The influence of design proposal viewing strategy: design aesthetics and professional background

Hou, Guanhua; Lu, Guoying

doi:10.1007/s10798-018-9450-7

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…In the meantime, new product development research in industrial design have been oppositely leaning towards a greater emphasis on aesthetics characteristics of the product designs. Some examples to highlight this situation are observed in the study by Jitender [17] that stresses on the importance of aesthetics to product design, and also by Hou and Lu [18], which concludes on a great influence of aesthetics on product design appeals. On the whole, it is clear that the main consideration in the product design process is essentially different between the current engineers and industrial designers, even though it has been acknowledged that a good quality product design requires a balanced trade-off of both functional and aesthetics aspects.…”

Section: Literature Reviewsmentioning

confidence: 99%

A comprehensive 7M IoT adoption model-a lifecycle shift paradigm

Ibrahim¹,

Romli²,

Arep³

et al. 2021

IJATEE

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One of the primary issues with many product design and development processes today is to have a good balance between the elements of form and function. While all products must be able to perform their intended functions, considerations of aesthetic features are also necessary for them to be accepted as a good quality design. However, most available design and development processes of engineering products have been more focused on achieving the functional aspects and tend to treat the aesthetic aspects as less crucial features. This approach has led to many failed functional products in the market as their physical design lacks appealing factors to the targeted customers. To improve the situation, a new product design and development framework is proposed in this study to better facilitate designers or engineers in creating an all -rounded quality product design. This new method is developed based on the findings from conducted survey among engineeringstudents, who are future product design engineers, to identify and resolve issues with current methods that they use for their design tasks. All in all, the new proposed method is essentially tailored to offer a comprehensive guide for developing beautiful and useful products based on semantics design approach.

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Section: Literature Reviewsmentioning

confidence: 99%

A comprehensive 7M IoT adoption model-a lifecycle shift paradigm

Ibrahim¹,

Romli²,

Arep³

et al. 2021

IJATEE

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“…Excellent GUI design not only provides visually pleasing experiences but also improves the usability and acceptability of an interface to a certain extent [1]. Aesthetics is a crucial element of an interactive system among all the user experience elements [2][3][4]. Although aesthetic preferences can vary among people, notably beautiful user interfaces clearly have a relatively high scoring consistency [5].…”

Section: Introductionmentioning

confidence: 99%

Computational model for predicting user aesthetic preference for GUI using DCNNs

Xing

Chen

et al. 2021

CCF Trans. Pervasive Comp. Interact.

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Visual aesthetics is vital in determining the usability of the graphical user interface (GUI). It can strengthen the competitiveness of interactive online applications. Human aesthetic preferences for GUI are implicit and linked to various aspects of perception. In this study, an aesthetic GUI image database was constructed with 38,423 design works collected from Huaban.com, a popular social network website for art and design sharing, collection, and exhibition in China. The numbers of user collection and likes of each design work were used as the annotation to represent user preference levels. Deep convolutional neural networks were applied to evaluate the aesthetic preferences of GUIs, based on a large dataset of user interface design images with the ground-truth annotations. The experimental result indicated the feasibility of the proposed method, with a mean squared error (MSE) of 0.0222 for user collection prediction and an MSE of 0.0644 for user likes prediction in the best model performance of Squeeze-and-Excitation-VGG19 networks (SE-VGG19). This study aims to build a large aesthetic image database, and to explore a practical and objective evaluation model of GUI aesthetics.

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“…Various factors are affecting the awarding result of a design proposal. However, visual aesthetics is proved to be critical in product design evaluation by statistical analysis and user study [9][10][11]. We put forward a hypothesis that there could be a potential pattern to predict the successfulness of the proposal in product design that a visual aesthetic can be a critical cue for classification modeling.…”

Section: Introductionmentioning

confidence: 99%

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

Xing

et al. 2020

IEEE Access

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A visual aesthetic is a crucial determinant of product design evaluation. Through the analysis of image features, not only can we evaluate the aesthetic level, but also we can reveal the whole quality of the design proposal. We assume that it could be a potential pattern to predict the ultimate success of the proposal in product design that a visual aesthetic can be a cue for award classification modeling. Consequently, we conduct investigation on a dataset of over 10,003 design submissions in a design competition held once a year from 2008 to 2018 in order to manifest the assumption. Due to the remarkable performance of deep convolutional neural networks (DCNNs), we compare seven deep learning methods to explore an optimal model for design award prediction based on product image analysis. The result of the experiments indicates that the proposed method achieves comparative accuracy in design award classification result predication, with the optimal classification accuracy of 70.79% using the SEFL-ResNet (Squeeze and Excitation-Focal Loss-ResNet) method.

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The influence of design proposal viewing strategy: design aesthetics and professional background

Cited by 9 publications

References 33 publications

A comprehensive 7M IoT adoption model-a lifecycle shift paradigm

A comprehensive 7M IoT adoption model-a lifecycle shift paradigm

Computational model for predicting user aesthetic preference for GUI using DCNNs

Product Design Award Prediction Modeling: Design Visual Aesthetic Quality Assessment via DCNNs

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