Special issue on affective design using big data

In addition to the functionality, prices, performance and quality of new products, consumers are increasingly concerned with affective aspects such as texture, outlook, colour, forms and images of newproducts. Nowadays, affective aspects of products without doubt play an important role in contributing to their success in themarket place. As an early example, the concept of affective design was originated from Kurosu and Kashimura who developed two cash teller machines having identical functional features (Kurosu and Kashimura 1995). One machine was equipped with attractive buttons and screen, and the other was with less attractive ones. User surveys indicated that the most attractive one could help promote apparent usability (Norman 2002; Zhang and Li 2005). Using amore recent example of affective design of smartphones, surveys have shown that smartphones that were equipped with more attractive interface designs helped promote the product, although smartphones are generally developedwith similar functions (Kim and Lee 2016). These two studies indicate that products with good affective design excite psychological feelings and improve consumer satisfaction in terms of emotional aspects. Therefore, considering affective aspects in engineered product designs is essential to identify and develop pleasurable features into new products that meet affective needs of customers. Affective needs of customers are commonly collected by surveys. Potential consumers are asked to fill in questionnaires and/or participate in interviews in order to uncover their affective needs towards products. However, conducting surveys/interviews is generally expensive and time consuming and there is no guarantee that all domains of affective needs can be captured. Since only limited Kansei words and affective needs can often be addressed in surveysor interviews, important affectiveneeds for newproductdevelopment may be partially or fully overlooked. Thanks to the advanced technologies of capturing ‘big data’, 2.5 quintillion bytes of data can be captured on a daily basis through the internet such as pervasive sensor networks, social media, web pages, or blogs (IBM 2015). ‘Big data’ can be used to capture useful information for developing corporate strategies, marketing campaigns and new products. Many companies adopt affective computing to realise product differentiation strategies. Techniques involving big data can potentially be applied to affective design. In line with the technologies of big data, affective computing has been examined over the past few years including product design (Ayas 2011; Koutsabasis and Istikopoulou 2013), fashion design (Sokolova and Fernández-Caballero 2015), web design (Koutsabasis and Istikopoulou 2013), media communication (Bergen and Ross 2013; Cao et al. 2014), computer game (Yannakakis et al. 2014), human computer interaction (Bakhtiyari, Taghavi, and Husain 2015; Park and Zhang 2015), service development (Hensher 2014; Morris and Guerra 2015) and urban landscape design. From the literature, a growing interest in mining multi-disciplinary affective data by both researchers and industry can be seen.