The purpose of this paper is to develop an effective approach to support and guide production improvement processes utilising online product reviews.
This paper combines two methods: (1) natural language processing (NLP) to support advanced text mining to increase the accuracy of information extracted from product reviews and (2) quality function deployment (QFD) to utilise the extracted information to guide the product improvement process.
The paper proposes an approach to automate the process of obtaining voice of the customer (VOC) by performing text mining on available online product reviews while considering key factors such as the time of review and review usefulness. The paper enhances quality management processes in organisations and advances the literature on customer-oriented product improvement processes.
Online product reviews are a valuable source of information for companies to capture the true VOC. VOC is then commonly used by companies as the main input for QFD to enhance quality management and product improvement. However, this process requires considerable time, during which VOC may change, which may negatively impact the output of QFD. This paper addresses this challenge by providing an improved approach.
Asadabadi, M.R., Saberi, M., Sadghiani, N.S., Zwikael, O. and Chang, E. (2023), "Enhancing the analysis of online product reviews to support product improvement: integrating text mining with quality function deployment", Journal of Enterprise Information Management, Vol. 36 No. 1, pp. 275-302. https://doi.org/10.1108/JEIM-03-2021-0143
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