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Management Responses to Online Reviews: Big Data From Social Media Platforms / Aytac Gokce
Swansea University Author: Aytac Gokce
DOI (Published version): 10.23889/SUthesis.61502
Abstract
User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competi...
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Swansea
2022
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| Institution: | Swansea University |
| Degree level: | Doctoral |
| Degree name: | Ph.D |
| Supervisor: | Hajli, Nick ; Thomas, Roderick |
| URI: | https://cronfa.swan.ac.uk/Record/cronfa61502 |
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| Abstract: |
User-generated content from virtual communities helps businesses develop and sustain competitive advantages, which leads to asking how firms can strategically manage that content. This research, which consists of two studies, discusses management response strategies for hotel firms to gain a competitive advantage and improve customer relationship management by leveraging big data, social media analytics, and deep learning techniques. Since negative reviews' harmful effects are greater than positive comments' contribution, firms must strategise their responses to intervene in and minimise those damages. Although current literature includes a sheer amount of research that presents effective response strategies to negative reviews, they mostly overlook an extensive classification of response strategies. The first study consists of two phases and focuses on comprehensive response strategies to only negative reviews. The first phase is explorative and presents a correlation analysis between response strategies and overall ratings of hotels. It also reveals the differences in those strategies based on hotel class, average customer rating, and region. The second phase investigates effective response strategies for increasing the subsequent ratings of returning customers using logistic regression analysis. It presents that responses involving statements of admittance of mistake(s), specific action, and direct contact requests help increase following ratings of previously dissatisfied returning customers. In addition, personalising the response for better customer relationship management is particularly difficult due to the significant variability of textual reviews with various topics. The second study examines the impact of personalised management responses to positive and negative reviews on rating growth, integrating a novel method of multi-topic matching approach with a panel data analysis. It demonstrates that (a) personalised responses improve future ratings of hotels; (b) the effect of personalised responses is stronger for luxury hotels in increasing future ratings. Lastly, practical insights are provided. |
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| Keywords: |
big data, social media, advanced analytics, text mining, management responses, online reviews |
| College: |
Faculty of Humanities and Social Sciences |