Big data and smart computing in network systems

Chen, Jiming; Ota, Kaoru; Wang, Lu; He, Jianping

doi:10.1007/s12083-019-00784-6

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The user's online reviews of products provide a wide and authentic source of UX information (Yang et al , 2019). Previous studies have shown that the user information obtained from online reviews is more reliable and effective than traditional user surveys (Chris et al , 2008; Jing et al , 2015; Hye-Sun et al , 2016; Chen et al , 2019 b ). Although some scholars have worked to extract UX information from online reviews, further study on how to relate discrete UX information to specific product attributes to find to-be-improved product attributes is needed.…”

Section: Introductionmentioning

confidence: 99%

A knowledge-enabled approach for user experience-driven product improvement at the conceptual design stage

Li,

Guo,

Zhang

et al. 2023

AIEDAM

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Improving existing products plays a vital role in enhancing customer satisfaction and coping with changes in the market. Analyzing user experience (UX) to find the deficiencies of existing products and establishing improved schemes is the key to UX-driven product improvement, especially at the conceptual design stage. Although some tools used in conceptual design, such as requirements analysis and knowledge reasoning, have advanced recently, they lack targeted goals and sufficient efficiency in identifying insufficient product attributes and improving existing functions and structures. The challenge lies in considering the influence imposed on design activities by the original product features (including attributes, functions, and structure). In this study, a knowledge-enabled approach and framework that integrates the conceptual design process, online reviews for UX, and knowledge is proposed to support product improvement. Specifically, a decision-making algorithm based on UX analysis is proposed to identify to-be-improved product attributes. Then, through optimizing the previous knowledge application model from knowledge requirement transformation, knowledge modeling, and knowledge reasoning, a smart knowledge reasoning model is established to push knowledge for functional solving of the to-be-improved attributes. A knowledge configuration method is used to modify product features to generate an improved scheme. To demonstrate the feasibility of the proposed approach, a case study of improving an agricultural sprayer is conducted. Through discussion, this study can help to regulate design activities for product improvement, enhance data and knowledge application, and promote divergent thinking during scheme modification.

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