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Based on Kansei Engineering, this study obtained consumers' emotional preferences aiming to enhance the emotional connection between consumers and clothing to extend the service life of clothing and realize sustainable clothing design.

Six Kansei word pairs that are the most important to consumers were identified through literature reviews, magazines, websites, card sorting of consumers and cluster analysis. Finally, the consumers scored the 32 product specimens through a 5-level rating semantic differential scale questionnaire of six Kansei word pairs. The researchers verified the consumers' emotional preferences through principal component analysis and established the relationship between Kansei words and design elements of color through partial least squares.

The study found consumers' emotional preferences: elegant, minimalist, formal, casual, mature, practical and distinctive style. Besides white, black, gray, blue, consumers will also like red and yellow-red in the future. The crucial findings of this study are to get recommended guidelines that consumers' emotional preferences match the corresponding design elements.

The study's findings can be used to style the design of men's plain-color shirts and guide online marketers and designers to design apparel that meets consumers' emotional needs to develop consumers' sustainability reliance on clothing. This study also explains the overall process and methodology for integrating consumer preferences and product design elements.

This paper aims to revise the ideal ratio for selecting new products based on their qualitative analysis of desired/undesired functions.

The Kano model has been integrated with the ideal ratio to select and prioritize the design of new products. First, the functional analysis method in value engineering was used to determine the attributes and functions of each product design. Then, the Kano model was used to determine the type of each product attribute and to use the desirable functions of attractive attributes, one-dimensional, must-be and undesirable functions of reverse attributes in the ideal ratio to select and prioritize the design of the product. To examine the application of the proposed approach, a gas instruments manufacturing company was investigated, and five new products were selected for the study.

Based on the results, the product design of industrial regulator GS 77/22 was selected as the superior product and the digital diaphragm gas meter, ultrasonic gas meter, axial regulator and turbine gas meter had the second to fifth priority, respectively.

The proposed method can help product designers determine product designs suitable for customers' expectations and provide a desirable prioritization of the product design in terms of their ideal ratio according to the customers.

The proposed approach provides a more desirable prioritization compared with other prioritization methods based on customers' viewpoints. In the proposed method, the Kano model results in respecting customers, understanding community needs, respecting consumers' rights and increasing the organization's social responsibility, which will significantly increase the chance of product success in the market.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

Africa has been identified as an area where higher mortality happens due to un-accessibility to health care, drugs and other health facilities. Nigeria, as one of the African countries, is not excluded from such difficulties. This study aims to examine the determinants of efficient last-mile delivery at selected health facilities and the Kaduna State Health Supplies Management Agency (KADSHMA).

The study sourced data from KADSHMA and the health facilities’ staff, with a total of 261 observations used. Likewise, the respondents were picked from warehouses of each health facility and KADSHMA. The data was analysed using the partial least square structural equation modelling analysis to estimate the relationship among the variables of the study.

The study’s findings revealed that all five variables of the study (i.e. determinants) were significantly affecting the efficient last-mile delivery. Four constructs (delivery cost [DC], delivery time [DT], mode of delivery [MD] and facilities technology [FT]) have shown a positive and significant association with efficient last-mile delivery, whereas one variable (product mix [PM]) indicated a negative and significant association with efficient last-mile delivery. The study concludes that DC, DT, MD, FT and PM played significant roles in efficient last-mile delivery.

The study provides that specific means of transportation should always be on standby to transport health supplies. Time schedules should always be prepared and adhered to when transporting health supplies to the facilities, and each facility should network with robust technology to ease communication in terms of order and order planning. Additionally, facilities should try as much as possible to reduce the varieties of products when ordering health supplies, as it will increase the efficiency of the delivery.

To the best of the authors’ knowledge, this study is the first of its kind that considered these five variables (DC, DT, MD, FT and PM) with impact on the last-mile delivery in one model, especially in the Nigerian case. This is a great contribution to knowledge, more importantly, to the last-mile delivery of the health sector. The result confirmed the importance of these determinants (DC, DT, FT and PM) of last-mile delivery efficiency in saving lives.

The purpose of this study/paper is conducting a Systematic mapping review, as a systematic literature review method for reviewing the literature of new product development by textmining and mapping the results of this review.

This research has been conducted with the aim of systematically reviewing the literature on the field of design and development of products based on textual data. This research wants to know, how text data and text mining methods, can use for the design and development of new products.

This review finds out what are the most popular algorithms in this field? What are the most popular areas in using these approaches? What types of data are used in this area? What software is used in this regard? And what are the research gaps in this area?

The contribution of this review is creating a macro and comprehensive map for research in this field of study from various aspects and identifying the pros and cons of this field of study by systematic mapping review.

The purpose of this paper is to improve the efficiency of pattern-making for suit lapels in the apparel industry, to master the perceptual knowledge of suit lapels and to establish a mapping relationship between suit lapel styles and perceptual judgment.

In this paper, notch lapel, peak lapel and shawl lapel are studied, and the authors extract the structural features in the order of drafting as the input values for model training. At the same time, after the expert screening, four perceptual evaluation dimensions based on a priori knowledge are selected as output values, which are finally classified using a decision tree algorithm.

The results show that different design parameters produce different perceptions of people's vision and, thus, different perceptual judgments. The decision tree model can effectively classify the perceptual judgment of the lapel shape.

The findings of this study help garment factories to provide optimal judgment of basic parameters before the production of a suit lapel and to improve the efficiency of the design.

Today's marketing is dominated by decision-making based on artificial intelligence and machine learning. This study focuses on one semi- and unsupervised machine learning technique, generative adversarial networks (GANs). GANs are a type of deep learning architecture capable of generating new data similar to the training data that were used to train it, and thus, it is designed to learn a generative model that can produce new samples. GANs have been used in multiple marketing areas, especially in creating images and video and providing customized consumer contents. Through providing a holistic picture of GANs, including its advantage, disadvantage, ethical considerations, and its current application, the study attempts to provide business some strategical orientations, including formulating strong marketing positioning, creating consumer lifetime values, and delivering desired marketing tactics in product, promotion, pricing, and distribution channel. Through using GANs, marketers will create unique experiences for consumers, build strategic focus, and gain competitive advantages. This study is an original endeavor in discussing GANs in marketing, offering fresh insights in this research topic.

The aim of the study is to determine the benefits of transforming a destination into smart destination (SD) to restart tourism after the COVID-19 outbreak, to match the dimensions of SD with restarting strategies, and to make conceptual inferences to serve this. For this purpose, two reports published by the World Tourism Organization (WTO) in 2020 were examined: Priorities for Tourism Recovery and Global Guidelines to Restart Tourism. Then, the contents of these reports and the dimensions of SDs were matched and a guiding model for tourism decision-makers was developed.

This chapter focusses on how qualitative research can capture the lived experiences of entrepreneurial individuals by exploring their subjective experiences. Traditional methods of data collection involve listening to what entrepreneurial individuals say and observing their actions, although particular attention to their feelings is often absent. To achieve data congruence and to gain a deeper understanding of their lived experiences, it is crucial to also take into consideration how they feel. This chapter will recount a confessional tale recorded during a recent field study in entrepreneurship that will shed light on the pivotal role that a researcher’s sensory processing sensitivity (SPS) trait can play during qualitative data collection in helping researchers becoming mindful of the feelings of entrepreneurial individuals, even when those feelings are not directly expressed by them. The introduction of the researcher’s SPS trait in promoting data congruence during qualitative data collection will be this chapter’s principal contribution.