Modelling the Service Experience Encounters Using User-Generated Content: A Text Mining Approach

Arpan Kumar Kar (), Sunil Kumar and P. Vigneswara Ilavarasan
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Arpan Kumar Kar: Indian Institute of Technology
Sunil Kumar: Indian Institute of Technology
P. Vigneswara Ilavarasan: Indian Institute of Technology

Global Journal of Flexible Systems Management, 2021, vol. 22, issue 4, No 1, 267-288

Abstract: Abstract Among services, the immense growth of Indian tourism in the last years has attracted the interest of practitioners, researchers, and governments. Service experiences at the point of encounter can impact the consumption of these tourism services extensively. However, measuring the service experience at the point of service encounter becomes a bit difficult. The tourists who visit India often share their experiences immediately regarding their service encounter in social media. These tweets often have high sentiments and emotional content. In this study, we attempt to identify factors which impact customer service experience, at the point of service encounter, by mining social media discussions. After removing spurious tweets, 7,91,804 tweets were identified and analysed in this study. Factors such as accessibility, accommodation, assurance, cultural attraction, Jugaadu service flexibility, cleanliness, hospitality, price, restaurant, and security were identified using topic modelling, topic association mining, and sentiment analysis. We attempt to model these experiences and their drivers across five zones of India, namely North, South, East, West, and North-East India. Our inferential analysis highlights that the importance and impact of these factors differ significantly zone wise across India, which indicates high location specificity of factors which impact the customer service experience. The study elaborates implications for theory and practice based on our findings.

Keywords: Customer service experience; Jugaadu service flexibility; Machine learning; Service science; Social media; Text mining; Tourism (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s40171-021-00279-5

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