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• W4387614097 abstract "The volume of user generated content (UGC) regarding the quality of provided services has increased exponentially. Meanwhile, research on how to leverage this data using data-driven methods to systematically measure service quality is rather limited. Several works have employed Data Analytics (DA) techniques on UGC and shown that using such data to measure service quality is promising and efficient. The purpose of this study is to provide insights into the studies which use Data Analytics techniques to measure service quality in different sectors, identify gaps in the literature and propose future directions. This study performs a systematic literature review (SLR) of Data Analytics (DA) techniques to measure service quality in various sectors. This paper focuses on the type of data, the approaches used, and the evaluation techniques found in these studies. The study derives a new categorization of the Data Analytics methods used in measuring service quality, distinguishes the most used data sources and provides insights regarding methods and data sources used per industry. Finally, the paper concludes by identifying gaps in the literature and proposes future research directions aiming to provide practitioners and academia with guidance on implementing DA for service quality assessment, complementary to traditional survey-based methods." @default.
• W4387614097 created "2023-10-14" @default.
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• W4387614097 date "2023-10-11" @default.
• W4387614097 modified "2023-10-15" @default.
• W4387614097 title "Data analytics methods to measure service quality: A systematic review" @default.
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• W4387614097 doi "https://doi.org/10.3233/idt-230363" @default.
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