Search results

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The adoption of social media has been extensively discussed. However, to explain the adoption of traditional social media, considering the benefits and risks accumulated from the experiences of social media use, the extent literature is limited. Thus, this paper investigated the act of traditional social media users’ switching behavior from a dynamic perspective and the level of information privacy concerns and social media privacy to measure the risks and benefit accumulated from this dynamic process.

This study of Facebook and Twitter users, who are regarded as representative of traditional social media, are selected as research targets surveyed and were required to answer a specially designed questionnaire in order to determine their general feeling on social media platforms they currently use. As a part of this process, quota sampling was used to collect different samples based on gender and age. In this paper, t-test, one-way ANOVA and multiple comparisons were used for the statistical analysis, conducted through SPSS.

Information privacy concerns and social media dependency affect the adoption of social media. Secondly, social media dependency is a more salient determinant for social media adoption. Therefore, social media firms should pay more attention to enhancing user dependency of social media by increasing user involvement of social media.

This study intends to conduct a research design that provides an overall and holistic understanding of user usage experience. To do this, it investigates the intensity of switching behavior through the level of dependency and the level of information privacy concern that users inevitably exhibit through the use of social media over long time.

This paper analyses how the purchase channel and customer complaint goals affect the sequential choice of post–purchase complaint channels when customers experience a service failure followed by a service recovery failure (double deviation).

An online survey involving a scenario manipulation was conducted with 577 apparel shoppers. The study employs multi-group latent class analysis to estimate latent customer segments within both online and offline groups of shoppers and compare latent classes between the two groups.

The results show that the purchase channel has a lock-in effect on the complaint channel, which is stronger for offline buyers. Moreover, there is evidence of channel synergy effects in the case of having to complain twice: shoppers who complain in store in the first attempt turn to online channels in the second complaint attempt, and vice versa. Complaint goals shape the choice of complaint channels and define different shopper segments.

The present study is the first to adopt a cross-stage approach that analyses the dependencies between the purchase channel and the complaint channel used on two subsequent occasions: the first complaint after a service failure and the second following a service recovery failure.

The chapter discusses the characteristics of audiovisual (AV) media sectors in the Baltic Sea region. Therein it focuses on the specifics of media industries in small countries in the region as they are challenged in ways notably different from large countries with large domestic markets for media content. It discusses the differences between the AV media industries in the Nordic and Baltic countries and suggests that while in the first case long-term welfare society policies and conscious policy-driven system building have conditioned growth and international success then also in the second case innovation policy rationales have facilitated recent growth and dynamics. It then discusses the specific challenges, especially platformisation to small media industries in contemporary globalising media markets, and suggests that opportunities to resist these challenges may be in local inter-sectoral cooperation, that is, in building cross-innovation systems.