Management Responses to Online Reviews: Big Data From Social Media Platforms

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

Electronic word of mouth in online social networks: strategies for coping with opportunities and challenges

In today's world, the widespread success of the Internet, social media, and online social networks (OSN) provide the basis for electronic word of mouth (EWOM). EWOM can be seen as a digital enhancement of traditional word of mouth that makes communication more efficient and involves less effort by its users. The resulting speed of diffusion and information transparency have caused transformative changes in consumer behaviour in all types of markets, which requires the development of new business strategies for adequately dealing with the new circumstances. This doctoral dissertation is divided into three overall subject areas that concern the investigation of capable strategies for coping with the emerged opportunities and challenges of EWOM in OSN. The first subject area concerns negative electronic word of mouth in OSN and investigates capable countermeasure strategies for firms to adequately address claims of unsatisfied customers. For this, three simulation studies are conducted in which the propagation of a negative message and its countering by a positive message published by the firm are numerically analysed. The results reveal that, in general, the persuasiveness of a firm's response is more important than a quick response with a less persuasive counter-message. To some extent, this also holds if the number of OSN members who initially disseminate the counter-message on behalf of the firm is increased. In the second subject area, an optimisation model for individualised pricing is developed for an online store whose customers are interconnected in an OSN and can share price information via EWOM. The model is solved numerically by artificial intelligence solution methods. The results indicate that personalised prices can be financially worthwhile even under price transparency. The third subject area investigates market entry strategies for social media apps and services that are advertised in an OSN for acquiring new users and examines the role of EWOM in this context. A diffusion model is developed and analysed numerically by simulation. Three different targeting approaches are compared to each other regarding their ability to reach a high share of active users in the OSN: (1) a random marketing strategy, where randomly chosen members in the OSN are presented the advertisement, (2) cluster marketing, where whole clusters of members who are densely connected to each other are simultaneously shown the advertisement, and (3) influencer marketing, where the most influential users in the OSN are selected to share sponsored posts about the app in the OSN. The results suggest that EWOM can have detrimental effects if OSN members are too early informed about the app or service. If the information about the app reaches clusters in the OSN prematurely where a sufficient level of activity is not present yet, it can deplete the excitement of the users. The lack of excitement, in turn, can significantly reduce the effect of subsequent marketing campaigns. However, if applied appropriately, a higher level of EWOM about the app or service can increase the performance of the random marketing strategy to the extent that it outperforms cluster and influencer marketing