Digital Service Quality Measurement Model Proposal and Prototype Development

Abstract

Traditional service quality models, which are survey-based methods, have been noted by researchers to contain operational errors in their application. Researchers criticize service quality models such as SERVQUAL and SERVPERF for containing operational errors, high implementation costs, and the issue of response recall. Additionally, these models face difficulties when applied to different sectors, as they were developed for the retail industry. The adaptation of the model, data collection, and processing have become outdated in comparison to current information processing technologies. With the rise in the use of social media, new communication paradigms have emerged. In this new paradigm, direct communication is established between people and institutions through social media. Institutions analyze social media data using text mining and sentiment analysis methods to keep up with this change. There are studies in the literature proposing new methods for measuring service quality by separately using text mining and sentiment analysis techniques. In this study, these two techniques have been combined. It is believed that combining these two techniques will result in a more robust service quality measurement model. Additionally, an application has been developed to demonstrate the functionality of the model. A municipality was specifically chosen as the application area because social media allows for fast, efficient, and inclusive participation between citizens and the municipality. The proposed model will enable the better identification of service quality deficiencies, leading to a more efficient use of municipal resources and fostering a more sustainable understanding of the municipality. With the implementation of the model, 463,886 tweets sent to the @ankarabbld and @mavimasa accounts were analyzed to identify 10 service quality dimensions and 106 keywords representing these dimensions, which would reveal the municipality’s service quality. The sentiment analysis technique was applied to 187,084 tweets containing the identified keywords. Thus, an attempt was made to uncover the municipality’s service quality.