonstrated using passenger–train interaction safety data. Practical implications – This study contributes to practice in two main directions: by documenting an effective approach to knowledge elicitation and knowledge sharing, while also helping the transport industry to understand safety. Social implications – By supporting the railway industry in their efforts to understand safety, this research has the potential to benefit railway passengers, staff and communities in general, which is a priority for the transport sector. Originality/value – This research applies a knowledge elicitation approach to understanding safety based on collaborative modelling, which is a novel approach in the context of transport.