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Sports garments play an important role in the well-being of an athlete by protecting the wearer from changing environmental conditions and providing a comfortable feel. Clothing requirements have changed in recent years and demand for apparel with a higher comfort performance has been rising. Hence, the purpose of this study is to explore consumers’ expectations and perception of comfort and to examine how different textiles are perceived by consumers to provide useful knowledge that allows to engineer comfort into fabrics and sports garments.

This online survey comprised 292 respondents, classified by sex, age, nationality and physical activity. The respondents were asked a total of 18 questions through the Bristol Online Survey tool to explore expectation, perception and preference of clothing comfort, specifically of sportswear.

Fit and comfort are closely linked together, both forming part of the clothing comfort concept. When purchasing garments online, the haptics of fabrics were identified as a crucial missing parameter. However, priorities of attributes within the concept varied according to the person’s sex and nationality. Women put more emphasis on garment fit and showed a higher need for tactile input, whereas men prioritised physiological comfort descriptors, i.e. properties which facilitate thermoregulation. Furthermore, there is an increased importance of physiological comfort parameters for people exercising for 10 or more hours per week. Finally, it was possible to identify common associations and preferences for textile materials (cotton, polyester, cotton/polyester blend and wool). However, consideration should be taken concerning sex and nationality.

Sex and nationality are parameters modulating the clothing comfort concept and the conceptualised feel of materials. Therefore, the sex and nationality of the end-consumer should be considered during the development phase of sports garments and particular attention should be given to the targeted market in which these will be sold.

In spinning industries, selection of the most appropriate fibre for yarn manufacturing plays an important role for achieving an optimal mix of several yarn characteristics, like maximum tenacity, elasticity and spinning ability; and minimum unevenness and hairiness. Identification of the best suited cotton fibre from a set of available alternatives in presence of different conflicting physical properties is often treated as a multi-criteria decision-making (MCDM) problem. The paper aims to discuss this issue.

In this paper, the preference ranking organisation method for enrichment of evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA) methods are integrated to solve a cotton fibre selection problem. The PROMETHEE II method ranks the alternative cotton fibres based on their net outranking flows, whereas GAIA acts as a visual aid to strongly support the derived selection decision. The weight stability intervals for all the considered fibre properties (criteria) over which the position of the top-ranked cotton fibre remains unchanged are also determined.

The clusters of cotton fibres formed in the developed GAIA plane act as a yard stick for their appropriate grading to aid the blending process. The ranking of 17 cotton fibres as achieved applying the combined PROMETHEE-GAIA approach highly corroborates with the observations of the past researchers which proves its immense potentiality and applicability in solving fibre selection problems.

Two MCDM methods in the form of PROMETHEE II and GAIA are integrated to provide a holistic approach for cotton fibre grading and selection while taking into consideration all the available cotton fibre properties.

This study compared physical characteristics in four fabrics before and after a wear trial. The constructed fabrics were: 100 per cent pima cotton, 90–10 per cent pima cotton/wool, 80–20 per cent pima cotton/wool, and 70–30 per cent pima cotton/wool. The physical properties of breaking strength, stiffness, tear resistance, pilling resistance, and wrinkle recovery were compared between worn and unworn fabrics. Wear trial participants (n=20) were asked to assess fabric comfort and performance satisfaction during wear. Results of physical testing found significant differences among blend levels and before and after wearing and care treatment levels. Tear resistance, breaking strength, stiffness, and wrinkle recovery were affected by fibre content and care level. Contrary to the authors' hypothesis, consumer satisfaction was also affected by fibre content of the fabrics.

The purpose of this paper is to explore the potentially conflicting positive and negative ethical aspects of wool apparel and the relative importance of these ethical attributes when consumers in the USA make wool apparel purchase decisions.

A two-stage mixed-method approach was used to explore the positive and negative ethical aspects of wool apparel and the relative importance of these ethical attributes in wool apparel purchase decisions. First, focus groups were used to identify ethical attributes that were important to wool apparel consumers in the USA. In the second stage, a conjoint survey was used to estimate the relative importance of the ethical and product attributes that were identified in the focus groups and the trade-offs made within this attribute set.

Seven themes of ethical issues related to wool apparel consumption emerged during the focus groups: animal welfare, workers’ rights, environmental impact, extrinsic attributes, natural wool, country of origin (COO) and fair trade. In the conjoint analysis respondents identified COO as having the highest relative importance, followed by price, brand, ethical attributes and style. A cluster analysis of survey responses suggested there were two clusters that differed in the importance they attached to ethical labelling issues in wool apparel. The first cluster, did not place a great deal of importance on the ethical labelling issues included in the study, however, the second smaller cluster, ethical issues, specifically the humane treatment of sheep, were considered most important.

The study identified wool apparel attributes that were valued by American consumers. That product attributes were more important than ethical attributes suggests a focus on ethical credentials alone may not be effective in wool marketing. Wool apparel was more likely to be purchased by American consumers if they were made in the USA, reasonably priced, made by an independent brand, from humanely produced wool and in a comfortable style.

The purpose of this paper is about understanding an existing situation in a South Indian village and developing a strategy to produce and market speciality hand knitting yarn, involving the unpaid labour (women) in the handloom industry. An observation method (field study) is used to identify an appropriate method for the design and development of speciality yarns.

Participatory action research is a recursive process that identifies methods leading to the choice of appropriate technology (AT) for the production of speciality yarn. A field study observation method was carried out to identify an AT that is acceptable for the community considering their socio-cultural background of the society. Once the technology (AT) was identified to design the speciality yarn, the research then tests the quality and marketability of the yarn.

The method used for product design and quality testing can be adapted by researchers and designers to develop craft items that can build a platform to start a small-scale business. The research describes a model/framework that could be used/investigated by other bodies in the future.

The limitation of AT and the approach taken for research cannot be clearly identified without testing the production method with the women in the village.

This research confirms that along with identifying a sustainable method of production for speciality yarn, it is very essential to ensure the quality of the product that can compete with other market-available hand knitting yarns.

Governments around the world are providing multi‐billion dollar subsidies to roll out fiber to the home (FTTH) to enable superfast broadband (50 Mbps and above). The premise for this is a belief that superfast broadband brings substantial economic and societal benefits. This paper's purpose is to examine whether this belief is well founded.

The authors critically review the arguments most commonly made in favor of FTTH, examining their logic and underlying evidence.

The paper finds that these arguments often inappropriately use benefits of basic broadband to make the case for the upgrade to superfast broadband, or use the benefits of providing superfast to business premises to argue for providing superfast to homes. The authors find the evidence that basic broadband brings economic growth is patchy, and that frequently studies that argue for a link do not adequately distinguish between correlation and causation.

Thus the authors conclude that the conventional wisdom that FTTH will bring substantial economic and societal benefits and therefore deserves a subsidy is, at best, much overstated. The case has simply not been made that FTTH has sufficient incremental externalities over other forms of broadband. This is an important conclusion for politicians, policy makers, telecoms providers and taxpayers, and suggests that billions of dollars of public money may be being wasted.

The purpose of this paper is to disseminate research findings that compared attitudes regarding apparel and fit preferences as well as actual buying behaviors for 229 female baby boomers employed at a midwestern university.

Researchers created a questionnaire that examined purchasing attitudes, behaviors and identified valued apparel characteristics and garment fit expectations/frustrations. Data were analyzed using descriptive statistics.

Results indicated that respondents were frustrated that the apparel industry did not cater to their apparel and fit needs more effectively. While they felt that they knew how to judge proper fit, they indicated that store personnel should be knowledgeable about products in general and clothing fit in particular. They also had distinct clothing design preferences and chose fit, product quality and price considerations over designer and brand names.

The population was limited by midwestern demographic and economic factors descriptive of employees working in a rural community. This may limit the application of results to more diverse baby boomer populations located throughout the USA.

Survey results provide the apparel and retail industry with data that can be used to design and market clothing that is a more accurate reflection of boomer needs and wants, thus producing more satisfied consumers and maximizing industry profits.

This unique study is helpful in expanding an understanding of the apparel and fit preferences of the lucrative baby boomer generation.

Early health education messages made the assumption that most recommendations made on healthy eating were irrelevant to the majority of elderly people. Discusses a new report by the UK Government which has challenged this idea and now suggests that the older generation should also reduce fat, sugar and alcohol intake and increase exercise to fall in line with the population in general. This approach creates difficulties because: the elderly are so diverse as a group; those in care have a range of very practical barriers to healthy eating; and staff of care homes have to balance nutritional requirements with the resident′s need for enjoyment. Despite the apparent problems healthy eating does have a role to play. Elderly people suffer from many degenerative disorders which respond to healthy eating in the form of nutritional therapy. This approach has numerous health benefits, not least because it may reduce the number of drugs a resident requires.

Heterogeneity in choice models is typically assumed to have a normal distribution in both Bayesian and classical setups. In this paper, we propose a semiparametric Bayesian framework for the analysis of random coefficients discrete choice models that can be applied to both individual as well as aggregate data. Heterogeneity is modeled using a Dirichlet process, which varies with consumers’ characteristics through covariates. We develop a Markov Chain Monte Carlo algorithm for fitting such model, and illustrate the methodology using two different datasets: a household-level panel dataset of peanut butter purchases, and supermarket chain-level data for 31 ready-to-eat breakfast cereal brands.