An approach of product usability evaluation based on Web mining in feature fatigue analysis

Wu, Mingxing; Wang, Liya; Li, Ming; Long, Huijun

doi:10.1016/j.cie.2014.07.001

Cited by 26 publications

(27 citation statements)

References 27 publications

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“…E-business is a crucial part of today's economy. Customers highly rely on e-business marketplaces for their daily purchases and organizations are competing to survive in e-business environment (Wu et al, 2014;Zhuang and Lederer, 2006). While the Internet and computer technologies allow organizations to reach their customers and run their businesses easily, the competitive environment also posts many challenges.…”

Section: Introductionmentioning

confidence: 99%

Predicting online e-marketplace sales performances: A big data approach

Ch’ng

Chong

et al. 2016

Computers & Industrial Engineering

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To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2,939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework.

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Section: Introductionmentioning

confidence: 99%

Predicting online e-marketplace sales performances: A big data approach

Ch’ng

Chong

et al. 2016

Computers & Industrial Engineering

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“…Pajo et al, 2014Pajo et al, , 2015. Online reviews were first used by Wu et al (2014) to analyze product usability, and increasingly became the data source of customers' needs in the most recent years (e.g. Zhang et al, 2019).…”

Section: Data-driven Design To Identify Customers' Needsmentioning

confidence: 99%

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

Bertoni

2020

Proc. Des. Soc.: Des. Conf.

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The paper presents a systematic literature review investigating definitions, uses, and application of data-driven design in the concept development process. The analysis shows a predominance of the use of text mining techniques on social media and online reviews to identify customers’ needs, not exploiting the opportunity granted by the increased accessibility of IoT in cyber-physical systems. The paper argues that such a gap limits the potential of capturing tacit customers’ needs and highlights the need to proactively plan and design for a transition toward data-driven design.

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“…Thus, it is suggested that user-oriented experiences and designs are a leading factor in the competitive marketing environment [2]. In addition, previous studies proposed an R&D framework that reflects users’ needs through opinion mining [3, 4].…”

Section: Introductionmentioning

confidence: 99%

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

et al. 2019

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Discovering technology opportunities from the opinion of users can promote successful technological development by satisfying the needs of users. However, although previous approaches using opinion mining only have classified various needs of users into positive or negative categories, they cannot derive the main reasons for their opinion. To solve this problem, this research proposes an approach to exploring technology opportunity by structuring user needs with a concept of opinion trigger of objects and functions of the technology-based products. To discover technology opportunity, first, an opinion trigger is identified from review data using Naïve Base classifier and natural language processing. Second, the opinion triggers and patent keywords that have a similar meaning in context are clustered to discover the needs of the user and need-related technology. Then, the sentimental values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent keywords, and their property. Based on the concept of opinion trigger-based methodology, a case study is conducted on automobile—related reviews, extracting the customer needs and presenting important R&D projects such as an extracted need (cargo transportation) and its R&D strategy (resolving contradiction). The proposed approach can analyze the needs of user at a functional level to discover new technology opportunities.

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An approach of product usability evaluation based on Web mining in feature fatigue analysis

Cited by 26 publications

References 27 publications

Predicting online e-marketplace sales performances: A big data approach

Predicting online e-marketplace sales performances: A big data approach

Data-Driven Design in Concept Development: Systematic Review and Missed Opportunities

Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

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