Implementing Sentiment Analysis of Online Reviews to Improve Product and Customer Satisfaction: A QFD/ Kano Model Integration

نوع المستند : المقالة الأصلية

المؤلف

قسم نظم معلومات الأعمال ، کلية تکنولوجيا الإدارة ونظم المعلومات، جامعة بورسعيد، بورسعيد، مصر

المستخلص

This study developed a Quality Function Deployment (QFD)/Kano model integration method for improving product development in terms of customer satisfaction by utilizing sentiment analysis as a Natural Language Processing (NLP) technique based on online reviews. This method is an alternative to traditional methods that rely on questionnaires and interviews to reduce costs and accelerate development. Instead of employing the QFD and Kano models independently, the integration technique produces findings almost immediately, utilizes thousands of evaluations and opinions at a cheap cost, and aids with decision-making issues. The data for this study were gathered from Amazon's enormous review database. Amazon dataset with 400,000 product ratings of Smartphone from June 2014 to December 2022 were used. The merging of two models has created new opportunities for Smartphone manufacturers in terms of deciding which technical specifications need to be modified and improved with customer satisfaction in mind. This could encourage companies to gradually heading towards the current approach that this research introduces, which makes it easier to gather feedback from a variety of customers in an affordable way.

الكلمات الرئيسية

المجلة العلمية للدراسات والبحوث المالية والتجارية
المجلد 6، العدد 1 - الرقم المسلسل للعدد 1
المجلد السادس؛ العدد الأول؛ الجزء الأول؛ يناير 2025
يناير 2025
الصفحة 485-523

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