Innovation diffusion across firms

This chapter seeks to add to the study of innovation diffusion as enacted within the UK construction sector. Whereas using relevant theoretical frames as touch points, the chapter maps out challenges associated with understanding innovation diffusion within the UK construction sector. Central to the argument developed here is just how diverse the UK construction sector is, resulting in the need to focus upon a specific constituent perspective within the sector. It is argued that constituents of the UK construction sector experience the reality of innovation diffusion differently. The chosen focus here is medium-size and typically regionally based construction firms rather than the big guns, because statistics continually demonstrate that this group of smaller firms undertake more than 80% of the sector’s output. As is pointed out in other chapters in the present volume, and as argued theoretically in the industrial network perspective of chapter 7, firms do not innovate in a vacuum. Innovation and diffusion occur within networks of firms typically around a project. A framework drawing upon empirical data is provided to additional insight on the process and the interconnections. It is argued here that the unit of analysis or level of understanding termed the firm can actually be fairly unhelpful for understanding innovation and its manifestation and diffusion within the broader UK construction sector, because this occurs across networks of firms.

Diffusion of digital innovation in construction: a case study of a UK engineering firm

The UK government is mandating the use of building information modelling (BIM) in large public projects by 2016. As a result, engineering firms are faced with challenges related to embedding new technologies and associated working practices for the digital delivery of major infrastructure projects. Diffusion of innovations theory is used to investigate how digital innovations diffuse across complex firms. A contextualist approach is employed through an in-depth case study of a large, international engineering project-based firm. The analysis of the empirical data, which was collected over a four-year period of close interaction with the firm, reveals parallel paths of diffusion occurring across the firm, where both the innovation and the firm context were continually changing. The diffusion process is traced over three phases: centralization of technology management, standardization of digital working practices, and globalization of digital resources. The findings describe the diffusion of a digital innovation as multiple and partial within a complex social system during times of change and organizational uncertainty, thereby contributing to diffusion of innovations studies in construction by showing a range of activities and dynamics of a non-linear diffusion process.

The diffusion of grassroots innovations for sustainability in Italy and Great Britain: an exploratory spatial data analysis

Little research so far has been devoted to understanding the diffusion of grassroots innovation for sustainability across space. This paper explores and compares the spatial diffusion of two networks of grassroots innovations, the Transition Towns Network (TTN) and Gruppi di Acquisto Solidale (Solidarity Purchasing Groups – GAS), in Great Britain and Italy. Spatio-temporal diffusion data were mined from available datasets, and patterns of diffusion were uncovered through an exploratory data analysis. The analysis shows that GAS and TTN diffusion in Italy and Great Britain is spatially structured, and that the spatial structure has changed over time. TTN has diffused differently in Great Britain and Italy, while GAS and TTN have diffused similarly in central Italy. The uneven diffusion of these grassroots networks on the one hand challenges current narratives on the momentum of grassroots innovations, but on the other highlights important issues in the geography of grassroots innovations for sustainability, such as cross-movement transfers and collaborations, institutional thickness, and interplay of different proximities in grassroots innovation diffusion.

An LDA and probability-based classifier for the diagnosis of Alzheimer's Disease from structural MRI

In this paper a custom classification algorithm based on linear discriminant analysis and probability-based weights is implemented and applied to the hippocampus measurements of structural magnetic resonance images from healthy subjects and Alzheimer’s Disease sufferers; and then attempts to diagnose them as accurately as possible. The classifier works by classifying each measurement of a hippocampal volume as healthy controlsized or Alzheimer’s Disease-sized, these new features are then weighted and used to classify the subject as a healthy control or suffering from Alzheimer’s Disease. The preliminary results obtained reach an accuracy of 85.8% and this is a similar accuracy to state-of-the-art methods such as a Naive Bayes classifier and a Support Vector Machine. An advantage of the method proposed in this paper over the aforementioned state of the art classifiers is the descriptive ability of the classifications it produces. The descriptive model can be of great help to aid a doctor in the diagnosis of Alzheimer’s Disease, or even further the understand of how Alzheimer’s Disease affects the hippocampus.

A Genetic Algorithm for the selection of structural MRI features for classification of Mild Cognitive Impairment and Alzheimer's Disease

This work investigates the problem of feature selection in neuroimaging features from structural MRI brain images for the classification of subjects as healthy controls, suffering from Mild Cognitive Impairment or Alzheimer’s Disease. A Genetic Algorithm wrapper method for feature selection is adopted in conjunction with a Support Vector Machine classifier. In very large feature sets, feature selection is found to be redundant as the accuracy is often worsened when compared to an Support Vector Machine with no feature selection. However, when just the hippocampal subfields are used, feature selection shows a significant improvement of the classification accuracy. Three-class Support Vector Machines and two-class Support Vector Machines combined with weighted voting are also compared with the former and found more useful. The highest accuracy achieved at classifying the test data was 65.5% using a genetic algorithm for feature selection with a three-class Support Vector Machine classifier.