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- W4205844635 abstract "Purpose The current study employs text mining and sentiment analysis to identify core banking service attributes and customer sentiment in online user-generated reviews. Additionally, the study explains customer satisfaction based on the identified predictors. Design/methodology/approach A total of 32,217 customer reviews were collected across 29 top banks on bankbazaar.com posted from 2014 to 2021. In total three conceptual models were developed and evaluated employing regression analysis. Findings The study revealed that all variables were found to be statistically significant and affect customer satisfaction in their respective models except the interest rate. Research limitations/implications The study is confined to the geographical representation of its subjects' i.e. Indian customers. A cross-cultural and socioeconomic background analysis of banking customers in different countries may help to better generalize the findings. Practical implications The study makes essential theoretical and managerial contributions to the existing literature on services, particularly the banking sector. Originality/value This paper is unique in nature that focuses on banking customer satisfaction from online reviews and ratings using text mining and sentiment analysis." @default.
- W4205844635 created "2022-01-25" @default.
- W4205844635 creator A5049973880 @default.
- W4205844635 creator A5065789519 @default.
- W4205844635 date "2022-01-07" @default.
- W4205844635 modified "2023-09-30" @default.
- W4205844635 title "Determining banking service attributes from online reviews: text mining and sentiment analysis" @default.
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- W4205844635 doi "https://doi.org/10.1108/ijbm-08-2021-0380" @default.
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