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This chapter presents a systematic review of over 30 types of online marketing methods. It describes different methods like email marketing, social network marketing, in-game marketing and augmented reality marketing, among other approaches. The researchers discuss that the rationale for using these online marketing strategies is to increase brand awareness, customer-centric marketing and consumer loyalty. They shed light on various personalization methods including recommendation systems and user-generated content in their taxonomy of online marketing terms. Hence, they explain how these online marketing methods are related to each other. The researchers contend that the boundaries between online marketing methods have not been clarified enough within the academic literature. Therefore, this chapter provides a better understanding of different online marketing methods. A review of the literature suggests that the “oldest” online marketing methods including the email and the websites are still very relevant for today’s corporate communication. In conclusion, the researchers put forward their recommendations for future research about contemporary online marketing methods.

A century ago, the ancestors of modern computers were largely devoted to analysis of social data, but sociology and computer science diverged, and today they need to be reunited. This conceptual chapter argues for the development of an integrated social-information science, in order to understand and develop socio-technical information systems, to explore and extend recommender and reputation systems, to establish the cultural basis for flourishing virtual worlds, and to deal with revolutionary issues concerning intellectual property rights. It suggests that three forms of human–machine collaboration will become increasingly important: (1) partnerships between humans and information technology, (2) cultures jointly created by the human mind and information technology, and (3) environments where humans and machines cooperate.

Facebook “likes” are often used as a proxy of users’ attention and an affirmation of what is posted on Facebook (Gerodimos & Justinussen, 2015). To determine what factors predict “likes,” the authors analyzed Facebook posts made by the campaigns of Hillary Clinton, Bernie Sanders, and Donald Trump, the top three candidates from the 2016 US primary election. Several possible factors were considered, such as the types of posts, the use of pronouns and emotions, the inclusion of slogans and hashtags, references made to opponents, as well as candidate’s mentions on national television. The results of an ordinary least-squared regression analysis showed that the use of highly charged (positive or negative) emotions and personalized posts (first-person singular pronouns) increased “likes” across all three candidates’ Facebook pages, whereas visual posts (posts containing either videos or photos) and the use of past tenses were liked more often by Hillary Clinton and Bernie Sanders’ followers than by Trump’s followers. Television mentions boosted likes on Clinton and Sanders’ posts but had a negative effect on Trump’s. The study contributes to the growing literature on digitally networked participation (Theocharis, 2015) and supports the emerging notion of the new “hybrid media” system (Chadwick, 2013) for political communication. The study also raises questions as to the relevance of platforms such as Facebook to deliberative democratic processes since Facebook users are not necessarily engaging with the content in an organic way, but instead might be guided to specific content by the Facebook timeline algorithm and targeted ads.

Teachers’ ability to identify and link content and language objectives is an important skill. This chapter explores how two-way immersion (TWI) teachers with a mainstream educator negotiated the shift to becoming a language-focused TWI teacher. We argue that it cannot automatically be assumed that these teachers have the knowledge and skills to attend to language issues. Specifically, our study examined how TWI teachers in three schools defined academic language and how they integrated language development into their practice through the use of language objectives. Our qualitative study features a constructivist framework using a thematic analysis of our data, which consisted of individual interviews and surveys with the teachers. Our analysis shows diverse interpretations of academic language and increased awareness of the role of language in their teaching and experienced benefits of making language objectives explicit, as teachers participated in professional development. Selecting and designing specific language-supporting activities, however, continued to be a challenge. We conclude that professional development needs to consider teachers’ different understandings and awareness of the role of language in the classroom. We also note that taking on the role of a language teacher may require a significant shift in assumptions about teaching and learning for teachers with mainstream teacher preparation and experiences and may depend on instructional context.

Purpose – As researchers need partners to collaborate with, this study aims to provide author recommendation for academic researchers for potential collaboration, conference planning, and compilation of scientific working groups with the help of social information. Hereby the chapter analyzes and compares different similarity metrics in information and computer science.

Methodology/approach – The study uses data from the multidiscipline information services Web of Science and Scopus as well as the social bookmarking service CiteULike to measure author similarity and recommend researchers to unique target researchers. The similarity approach is based on author co-citation, bibliographic coupling of authors and collaborative filtering methods. The developed clusters and graphs are then evaluated by these target researchers.

Findings – The analysis shows, for example, that different methods for social recommendation complement each other and that the researchers evaluated user- and tag-based data from a social bookmarking system positively.

Research limitations/implications – The present study, providing author recommendation for six target physicists, is supposed to be a starting point for further approaches on social academic author recommendation.

Practical implications – The chapter investigates in recommendation methods and similarity algorithm models as basis for an implementation of a social recommendation system for researchers in academics and knowledge-intensive organizations.

Originality/value of chapter – The comparison of different similarity measurements and the user evaluation provide new insights into the construction of social data mining and the investigation of personalized recommendation.

The aim of this chapter is to present in a pedagogical way the main digital tools used by tourism-related businesses, especially by hospitality businesses. The main purpose of this chapter is to illustrate our discussion with concrete examples and to give a set of advices for efficient use of those tools.

Literature review was conducted on conceptual issues, as well as managerial and marketing aspects of digital tools, their value and use in the hospitality industry.

This chapter highlights the fact that needs in terms of digitalisation depend on the size of the hotel. The main differences can be explained by differences in terms of hotel capacity, and digital technologies should be customised to different types of structures.

This chapter is exploratory in nature, based on a literature review.

It provides clear and practical guidance about the way independent hospitality businesses could use digital tools for marketing purposes. It also suggests the most efficient digital technologies to improve their performance in the field of marketing and customer relationship management.

The chapter demonstrates the huge gap between best practices in the hospitality industry and the way independent enterprises really use, in practice, the digital tools for marketing purposes. It shows how digital technologies could be used in a more efficient way, to take advantage of their full potential.

Neural networks, which provide the basis for deep learning, are a class of machine learning methods that are being applied to a diverse array of fields in business, health, technology, and research. In this chapter, we survey some of the key features of deep neural networks and aspects of their design and architecture. We give an overview of some of the different kinds of networks and their applications and highlight how these architectures are used for business applications such as recommender systems. We also provide a summary of some of the considerations needed for using neural network models and future directions in the field.

Purpose — This chapter illustrates and explains the ambiguity and vagueness of the term social search and aims at describing and classifying the heterogeneous landscape of social search implementations on the WWW.

Methodology/approach — We have looked at different definitions as well as the context of social search by carrying out an extensive literature review, and tried to unify and enhance existing ideas and concepts. Our definition of social search is illustrated by a general review of existing social search engines, which are analyzed and described by their specific features and social aspects.

Findings — The chapter presents a discussion of social search as well as a comparison of existing social search engines.

Social implications — The definition of social search and the comparison of social search engines summarize the many ways people can search the web together and allow for an assessment of future developments in this area.

Originality/value of paper — Although different attempts to define social search have been made in the past, we present an argumentation that unifies some existing definitions and which is different from other interpretations of the social search concept. We present an overview and a comparison of the different genres of social search engines.