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The purpose of this study is to identify Offshore Wind Energy (OWE) as a key technology that could drive countries toward achieving climate goals. However, there are multiple challenges that this sector faces.

This study aims to identify the challenges faced by the sector globally by systematically reviewing the existing literature in global context and portraying it in the Indian context. Factors are identified using content analysis.

Results suggest high levelized cost of energy as the most discussed challenge for the growth of OWE. Insufficient financial support and policy, initial capital and inadequate technology formed the second, third and fourth most discussed challenges respectively.

To reduce the cost of OWE, the distribution companies in India could adopt feed-in tariffs (FiTs) in the early stages of development and make OWE procurement mandatory. The renewable purchase obligation (RPO) in India is specific to solar and non-solar; policy should accommodate offshore wind-specific RPO targets for each state to reach the 2030 target of 30 GW from OWE.

To the best of the authors’ knowledge, this is the first attempt to study the challenges of OWE development from a global perspective and portray these major challenges in the Indian context and uses content analysis from the existing literature to ascertain the major roadblocks for the development of OWE.

The study identifies the unexplored gap in literature that includes futuristic challenges for OWE from climate change. Future studies can explore the possibilities of forecasting based on climate change scenarios and rank the challenges based on their relevance caused by possible damages.

The Web 3.0 has been hugely enabled by smartphones and new generation mobile applications. With the growing adoption of smartphones, the use of mobile applications has grown exponentially and so has the development of mobile applications. This study is an attempt to understand the issues and challenges faced in the mobile applications domain using discussions made on Twitter based on mining of user generated content.

The study uses 89,908 unique tweets to understand the nature of the discussions. These tweets are analyzed using descriptive, content and network analysis. Further using transaction cost economics, the findings are reviewed to develop practice insights about the ecosystem.

Findings indicate that the discussions are mostly skewed toward a positive polarity and positive user experiences. The tweeters are predominantly application developers who are interacting more with marketers and less with individual users.

Most of these applications are for individual use (B2C) and not for enterprise usage. There are very few individual users who contribute to these discussions. The predominant users are application reviewers or bloggers of review websites who use the recently developed applications and discuss their thoughts on the same.

The results may be useful in varied domains which are planning to expand their reach to a larger audience using mobile applications and for marketers who primarily focus on promotional content.

The domain of mobile applications on social media is still restricted to promotions and digital marketing and may solely be used for the purpose of link building by application developers. As such, the discussions could provide inputs towards mobile phone manufacturers and ecosystem providers on what are the real issues these communities are facing while developing these applications.

The study uses mixed research methodology for mining experiences in the domain of mobile application developers using social media analytics and transaction cost economics. The discussion on the findings provides inputs for policy-making and possible intervention areas.

Introduction: The insurance sector is playing a crucial role in the sustainable growth of the Indian economy. But in India, this sector loses crores of rupees every year due to the increasing fraud cases. With the increase in insurance customers, insurance companies need to efficiently equip themselves with a robust system to handle claims fraud. Detection of insurance fraud is a pretty challenging problem. Nowadays, machine learning (ML) and artificial intelligence (AI) are the strategic choices of many leading organisations that want to proceed in a new digital arena.

Purpose: This chapter’s main objective is to highlight the fundamental market forces driving the adoption of AI and ML and showcase the traditional and modern methods to predict insurance claims fraud intelligently.

Methodology: Various research papers have been reviewed, and ML methods have been discussed, which are all being used to predict insurance fraud claims. This chapter also highlights various driving forces influencing the adoption of ML.

Findings: This study highlights the introduction of blockchain technology in fraud detection and in combatting insurance fraud. Literature indicates that the quantity and quality of data significantly impact predictive accuracy. ML models are beneficial to identify the majority of fraudulent cases with reasonable precision. Insurance companies should explore the benefits of experienced resource persons from the same domain and develop unique business ideas/rules.

This study explores the interplay among climate change, economic growth and energy consumption in G20 countries by considering the role of green energy.

This study uses various empirical tools to determine the association between carbon emissions, economic growth, renewables, non-renewables, population and urbanization for a panel of G20 countries between 1990 and 2014.

Empirical outcomes from various empirical tools reveal a positive and significant impact of economic growth, non-renewable energy consumption and urbanization on carbon emissions, and their increase will further lead to the deterioration of environmental quality. The elasticity coefficient of renewable energy coefficient is negative and significant implying an increase in its consumption will improve environmental quality. Panel causality test results reveal the existence of both short-run and long-run causality among the variables. Therefore, results infer that a reduction in the consumption of non-renewable and substitution with renewables will have a significant impact on carbon emission mitigation.

Through this study, the authors suggest the sustainable use of renewables as they are sustainable, secure, efficient, environmentally justifiable and economically viable sources of energy. Therefore, replacing traditional non-renewables with modern renewables has the potential in avoiding the dangerous impacts of greenhouse gases (GHGs) particularly in the G20 countries. This paper intends to guide policymakers regarding the environmental quality and renewable energy consumption required to hold back the fossil fuel dependence for a cleaner and greener planet.

Energy use occupies an important place among the service activities offered to tourists that guide the tourism industry. The realization of basic needs such as heating, cooling, ventilation, lighting and decontamination in these enterprises are among the important factors that directly affect energy use. In order to obtain the energy needed for the sustainability of services at a more affordable cost, renewable energy sources should be put into operation. In this direction, it makes it more advantageous for businesses in the tourism sector to invest by turning to renewable energy sources in order to maintain their activities more economically. In this context, the main purpose of the study carried out in this part of the book is to reveal the latest developments in the field of evaluation of renewable energy sources in tourism enterprises. Bibliometric analysis was carried out by using the Web of Science (WoS) database in the research and with the findings obtained, it was concluded that the field is new and up-to-date and needs to be studied more. When looking at the WoS categories of studies titled renewable energy in tourism enterprises; it was concluded that more energy fuel, green sustainable science technology, science themes were given weight. According to the network analysis, the most cited authors and countries' densities were determined and the intensive expressions in the network keywords in their studies titled renewable energy in tourism enterprises are renewable energy, renewable energy sources, sustainable tourism, sustainability, carbon dioxide (CO₂) emissions, green marketing, blue economy, and energy efficiency.

This study aims to provide insight into the operational factors of big data. The operational indicators/factors are categorized into three functional parts, namely synthesis, speed and significance. Based on these factors, the organization enhances its big data analytics (BDA) performance followed by the selection of data quality dimensions to any organization's success.

A fuzzy analytic hierarchy process (AHP) based research methodology has been proposed and utilized to assign the criterion weights and to prioritize the identified speed, synthesis and significance (3S) indicators. Further, the PROMETHEE (Preference Ranking Organization METHod for Enrichment of Evaluations) technique has been used to measure the data quality dimensions considering 3S as criteria.

The effective indicators are identified from the past literature and the model confirmed with industry experts to measure these indicators. The results of this fuzzy AHP model show that the synthesis is recognized as the top positioned and most significant indicator followed by speed and significance are developed as the next level. These operational indicators contribute toward BDA and explore with their sub-categories' priority.

The outcomes of this study will facilitate the businesses that are contemplating this technology as a breakthrough, but it is both a challenge and opportunity for developers and experts. Big data has many risks and challenges related to economic, social, operational and political performance. The understanding of data quality dimensions provides insightful guidance to forecast accurate demand, solve a complex problem and make collaboration in supply chain management performance.

Big data is one of the most popular technology concepts in the market today. People live in a world where every facet of life increasingly depends on big data and data science. This study creates awareness about the role of 3S encountered during big data quality by prioritizing using fuzzy AHP and PROMETHEE.

This paper aims to investigate the impact of online identity orientation and online friendship homophily on online socializing, online information search and ethnic guests’ hospitality experiences.

The study uses structural equation modeling to test a conceptual model developed after reviewing hospitality literature. Data is collected from a sample of 514 Turkish-Dutch ethnic guests living in the Netherlands using a self-administered questionnaire.

The results show that online identity orientations aligned with minority and majority cultures impact online friendship homophily and online socializing, which subsequently impact online information search and hospitality experiences of ethnic guests.

On the whole, ethnic communities have considerable spending power. The findings point to heritage and mainstream cultural socialization accounting for travel and hospitality experiences within an ethnic minority group. The findings supply relevant information for hospitality sectors on services to endorse or promote to guests from ethnic communities.

The study examines the simultaneous effects of online identity orientations and online friendship homophily on online socialization and hospitality experiences of ethnic guests. It highlights the role of culture in explaining the use of social networking sites and its potential impact on hospitality-related behaviors and experiences of ethnic guest consumers.

This paper aims to examine the reaction of energy inflation to geopolitical risks in the European Economic Area between 1990 and 2015.

This study applies the nonparametric time-varying coefficient panel data model with fixed effects. In addition, to further reveal potential tail effects that may not have been captured by conditional mean-based regressions, the method of moments quantile regression was also used.

The findings of this study are as follows: first, as European countries get exposed to geopolitical tensions, it is expected that energy prices will surge. Second, the ability of geopolitical risk to trigger energy inflation in recent times is not as powerful as it used to be. Third, countries with a lower inflation rate, when exposed to geopolitical risks, experience smaller increases in energy inflation compared to countries with a higher inflation rate.

The findings of this study lead us to the conclusion that transitioning from nonrenewable to renewable energy use is one channel through which the sampled countries can battle the energy inflation, which geopolitical risks trigger. A sound macroeconomic policy for inflation control is a complementary channel through which the same goal can be achieved.

Given the increasing level of energy inflation and geopolitical risks in the world today, this study is an attempt to reveal the time-varying characteristics of the relationship between these variables in European countries using a nonparametric time-varying coefficient panel data model and method of moments quantile regression with fixed effects.

This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.

We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.

The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.

This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.

This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.

In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

This study integrates fishers’ and decision-makers’ views on the critical factors for non-compliance in the artisanal fisheries of Oman.

A questionnaire survey was implemented covering all coastal governorates of Oman. The questionnaires for fishers and decision-makers contained 46 and 43 questions, respectively, divided into various sections based on the Table of Eleven. Compliance factors were divided into spontaneous factors and enforcement factors. The data were collected through 1,242 questionnaires (1,125 fishers and 117 decision-makers).

The results indicated that spontaneous compliance factors (e.g. financial/economic, level of knowledge and social norms) and enforced compliance factors (e.g. social control, sanction certainty and sanction severity) have a significant influence on fishers' motivation to comply with regulations. The chi-square test (X²) was used to show that the differences between the means of responses of fishers and decision-makers in regard to the factors that influence non-compliance in the fishery were insignificant.

This consistency of opinions has an essential policy inference for the regulatory institutions in that it delivers assistance and trust in fisheries management authority's efforts to create effective compliance plans for the fisheries.