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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 nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.

Advances in Artificial Intelligence (AI) technologies and Autonomous Unmanned Vehicles are shaping our daily lives, society, and will continue to transform how we will fight future wars. Advances in AI technologies have fueled an explosion of interest in the military and political domain. As AI technologies evolve, there will be increased reliance on these systems to maintain global security. For the individual and society, AI presents challenges related to surveillance, personal freedom, and privacy. For the military, we will need to exploit advances in AI technologies to support the warfighter and ensure global security. The integration of AI technologies in the battlespace presents advantages, costs, and risks in the future battlespace. This chapter will examine the issues related to advances in AI technologies, as we examine the benefits, costs, and risks associated with integrating AI and autonomous systems in society and in the future battlespace.

Rapid development of IT and communication technologies resulted in the “Smart” concept, which of late has become quite popular. The smart concept signifies the integration of organizational networks and smart features that enrich the ecosystem, facilitate daily activities for all stakeholders, and for automatization. Smart concept was discussed as a complicated technological infrastructure in urban areas intended to promote economic, social and environmental welfare. The latest technological developments gave rise to the concepts of smart planet, smart city and smart destination that have become important in recent years. Technological innovations have had a big influence on the development of the tourism industry. Smart concept is conceptualized as smart tourism for tourism sector. Smart Tourism generally has a positive effect on the rapid change of information and technology and on the tourism sector, tourism activities and increase in customer satisfaction. In this chapter, the concept of Smart and its smart technologies are explained and information about its reflections on the tourism sector and smart tourism destinations are discussed.

This chapter documents the contributions in the business-to-business (B2B) marketing–buying literature that focus on implemented strategies in specific contexts. Research on implemented strategies often includes thick descriptions of how things actually get done over a period of weeks, months, or years including how decision makers make sense of situations, go about processing information, make choices, interact with other decision makers, participate in specific actions, and interpret events and outcomes. Research on implemented strategies favors “direct research” (Mintzberg, 1979) that includes multiple face-to-face interviews of the same and different participants in B2B processes over the course of days, week, months, or years. Direct research is inherently inductive theory-building and case-based data driven in its theory-empirical approach. Direct research includes applying a number of possible research methods and results in a number of advances in B2B implemented-strategy-in-context theory.

The conceptualization of smart emerged by technological advancements penetrated the tourism industry with the pace of globalization transformed the destinations providing digitalized products. Even though smart tourism destinations are initiated by advanced technologies, the notion evolved in embracing Sustainable Development Goals (SDGs) project economic, social, and environmental proliferation. A tourism destination is counted as “smart” which provides advanced technologies to improve the quality of tourists' experiences and enhance the residents' quality of life. The structure of a smart tourism destination is established on four basic pillars that cover technology, innovation, accessibility, and sustainability issues. Designating the notion of smart into tourism destinations is extremely vital since the shifting paradigm of tourists’ demands concern sustainability. The development of smart in a tourism destination is a crucial concern for destination management organizations (DMOs) integrating key destination components with the dimensions of a smart city to enhance the tourists' satisfaction and competitiveness of the destination. Therefore, one aim of this chapter is to elucidate the association between key destination components with the dimensions of a smart city to reveal the related smart tourism destination applications. Moreover, cultural heritage in smart tourism destinations forms an important part of tourism both with its tangible and intangible resources which have been involved in the emergent era of digitalization inevitably with all parties and processes. Hence, another aim of this chapter is to examine the dimensional shift in cultural heritage tourism within the framework of digitalization. Sharing cases of digitization of cultural heritage from different parts of the world, this chapter also reflects that it is inevitable to benefit from digitization and ICTs in order to reach the SDGs on the scale of smart tourism destinations. Analysis of academic publications and the national and international reports of the related authorities sums the methodology used to conclude the study with suggestions of future research paths to develop the field.

In times of crisis, such as the Covid-19 global pandemic, journalists who write about biomedical information must have the strategic aim to be clearly and easily understood by everyone. In this study, we assume that journalistic discourse could benefit from language redundancy to improve clarity and simplicity aimed at science popularization. The concept of language redundancy is theoretically discussed with the support of discourse analysis and information theory. The methodology adopted is a corpus-based qualitative approach. Two corpora samples with Brazilian Portuguese (BP) texts on Covid-19 were collected. One with texts from a monthly science digital magazine called Pesquisa FAPESP aimed at students and researchers for scientific information dissemination and the other with popular language texts from a news Portal G1 (Rede Globo) aimed at unspecified and/or non-specialized readers. The materials were filtered with two descriptors: “vaccine” and “test.” Preliminary analysis of examples from these materials revealed two categories of redundancy: paraphrastic and polysemic. Paraphrastic redundancy is based on concomitant language reformulation of words, sentences, text excerpts, or even larger units. Polysemic redundancy does not easily show material evidence, but is based on cognitively predictable semantic association in socio-cultural domains. Both kinds of redundancy contribute, each in their own way, to improving text readability for science popularization in Brazil.