PurposeThe purpose of this paper is to explore pre-purchase apparel evaluation cues and examine the effect of demographic variables empirically in the context of a developing country. The initiation for the study was driven by the absence of such prior research and supplemented by the big market opportunity for clothing products in the country under investigation.Design/methodology/approachA self-administered online survey was used for data collection. Demographic questions, 23 apparel measurement items composed of 17 product-based and 6 sustainability-based items, and an open-ended question were included in the questionnaire. Factor analysis was used for dimension reduction and one-way multivariate analysis of variance (MANOVA) for hypotheses testing.FindingsGarment fit, design features, colour, fabric type and seasonality are the five important characteristics most Ethiopian consumers consider while buying apparel products. Factor analysis resulted in five important factors used for pre-purchase apparel evaluation amongst which the design and extrinsic cue is found to be the most important. Environmental factors in the apparel industry got higher emphasis than social factors. While age and educational background made differences in apparel evaluation, gender did not show a significant difference.Originality/valueThe paper provides a founding insight in exploring apparel evaluation cues by considering product- and sustainability-based cues in a developing country context. It also examines the effect of three demographic variables which are rarely studied in such combination.
Monitoring decreases in the mean of Weibull time between events data to address process quality deteriorations is an important task in reliability analysis. Two new control charts such as Weibull exponentially weighted moving average and mixed cumulative sum‐exponentially weighted moving average by transforming the Weibull data to the exponential data are proposed and compared with 2 existing control charts such as Weibull cumulative sum and mixed exponentially weighted moving average‐cumulative sum. The performance comparison provides a way to select a specific control chart in a given situation. The average run length and the standard deviation of the run length are used as performance measures. The relative mean index is also utilized to measure the overall performance. The smaller the value of the relative mean index, the better the performance of the control chart and vice versa. Two illustrative examples are provided to show the applications of the proposed control charts.
In this paper, we propose 3 new control charts for monitoring the lower Weibull percentiles under complete data and Type‐II censoring. In transforming the Weibull distribution to the smallest extreme value distribution, Pascaul et al (2017) presented an exponentially weighted moving average (EWMA) control chart, hereafter referred to as EWMA‐SEV‐Q, based on a pivotal quantity conditioned on ancillary statistics. We extended their concept to construct a cumulative sum (CUSUM) control chart denoted by CUSUM‐SEV‐Q. We provide more insights of the statistical properties of the monitoring statistic. Additionally, in transforming a Weibull distribution to a standard normal distribution, we propose EWMA and CUSUM control charts, denoted as EWMA‐YP and CUSUM‐YP, respectively, based on a pivotal quantity for monitoring the Weibull percentiles with complete data. With complete data, the EWMA‐YP and CUSUM‐YP control charts perform better than the EWMA‐SEV‐Q and CUSUM‐SEV‐Q control charts in terms of average run length. In Type‐II censoring, the EWMA‐SEV‐Q chart is slightly better than the CUSUM‐SEV‐Q chart in terms of average run length. Two numerical examples are used to illustrate the applications of the proposed control charts.
Purpose The purpose of this study is to explore fit problems, satisfaction and preferences of Ethiopian male consumers of ready-made garments (shirt, polo shirt, sweater and khaki and jeans trousers) and highlight the need for a domestic standard garment size chart. Design/methodology/approach Using a structured questionnaire, 405 usable responses were collected from consumers in four cities (Bahir Dar, Kombolcha, Dessie and Addis Ababa) based on convenience sampling. Moreover, the pattern-making methods of 12 domestic garment manufacturing companies were investigated. One-way analysis of variance and multivariate analysis of variance were used to examine differences in fit satisfaction with age, body size and shape. Multiple regression was used to test hypotheses. Findings The participants were mostly neither satisfied nor dissatisfied with the fit of the garments irrespective of their age, body size and shape. While age was found to be insignificant, apparel sizes worn and body shape were found to be significant predictors of fit type in most garments. It was also found that most of the domestic garment manufacturing companies use the knock-off method for pattern making, which results in a bad fit as the basic garment for the knock-off is constructed based on other countries’ standards. Originality/value This study investigates the fit problems and preferences of ready-made garments in the context of consumers in a developing country. Moreover, it has a contribution in considering men’s body shape in the analysis of fit preferences. The results have implications for developing domestic standard garment size charts to improve fit satisfaction.
The statistical learning classification techniques have been successfully applied to statistical process control problems. In this paper, we proposed a one-sided control chart based on support vector machines (SVMs) and differential evolution (DE) algorithm to monitor a process with multivariate quality characteristics. The SVM classifier provides a continuous distance from the boundary, and the DE algorithm is used to obtain the optimal parameters of the SVM model by minimizing mean absolute error (MAE). The average run length of the proposed chart is computed using the Monte Carlo simulation approach. Several simulated cases are conducted using a multivariate normal distribution with 10 and 20 dimensions and three different process shift scenarios. In addition, we consider two non-normal distribution cases. The ARL performance of the proposed chart is better than the distance-based SVM chart. A real example is used to illustrate the application of the proposed control chart.
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