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A literature review or review article is an integral part of a scientific body of research which synthesizes prior knowledge and provides a holistic overview of a subject domain. While several studies emphasize the significance of literature reviews and include the guidelines for conducting a review, limited studies demonstrated different types of literature review methodologies in a comprehensive way. Accordingly, this chapter presents various types of review methodologies which includes narrative, descriptive, systematic, meta-analysis, hybrid, umbrella, scoping, theoretical, and critical reviews. In addition, the authors' skills including logical reasoning, content analysis, literature mapping, critical writing, and ethical consideration are presented. Further, quality aspects of the literature review are discussed such as the rigor and relevance of the selected studies. Overall, this chapter provides implications for researchers in understanding types of literature review methodologies along with their objectives, strengths, and weaknesses which can assist them in selecting a suitable methodology while conducting a review.

The financial sector’s growing interest in leveraging artificial intelligence (AI) for forecasting has been noted in recent years. In this chapter, we delve into the application of AI in financial forecasting within Indonesia’s stock market. Our primary focus is to assess how AI’s prediction potential can impact investors and financial regulators in this context. Our review spans existing literature on AI and financial forecasting, recent developments in the Indonesian stock market, and ethical and regulatory concerns that surround AI in finance. Our analysis indicates that AI can enhance forecast accuracy in Indonesia’s stock exchange; however, we must also consider limitations and challenges.

To predict the future of the business and implement successful changes, a credit analyst must make quick decisions about the economics and assets of their clients. Because the marketplace is constantly changing, companies that lack the interpersonal skills necessary to communicate with their customers run the risk of falling behind the competition and becoming obsolete. The objective of this research was to assess whether credit analysts in Peruvian banks that used digital resources also improved their communication and interpersonal skills. The study was quantitative in nature, with an applied and correlational design that lacked an experimental component. The sample consisted of 109 credit analysts from four different Peruvian banks (Interbank, Scotiabank, BBVA, and BCP). Two questionnaires were used in this survey; both were submitted to expert review for validation before being submitted for use, and their reliability was determined using Cronbach's alpha. In terms of use of digital resources (59.5%) and mastery of interpersonal skills (61.3%), credit analysts were at the average. Conclusions the p-value for the correlation between credit analysts' use of digital resources and their soft skills in Peruvian banks was less than 0.05, indicating a direct and strong link between these two factors. The Rho correlation coefficient was 0.738.

Due to the armed conflict in Syria that commenced in 2011 and is still ongoing while writing this article, Armenia has experienced a massive inflow of Syrian-Armenian refugees. The state was not prepared to provide the appropriate legal and logistical assistance to manage such a large flow of refugees and returning Armenians. It was also ill-prepared to ensure their resettlement and proper integration into Armenia's society. As a result, in 2012, the government put forward immigration and resettlement policies specifically designed to deal with the Syrian-Armenians fleeing the Syrian conflict. This chapter aimed to capture the results from a conceptual framework we developed to document and assess the Syrian-Armenian integration experience. The framework is based on expert interviews, in-depth interviews and document analysis. Main findings: Syrian-Armenian refugees experience different challenges and choose different ways to overcome them. The older age group tends to experience increased difficulties in integrating into their host communities, while the younger generations adapt easier to their new environment. The younger generations are better equipped for social and economic changes and are less stuck in poverty. For Syrian-Armenian women, their new environment seems safer. Most feel at ease regarding public attitudes towards them and are, for the most, satisfied with their employment choices. Those with a high school education tend to seek lower paying jobs, and the outliers from the highest and lowest socioeconomic status tend to leave Armenia. Many return to Syria as the conflict winds down in certain areas or decide to travel to another country.

Collegiality is often discussed and analyzed as a challenged form of governance, a form of working that used to function well in universities prior to the emergence of contemporary and modern forms of governance. This seems to suggest that collegiality used to dominate, while other forms of governance are now taking over. The papers in volume 86 of this special issue support the notion of challenged collegiality, but also show that for the most part, nostalgic notions of “the good old days” are neither true nor helpful if we are to revitalize academic collegiality. After examining whether a golden age of collegiality ever existed, we discuss why collegiality matters. Exploring what are often described as limitations or “dark sides” of collegiality, we address four such “dark sides” related to slow decision-making, conflicts, parochialism, and diversity. This is followed by a discussion of how these limitations may be handled and what measures must be taken to maintain and develop collegiality. With a brief summary of the remaining papers under two headings, “Maintaining collegiality” and “Revitalizing collegiality,” we preview the rest of this volume.

This chapter provides an overview of the strategic implications of chatbot use and implementation, including potential applications in marketing, and factors affecting customer acceptance. After presenting a brief history and a classification of conversational artificial intelligence (AI) and chatbots, the authors provide an in-depth review at the crossroads between marketing, business, and human–computer interaction, to outline the main factors that drive users' perceptions and acceptance of chatbots. In particular, the authors describe technology-related factors and chatbot design characteristics, such as anthropomorphism, gender, identity, and emotional design; context-related factors, such as the product type, task orientation, and consumption contexts; and users-related factors such as sociodemographic and psychographic characteristics. Next, the authors detail the strategic importance of chatbots in the field of marketing and their impact on consumers' perceived service quality, satisfaction, trust, and loyalty. After discussing the ethical implications related to chatbots implementation, the authors conclude with an exploration of future opportunities and potential strategies related to new generative AI technologies, such as ChatGPT. Throughout the chapter, the authors offer theoretical insights and practical implications for incorporating conversational AI into marketing strategies.

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.