Aims and Themes
Rapid increase of consumer expectation and demand in the business environment has elicited an urgent need of competitive strategy to develop a successful product. Consumers not only consider functionality and reliability of products, they are also concerned with affective aspects of products such as affective elements, texture, form, colour, and style in relation to the creation and appreciation of products. Successful products nowadays need to demonstrate their competitive edge with affective features in addition to the basic functions. As an example, smartphones are generally developed with similar functions. In the market place, some are equipped with more attractive interfaces while others with less attractive ones. Surveys have indicated that attractive features could help promote product success. It clearly demonstrated the importance of affective design: products with good affective design excite psychological feelings and can help improve consumer satisfaction in terms of emotional aspects. Therefore, it is essential to consider affective design when developing products with pleasurable features.
Affective features can be acquired via analysis of “big data” which may engage 2.5 quintillion bytes of data on a daily basis . Such data can be captured from a wide range of sources such as pervasive sensor networks, internet services and social media. Over the past few years, “big data” has raised growing interest in capturing useful information for developing company strategies, marketing campaigns and product preferences. By collecting and processing this data, affective design can be defined as a function of various affective features/elements/dimensions, and such function can be optimised via analysis and modelling of the relationship between affective dimensions and design attributes. However, “big data” is generally in high volume, multi-dimensional, multi-sourced, highly varied, and highly uncertain. There are still significant challenges to capture, store, process, visualise, query, and manipulate “big data” to extract useful information for affective design. Distilling information through “big data” from web pages, blogs or social media remains a difficult task, as such data often consists of images, content and videos that are often unstructured as well as dynamic in nature. While such data can be readily understood by humans, it is difficult, if not impossible, to achieve the same using digital technologies such as computers. Therefore, state-of-the-art technologies/techniques that are intelligent and accessible are required to support the identification and optimisation of affective design.
This special issue, aims to address the above challenges and solicit original papers describing innovative technologies/techniques to conjunct affective design with big data. Consequently, topics of interest below should all be clearly related/integrated within affective design activity or process and include (but not limited to):
- Data collection and knowledge management for affective design based on social media, online product communities, multimedia-based groups, and cloud computing;
- Capturing affective intention and conducting affective survey through big data; alternatives to traditional survey-based questionnaires, conducting physiological assessment from human through internet, collecting affective data through sensor networks;
- Transforming unstructured affective data using text mining and ontology;
- Analysing, capturing and evaluating consumer affective requirements and concepts through big data;
- Identification of affective features/elements from big data;
- Impact of uncertainty on generation and evaluation of affective design when using big data; uncertainty analysis with vague concepts or parameters such as Kansei words or psychophysical elements when using big data;
- Big data mining for Kansei engineering, cognitive-affective modeling, affective quality improvement;
- Modelling of consumer affective preference / satisfaction using big data;
- Using big data for correlating consumer satisfaction, engineering characteristics and design attributes on affective design;
- Using big data to defining affective design specifications from marketing perspective;
- Using big data to develop smart systems which combine emotionally designed and affective aspects for product development;
- Development of machine learning/artificial intelligence techniques for affective design based on big data;
- Development of computer-aided design systems for affective products using big data; incorporating decision-making within the development process of affective products using big data.
This special issue will bring both academia and industry to report the latest technologies/techniques on using big data for affective design. It will also explore new topics for big data mining and affective design. Potential target audience includes:
- Research students, researchers and scientists from academia or research institutes who are involved in the development of affective design technologies using big data mining, and solving difficult and complex problems relating to affective design and big data;
- Engineers and product designers from manufacturing industries who use big data for affective design.
- Full Papers Due for Review: June 30 2017
- Notification of Review Decision: August 31 2017
- Revised Manuscript Submission: September 30 2017
- Final Decision: November 15 2017
- Final Manuscripts: January 15 2018
- Expected Date of Publication: February 2018 (vol. 30, no.2)
Please prepare your paper following the “Instructions for Authors”
Please submit your paper directly to the journal and once logged in, select “Author Centre”.
(NOTE: In Step 1, check “Special Issue Article” for the type of article. In Step 5, check “Yes” for the last question, “Is the manuscript a candidate for a Special Issue?” and enter “Special Issue on Affective Design using Big Data” in the accompanying text box)
- Guest Editor: Kit Yan Chan, Department of Electrical and Computer Engineering, Curtin University, Australia (firstname.lastname@example.org)
- Guest Editor: C.K Kwong, Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong (email@example.com)
- Guest Editor: T.C. Wong, Department of Design, Manufacture, and Engineering Management, University of Strathclyde, United Kingdom (firstname.lastname@example.org)