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This chapter aims to shed light on some aspects of instructional collaboration with the purpose of providing further understanding of how teachers collaborate and what hinders their collaboration in Saudi inclusive and mainstream schools. Instructional collaboration among teachers in Saudi educational settings has not been thoroughly reviewed, nor has it been adequately considered as an essential component in assuring the implementation of inclusive education. The voice of two special education teachers and two college professors are presented and discussed in order to explain and clarify aspects of instructional collaboration. The last part of the chapter delineates proposed changes that may motivate teachers and aid them in developing a clear understanding of how to practice instructional collaboration in inclusive and mainstream settings, namely, provision of professional development for special and general education teachers, endorsement of legislation and regulations to promote instructional collaboration, and development of teacher education programs.

In this chapter, we draw on social science theoretical and empirical literatures to discuss the factors that influence buying and using an electric vehicle (EV), as well as how adopting an EV can impact on other travel choices or broader sustainability behaviours. We provide an overview of theories of technology adoption, which expose the interplay of individual, technological, and societal factors that dictate how rapidly a technology will spread throughout society. From the empirical literature, we show that far from being a purely economic or pragmatic decision, choosing an EV is also deeply grounded in social, moral and personality factors, such as self-presentation, norms and values, and appetite for risking the novel. Furthermore, since running an EV is not the same as running an internal combustion engine vehicle (ICEV), we explore how adopters adjust their behaviour to the technology, and also how EV ownership may trigger or undermine broader shifts in lifestyle required to achieve climate change and other sustainability goals. We therefore provide a critical reflection on the drivers, barriers, and behavioural implications of choosing an EV.

In the present study, we examined how effectively online travel agencies (OTAs) handle negative e-word-of-mouth on social media platforms like Facebook, Twitter, and Instagram. We collected data from 497 participants using survey method. To test the hypotheses formulated from the existing literature, structural equation modeling was adopted in this study. The results from structural equation modeling indicate effective handling of the negative e-word of mouth (e-WOM) on social media websites significantly affects customer satisfaction and repurchase intention. The current research work provides insight into social media recovery efforts and service fairness when handling negative e-WOM. The study recommends that customers can distinguish the differences between general efforts and adaptive complaint-handling efforts, and dissimilarities may influence satisfaction, repurchase intentions, etc. Although empathy, apology, responsiveness, and paraphrasing are considered pioneer strategies in complaint handling, customers' negative e-WOM, and firms' recovery management, but the current study is among a few to categorize OTAs' handling of negative e-WOM and complaint handling efforts in the social media environment.

This research aims to explore the potential of big data technology for sustainable management and investigate its impact on tourism. Its goal is to obtain meaningful results related to sustainable tourism to understand better how big data technology plays a role in decision-making by looking at it through the lens of various studies.

A systematic review, which is a qualitative method, was used in this study. The analysis was conducted using secondary data from the Web of Science Core Collections databases.

Big data technology has many economic benefits for businesses, but it also has managerial benefits such as forecasting, decision-making and tracking human and machine behaviour. Furthermore, big data technology offers sustainability benefits such as resource efficiency, preventive quality systems, carbon reduction and environmentally friendly production.

Big data's capabilities enable businesses to make more informed business decisions, improve overall business performance and contribute to achieving various SDGs. Big data, which aids in developing smart and sustainable tourism in the tourism sector, assists tourism managers in making economically, socially and environmentally sound decisions.

This chapter examines the internationalisation of public higher education in Brazil using the theoretical triptych of internationalisation as developed by leading scholars in the field: internationalisation at home (IaH), internationalisation abroad (IA), and internationalisation at a distance (IaD). This framework – while rooted in knowledge, systems, and scholarship from researchers and institutions in the Global North – is a constructive tool for categorising and understanding internationalisation at Brazil’s higher education institutions (HEIs) when coupled with an exploration of the history, context, policy, and dynamics of internationalisation efforts. The chapter then summarises and underscores recent and important scholarship by Brazilian researchers and others in the Global South that describes the history of the nation’s internationalisation efforts. It also critiques the powerful influence that Global North-centred objectives and priorities for internationalisation have on the process at Brazilian HEIs. Following a discussion of the theoretical framework and relevant literature, the chapter provides a case study of internationalisation efforts and initiatives of an elite public university, the Universidade Federal de Minas Gerais (UFMG). Content analysis of UFMG’s website and publicly available reports and data demonstrates a high level of institutional internationalisation that has unfolded in recent years, stimulated by federal funding and guided by a strategic framework developed within the Ministry of Education. An analysis of UFMG’s mission, partnerships and programmes finds that the institution serves as an example of internationalisation in Brazil’s public higher education context, as its programmes and initiatives exemplify the overarching objectives of internationalisation in Brazilian higher education.

Tremendous measure of data lakes with the exponential mounting rate is produced by the present healthcare sector. The information from differing sources like electronic wellbeing record, clinical information, streaming information from sensors, biomedical image data, biomedical signal information, lab data, and so on brand it substantial as well as mind-boggling as far as changing information positions, which have stressed the abilities of prevailing regular database frameworks in terms of scalability, storage of unstructured data, concurrency, and cost. Big data solutions step in the picture by harnessing these colossal, assorted, and multipart data indexes to accomplish progressively important and learned patterns. The reconciliation of multimodal information seeking after removing the relationship among the unstructured information types is a hotly debated issue these days. Big data energizes in triumphing the bits of knowledge from these immense expanses of information. Big data is a term which is required to take care of the issues of volume, velocity, and variety generally seated in the medicinal services data. This work plans to exhibit a survey of the writing of big data arrangements in the medicinal services part, the potential changes, challenges, and accessible stages and philosophies to execute enormous information investigation in the healthcare sector. The work categories the big healthcare data (BHD) applications in five broad categories, followed by a prolific review of each sphere, and also offers some practical available real-life applications of BHD solutions.

Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.

Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.

Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.

Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified long-term variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.

Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.

Information and communication technologies can transform how services can be and are delivered as has already happened in other arenas, such as civil aviation, financial services and retailing. Most modern health care is heavily dependent on e-health, including record keeping, targeted information sharing and digital diagnostic and imaging techniques. However, there remains little scientific knowledge base for optimal system content and function in primary health care, particularly for children. Models of Child Health Appraised (MOCHA) aimed to establish the current e-health situation in children’s primary care services. Electronic health records (EHRs) are in regular use in much of northern and western Europe and in some newer European Union Member States, but other countries lag behind. MOCHA investigated the use of unique identifiers, the use of case-based public health EHRs and the capability of record linkage, linkage of information with school health data and monitoring of social media influences, such as health websites and health apps. A widespread lack of standards underlined a lack of research enquiry into this issue in terms of children’s health data and health knowledge. Health websites and apps are a growing area of healthcare delivery, but there is a worrying lack of safeguards in place. The challenge for policy-makers and practitioners is to be aware and to lead on the innovative harnessing of new technologies, while protecting child users against new harms.

Technology-facilitated abuse, so-called “tech abuse,” through phones, trackers, and other emerging innovations, has a substantial impact on the nature of intimate partner violence (IPV). The current chapter examines the risks and harms posed to IPV victims/survivors from the burgeoning Internet of Things (IoT) environment. IoT systems are understood as “smart” devices such as conventional household appliances that are connected to the internet. Interdependencies between different products together with the devices' enhanced functionalities offer opportunities for coercion and control. Across the chapter, we use the example of IoT to showcase how and why tech abuse is a socio-technological issue and requires not only human-centered (i.e., societal) but also cybersecurity (i.e., technical) responses. We apply the method of “threat modeling,” which is a process used to investigate potential cybersecurity attacks, to shift the conventional technical focus from the risks to systems toward risks to people. Through the analysis of a smart lock, we highlight insufficiently designed IoT privacy and security features and uncover how seemingly neutral design decisions can constrain, shape, and facilitate coercive and controlling behaviors.