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The purpose of this study is to develop a review rating prediction method based on a supervised text mining approach for unrated customer reviews.

Using 2,851 hotel comment card (HCC) reviews, this paper manually labeled positive and negative comments with seven aspects (dining, cleanliness, service, entertainment, price, public, room) that emerged from the content of said reviews. After text preprocessing (tokenization, eliminating punctuation, stemming, etc.), two classifier models were created for predicting the reviews’ sentiments and aspects. Thus, an aggregate rating scale was generated using these two classifier models to determine overall rating values.

A new algorithm, Comment Rate (CRate), based on supervised learning, is proposed. The results are compared with another review-rating algorithm called location based social matrix factorization (LBSMF) to check the consistency of the proposed algorithm. It is seen that the proposed algorithm can predict the sentiments better than LBSMF. The performance evaluation is performed on a real data set, and the results indicate that the CRate algorithm truly predicts the overall rating with ratio 80.27%. In addition, the CRate algorithm can generate an overall rating prediction scale for hotel management to automatically analyze customer reviews and understand the sentiment thereof.

The review data were only collected from a resort hotel during a limited period. Therefore, this paper cannot explore the effect of independent variables on the dependent variable in context of larger period.

This paper provides a novel overall rating prediction technique allowing hotel management to improve their operations. With this feature, hotel management can evaluate guest feedback through HCCs more effectively and quickly. In this way, the hotel management will be able to identify those service areas that need to be developed faster and more effectively. In addition, this review rating prediction approach can be applied to customer reviews posted via online platforms for detecting review and rating reliability.

Manually analyzing textual information is time-consuming and can lead to measurement errors. Therefore, the primary contribution of this study is that although comment cards do not have rating values, the proposed CRate algorithm can predict the overall rating and understand the sentiment of the reviews in question.

The purpose of this chapter is to shed light on researchers and practitioners about sentiment analysis in hospitality and tourism. The technical details described throughout the chapter with a case study to provide clarifying insights. The proposed chapter adds significantly to the body of text mining knowledge by combining a technical explanation with a relevant case study. The case study used supervised machine learning to predict overall star ratings based on 20,247 comments related to Royal Caribbean International services for determining the impact of cruise travel experiences on the evaluation company process. The results indicate that travelers evaluate their travel experiences according to the most intense negative or positive feelings they have about the company.

This case is about the success of Filli Boya, one of the well-known brands of the Turkish paint industry, on using traditional TV advertisements for creating brand awareness. The purpose of this case study is to point out how TV advertisement, the so-called ‘traditional and boring’ communication tool, could still be effective when integrated into the contemporary marketing philosophy and modern marketing communication trends such as real-time social media marketing.

A qualitative method was applied and both primary and secondary sources of information were used in this study. As primary sources of information, in-depth interviews were conducted. Purposive sampling method was used, and participants were recruited from a sample that was broadly believed to be able to evaluate the brand from different points of view. Interviews were analysed through content analysis using the grounded theory approach. The secondary sources of information, including advertisements and news in media, reports and user contents shared in social media were also used considering the principle of multiple data sources.

Uncovering the contemporary ways of using traditional mass advertising based on a Turkish brand’s experiences, this case could be taken up in advertising courses at both graduate and postgraduate levels when discussing creativity in advertising and also modern integrated marketing communication methods.

The human and technology have integrated, and distinctive characteristics of different societies have intertwined with technology. Thus, technology constitutes the reflection of the society, where it exists, in economic, social, and political domains. The desires, expectations, and needs of people have affected the change of technology in time. This effect contains different domains in itself. Life has changed since the 1980s under the leadership of means of informatics, and following the 1990s, internet technology have opened the doors of an informatics era whose effect is being highly sensed today. In addition to its importance, it creates significant changes and effects on individuals, society, culture, and other domains. When significant changes in technology and digitalization have combined with the globalization of information and communication, the resulting problems have begun to spread faster.

In this study, which was performed for examining the digital conflicts arising in business infrastructure, literature was reviewed thoroughly. In the first part of the study, the conceptual framework of business infrastructure was examined and tried to be clarified. Moreover, it was intended to convey the commencement of the technologies used, and their following processes. Then, it was urged upon clarifications regarding business processes and digitalization of business processes, and by referring to the implementations of technologies – used in business processes – in the domain of digitalization, structural examinations were conveyed from the literature. Finally, the issues of digital conflicts and cyber security occurring in business infrastructure, which are being expressed as more prominent factors, were clarified.

This empirical study uses the stimulus-organism-response (S-O-R) framework to examine the interrelationships amongst hotel websites and app quality, flow, telepresence, user engagement and booking intentions.

Data from two different datasets, including users of hotel websites (N sample 1 = 257) and hotel mobile apps (N sample 2 = 292), were collected. Partial least squares (PLS-SEM) was used to test the research model.

Findings indicate that the quality of the hotel websites and mobile apps positively influences telepresence, flow and engagement. Telepresence and flow positively affect the users booking intentions for both the samples. However, for hotel website users, engagement has a no-significant effect on booking intentions. Finally, telepresence has a non-significant effect on flow, and flow has a non-significant effect on engagement for both the users of hotel websites and mobile apps.

This study uses two datasets to understand how hotel booking channel (hotel website and mobile app) quality leads to booking intentions by tapping into telepresence, flow and engagement.

This chapter, against studies on success history, puts its attention on the firm's behaviour facing a critical socio-political problem. The specific question is how the relationship between business and socio-political actors in an emerging market under a scandal develops and how these actors manage such a situation. To reach this aim the study concerns with the Roche Scandal in Turkey and discusses the reactions of business and socio-political actors before and after the scandal, and further, how Roche subsequently managed these relationships. The theoretical framework for analysis of the Roche Turkey Scandal is constructed on relationship elements of trust, legitimacy and learning. The analysis provides some answers to the question of how Roche Turkey in the face of reactions given by the network actors managed the aftermath of the scandal and how the legitimacy loss was recovered by learning and adaptation. Conclusions enhance our knowledge on the behaviour of firms under critical condition.