Commercial fashion designers are usually focused on developing designs using color, fabric and style lines, whereas artistic fashion designers whose primary goal is innovation are…
Abstract
Commercial fashion designers are usually focused on developing designs using color, fabric and style lines, whereas artistic fashion designers whose primary goal is innovation are keen on developing designs by means of a peculiar manner of presentation or a new conception of fabric. The aims of this research are to analyze the potential for developing novel methods of generating material and create new fabrics that are useful for both fashion and decorative art, as well as explore the potential uses with the newly generated fabrics. Acknowledging that fabric is a crucial element for fashion, this researcher set about investigating the potential use of sewing thread for creating fabric. This study documents a personal experimentation with a new dimension whereby fabric can be generated from overstitched threads using a variety of techniques; non-woven, patterned non-woven, interweaving, knitting, and embroidering. This study demonstrates not only the possibility of approaching artistic fashion by manipulating overstitching, but also the feasibility of conducting a fashion design process in which product aesthetics is generated from yarn texture, fabric structure, and garment construction. Thus, this study contributes to developing an approach in which overstitching and fashion are merged to integrate a new dimension of creation.
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Maite Tapia, Manfred Elfström and Denisse Roca-Servat
In this paper, we draw from our own empirical data on worker organizing and identify important concepts that bridge social movement (SM) and industrial relations (IR) theory. In a…
Abstract
In this paper, we draw from our own empirical data on worker organizing and identify important concepts that bridge social movement (SM) and industrial relations (IR) theory. In a context of traditional union decline and a surge of alternative types of worker mobilization, we apply SM and IR concepts related to the mobilizing structures and culture to cases of labor organizing via worker centers and community–labor alliances in the United States and China. From an analytical perspective, we argue that the field of SMs and IR can both benefit from this type of cross-discipline theorization.
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Craig Allen Talmage, Jocelyn Bell and Gheorghe Dragomir
This paper aims to extend social entrepreneurship theory by investigating the darker sides of innovation and enterprise. Entrepreneurship and social entrepreneurship theories…
Abstract
Purpose
This paper aims to extend social entrepreneurship theory by investigating the darker sides of innovation and enterprise. Entrepreneurship and social entrepreneurship theories regarding shifting equilibriums are considered alongside other traditions. This research presents how individuals see enterprises as dark and light and discusses how such perceptions are important to building emerging theories of light and dark social entrepreneurship.
Design/methodology/approach
The study uses a survey of public perceptions (n = 631) regarding the social and economic impact of a total of 15 different enterprises to create a map of the darker variations of enterprises. An 11-point scale was used to evaluate perceived impact.
Findings
The mapping of each enterprise on a coordinate plane resulted in four thematic areas: traditional enterprises (light social, light economic), taboo enterprises (dark social, light economic), dark enterprises (dark social, dark economic) and alternative enterprises (light social, dark economic). Some enterprises crossed between the thematic areas.
Research limitations/implications
This study opens up new directions for research on dark social entrepreneurship and research on enterprises that influence social equilibriums.
Practical implications
This study provides guidance for practitioners and policymakers to better understand phenomena such as dark, taboo and alternative enterprises and their nuances.
Social implications
This study allows for a broader look at social entrepreneurship, innovation and enterprise to better understand dark and light nuances. Similarities between the lighter and darker forms of enterprises are noted.
Originality/value
This study builds on dark entrepreneurship and dark social entrepreneurship theories and concepts using empirical methods.
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Loretta Bortey, David J. Edwards, Chris Roberts and Iain Rillie
Safety research has focused on drivers, pedestrians and vehicles, with scarce attention given to highway traffic officers (HTOs). This paper develops a robust prediction model…
Abstract
Purpose
Safety research has focused on drivers, pedestrians and vehicles, with scarce attention given to highway traffic officers (HTOs). This paper develops a robust prediction model which enables highway safety authorities to predict exclusive incidents occurring on the highway such as incursions and environmental hazards, respond effectively to diverse safety risk incident scenarios and aid in timely safety precautions to minimise HTO incidents.
Design/methodology/approach
Using data from a highway incident database, a supervised machine learning method that employs three algorithms [namely Support Vector Machine (SVM), Random Forests (RF) and Naïve Bayes (NB)] was applied, and their performances were comparatively analysed. Three data balancing algorithms were also applied to handle the class imbalance challenge. A five-phase sequential method, which includes (1) data collection, (2) data pre-processing, (3) model selection, (4) data balancing and (5) model evaluation, was implemented.
Findings
The findings indicate that SVM with a polynomial kernel combined with the Synthetic Minority Over-sampling Technique (SMOTE) algorithm is the best model to predict the various incidents, and the Random Under-sampling (RU) algorithm was the most inefficient in improving model accuracy. Weather/visibility, age range and location were the most significant factors in predicting highway incidents.
Originality/value
This is the first study to develop a prediction model for HTOs and utilise an incident database solely dedicated to HTOs to forecast various incident outcomes in highway operations. The prediction model will provide evidence-based information to safety officers to train HTOs on impending risks predicted by the model thereby equipping workers with resilient shocks such as awareness, anticipation and flexibility.
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Anuoluwapo Ajayi, Lukumon Oyedele, Juan Manuel Davila Delgado, Lukman Akanbi, Muhammad Bilal, Olugbenga Akinade and Oladimeji Olawale
The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of…
Abstract
Purpose
The purpose of this paper is to highlight the use of the big data technologies for health and safety risks analytics in the power infrastructure domain with large data sets of health and safety risks, which are usually sparse and noisy.
Design/methodology/approach
The study focuses on using the big data frameworks for designing a robust architecture for handling and analysing (exploratory and predictive analytics) accidents in power infrastructure. The designed architecture is based on a well coherent health risk analytics lifecycle. A prototype of the architecture interfaced various technology artefacts was implemented in the Java language to predict the likelihoods of health hazards occurrence. A preliminary evaluation of the proposed architecture was carried out with a subset of an objective data, obtained from a leading UK power infrastructure company offering a broad range of power infrastructure services.
Findings
The proposed architecture was able to identify relevant variables and improve preliminary prediction accuracies and explanatory capacities. It has also enabled conclusions to be drawn regarding the causes of health risks. The results represent a significant improvement in terms of managing information on construction accidents, particularly in power infrastructure domain.
Originality/value
This study carries out a comprehensive literature review to advance the health and safety risk management in construction. It also highlights the inability of the conventional technologies in handling unstructured and incomplete data set for real-time analytics processing. The study proposes a technique in big data technology for finding complex patterns and establishing the statistical cohesion of hidden patterns for optimal future decision making.