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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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Published: Swansea 2022
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Hajli, Nick ; Thomas, Roderick
URI: https://cronfa.swan.ac.uk/Record/cronfa61502
first_indexed 2022-10-10T13:08:48Z
last_indexed 2023-01-13T19:22:17Z
id cronfa61502
recordtype RisThesis
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spelling 2022-10-10T14:24:25.0614407 v2 61502 2022-10-10 Management Responses to Online Reviews: Big Data From Social Media Platforms ca5f50421242a544d6290d101f106464 Aytac Gokce Aytac Gokce true false 2022-10-10 CBAE 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. E-Thesis Swansea big data, social media, advanced analytics, text mining, management responses, online reviews 7 10 2022 2022-10-07 10.23889/SUthesis.61502 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University Hajli, Nick ; Thomas, Roderick Doctoral Ph.D Ministry of Turkish Republic 2022-10-10T14:24:25.0614407 2022-10-10T14:06:14.8759913 Faculty of Humanities and Social Sciences School of Management - Business Management Aytac Gokce 1 61502__25384__0dc8d8aec07344248ad1e7a7ec5e399b.pdf Gokce_Aytac_PhD_Thesis_Final_Redacted_Signature.pdf 2022-10-10T14:22:48.3961987 Output 2083514 application/pdf E-Thesis – open access true Copyright: The author, Aytac Gokce, 2022. true eng
title Management Responses to Online Reviews: Big Data From Social Media Platforms
spellingShingle Management Responses to Online Reviews: Big Data From Social Media Platforms
Aytac Gokce
title_short Management Responses to Online Reviews: Big Data From Social Media Platforms
title_full Management Responses to Online Reviews: Big Data From Social Media Platforms
title_fullStr Management Responses to Online Reviews: Big Data From Social Media Platforms
title_full_unstemmed Management Responses to Online Reviews: Big Data From Social Media Platforms
title_sort Management Responses to Online Reviews: Big Data From Social Media Platforms
author_id_str_mv ca5f50421242a544d6290d101f106464
author_id_fullname_str_mv ca5f50421242a544d6290d101f106464_***_Aytac Gokce
author Aytac Gokce
author2 Aytac Gokce
format E-Thesis
publishDate 2022
institution Swansea University
doi_str_mv 10.23889/SUthesis.61502
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 1
active_str 0
description 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.
published_date 2022-10-07T02:33:03Z
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score 11.04748

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