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Purpose – Occasionally, we find our social roles transitioning from friend to researcher. This chapter is a reflexive account of one such transition. The author examines the emotions, the concerns and the rewards and stresses of this shift in her relationship with individuals and community.

Methodology/Approach – The author moved to Arviat, Nunavut, in 2004 and gradually found her inner sociologist could not be contained. Through a process of consultation with the Inuit community in which she was residing, she transitioned from the role of friend to that of researcher. This was complicated by her social location as a Western outsider who had been accepted as a community member.

Findings – Reflexivity is a key component of mitigating the challenges which arose and pursuing ethical research, as well as managing the dynamic range of experiences and feelings which emerged during this process.

This research explores two interconnected questions: (1) How do we approach stylistic features of multimodal rhetorical artifacts such as protest posters? (2) Do said artifacts designed for different purposes exhibit systematic stylistic differences? Drawing on Charles Sanders Peirce’s semiotic categorization, this study develops a framework for examining concision, one of the primary stylistic considerations for multimodal rhetorical artifacts such as protest posters. This paper illustrates the use of this framework by exploring the correlation between rhetorical purpose and concision in posters created and disseminated before and during the 2011–2012 Québécois student movement. This study fine-tunes our existing knowledge on multimodality with style sensitivity, and demonstrates how an economy-of-sign based semiotic approach could enrich the empirical examination of multimodal rhetorical artifacts by generating more controlled interpretations.

Interest in the Electronic Word-of-Mouth (eWOM) in connection with tourism is constantly growing not only among consumers but also among theoreticians. Therefore, the objective of this chapter is to provide an overview of studies that focus on eWOM in the tourism sector using the snowball method. The article is based on a review of the literature of 60 studies that focus not only on consumer behavior in tourism and the impact of eWOM on tourism supply but also on the impact of hotel managers' responses to other consumer behavior and tourism companies. The results of the studies show that eWOM has a significant impact not only on consumer behavior but also on tourism supply. Manager responses can also strongly affect other consumer behavior in decision-making. When eWOM is distributed, consumers are influenced by their emotions, motives, and also by the websites to which they have decided to contribute. The chapter proposes further research areas for different authors.

Introduction: The insurance sector provides security to society by pooling resources to manage risks. Insurers’ improved ability to analyse risks by examining vast amounts of granular data has considerably refined this technique. Compiling and analysing the fine data sets is now transformed into the ‘Big Data’ technique. The introduction of big data analytics (BDA) is transforming the insurance industry and the role data plays in insurance.

Purpose: This chapter will attempt to examine the applications and role of big data in the insurance sector and how big data affects the different insurance segments like health insurance, property and casualty, and travel insurance. This chapter will also describe the disruptive impact of big data on the insurance market.

Methodology: Systematic research is carried out by analysing case studies and literature studies, emphasising how BDA is revolutionary for the insurance market. For this purpose, various articles and studies on BDA in the insurance market are selected and studied.

Findings: The execution of big data is continuously increasing in the insurance sector. The performance of big data in the insurance market results in cost reduction, better access to insurance services, and more fraud detection that benefits the customers and stakeholders. Therefore, big data has revolutionised the insurance market and assisted insurers in targeting customers more precisely.