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Electronic word of mouth (eWOM) is a digital marketing method that has been considered by some companies as an effective and efficient approach to enhance social learning and the environment for the customers. A social media user could benefit from gaining information from other users to aid their decision-making process. Instagram is an example of a social media platform that could be utilized for the application of eWOM. It could serve as a source of quality, credible and detailed information, and a channel to increase customer interactions and trust. This study aims to determine the effects of eWOM information generated through Instagram on patients’ intention to join the in vitro fertilization (IVF) programs. By adopting the information acceptance model (IACM) theory, several parameters were evaluated: information quality, credibility, usefulness, adoption, attitude toward information, and purchase intention. The structural equation modeling (SEM) was used to analyze as many as 200 respondents of Morula IVF Jakarta and were active Instagram users. The results showed that the adoption of information and attitude toward information had considerably influenced patients’ intentions to join the IVF programs. Such circumstances might have occurred as a result of the increase in information exchange about IVF, through the discussions and sharing of experiences by patients on Instagram. Furthermore, information usefulness was demonstrated to affect information adoption and was influenced by the credibility of information and attitudes toward information. Nonetheless, one variable which did not display any effects on the usefulness of information was the quality of information. In summary, the characteristics of eWOM information that were conveyed on Instagram could affect the intention of patients in joining the IVF program.

Today, businesses, organizations and governments attach great importance to digital transformation to meet the needs of their customers, business partners, and employees to adapt to the developing technology in recent years. Digital transformation, which is a challenging and mandatory process, has been and continues to be passed by institutions today. However, the successful management of this transformation without conflict can be realized by accurately detecting new communication technologies and examining, understanding, and implementing the transformation process in detail. This process will be painful, where radical changes will take place in the structure, processes, functions, and business models of the organization. Different challenges may be encountered in each of the startup, execution, and governance subprocesses examined in the digital transformation process. Many conflicts such as time and budget shortages, inadequate digital skills and lack of vision for digital customer processes, cybersecurity threats, human resource shortages, difficulty in managing technology, failure to achieve cloud structure integration, vision, and culture differences are the reasons why this process cannot be managed fluently and accurately. For businesses that focus on this goal, regardless of scale, digital transformation has become a necessity, not an alternative to choose. In this study, the digital transformation process and maturity model were discussed, and technological and digital conflicts were emphasized. It seeks to shed light on the work they will do by making recommendations for institutions to manage this process in the best way.

The shift to the fourth industrial revolution presents new sustainable development challenges. The externalities of the second and third industrial revolutions were related to the degradation of the biosphere. It is suggested that the externalities of the fourth industrial revolution, underpinned by artificial intelligence, are extending the repertoire of externalities to include the degradation and manipulation of cognition and by extension the fabric of society. The implications for South Africa’s transformation within the fourth industrial revolution are highlighted. It is shown that digital nudging and choice architecture are tools that are capable of maximising profit while externalising social value through the algorithmic manipulation of our decision-making processes. The case for an alternative explanatory framework is presented because the limitations of the prevailing conception of physical reality appear unable to adequately resolve these super-wicked problems. Constructor theory is introduced as an alternative explanatory framework, and the constructor-theoretic conception is suggested as an ontological framework that will provide better explanations. Some principles of the theory are introduced.

The production of news through artificial intelligence (AI) is a reality in many countries, including the United States, where two leading companies in this area are based. The purpose of this explanatory bibliographic research is to discuss who or what should bear the ethical responsibility for automated writing in a newsroom, considering that codes of ethics address only the conduct of people. This chapter compares the Code of Ethics of the National Federation of Brazilian Journalists with the code used in the United States and highlights examples of news released with incorrect information, produced by algorithms, and published by US news organizations. Facing the current informational scenario, the ethical responsibility of the automated news must be attributed to the media, represented in the figure of the editor. Such a conclusion is motivated by the fact that neither the algorithm nor the person who developed it takes part in the decision-making process of publishing an article or not.

Introduction: Numerous decision-making situations are faced in education where Artificial Intelligence may be prevalent as a decision-making support tool to capture streams of learners’ behaviours.

Purpose: The purpose of the present study is to understand the role of AI in student performance assessment and explore the future role of AI in educational performance assessment.

Scope: The study tries to understand the adaptability of AI in the education sector for supporting the educator in automating assessment. It supports the educator to concentrate on core teaching-learning activities.

Objectives: To understand the AI adaption for educational assessment, the positives and negatives of confidential data collections, and challenges for implementation from the view of various stakeholders.

Methodology: The study is conceptual, and information has been collected from sources comprised of expert interactions, research publications, survey and Industry reports.

Findings: The use of AI in student performance assessment has helped in early predictions for the activities to be adopted by educators. Results of AI evaluations give the data that may be combined and understood to create visuals.

Research Implications: AI-based analytics helps in fast decision-making and adapting the teaching curriculum’s fast-changing industry needs. Students’ abilities, such as participation and resilience, and qualities, such as confidence and drive, may be appraised using AI assessment systems.

Theoretical Implication: Artificial intelligence-based evaluation gives instructors, students, and parents a continuous opinion on how students learn, the help they require, and their progress towards their learning objectives.

It is important to implement artificial intelligence (AI) because it can simplify and solve complex problems faster than humans. Because AI learns about people and their behavior from the first purchase, AI marketing can boost marketing efforts by leveraging data to target extremely precise consumer groups. There is a debate about the efficacy of AI marketing due to the constraints and limits imposed by the system's nature. This chapter presents insights from published studies regarding the relationship of AI with marketing and how AI can affect marketing. A real-world example of Netflix's usage of AI in marketing has been demonstrated. Then, consumer attitudes regarding AI were revealed. Then, several ethical considerations concerning AI were highlighted. Finally, the anticipated future of AI marketing was addressed. This chapter demonstrated the significance of firms implementing AI marketing to get a competitive advantage. Although some of the difficulties mentioned in this study need to be resolved, AI marketing has a bright future. There are ethical concerns about bias and privacy that should be addressed further. This chapter will encourage firms to use AI systems in marketing, and it will open the door to concerns that will need to be investigated academically in the future.

This chapter provides an overview of the status of applied econometric research in hospitality and tourism management and outlines the econometric toolsets available for quantitative researchers using empirical data from the field. Basic econometric models, cross-sectional models, time-series models, and panel data models are reviewed first, followed by an evaluation of relevant applications. Next, econometric modeling topics that are germane to hospitality and tourism research are discussed, including endogeneity, multi-equation modeling, causal inference modeling, and spatial econometrics. Furthermore, major feasibility issues for applied researchers are examined based on the literature. Lastly, recommendations are offered to promote applied econometric research in hospitality and tourism management.