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The last few years have witnessed massive artificial intelligence (AI) and gaming adoption that has navigated the emerging markets. Moreover, according to the WOG summit (world government summit report, by Nielsen) 2020 reports, AI with gaming mechanisms are expected to enrich marketing services in the coming future in the emerging markets. Countries such as India, China and South Korea contribute significantly to this area, and recent forecasting allows the need to increase in emerging markets. Similarly, these countries have a maximum number of youth gamers and AI-driven technology adopters. The adoption of AI-driven technologies and amplification of gamification in marketing services are new phenomena. Moreover, gaming and AI dynamics are relatively new in emerging countries and need greater attention. Thus, this book chapter proposes a dyad model that would explain users' and companies' perspectives to understand the role of AI and gamification for the emerging markets. The chapter will explain how AI-driven gamification helps the users of emerging markets. The chapter will also illustrate how companies in emerging markets use AI for gamification. Therefore, the dyad model would also comprehend the gap, opportunities and challenges in this area and the subsequent strategies to help all the stakeholders.

Instagram is a social media platform that has quickly evolved from a photo-sharing application to a successful marketing tool. It holds various business opportunities, and its rapid growth helps showcase the business offerings. Its innovative use of adverts entices clients, and its visual nature complements the social media marketing strategy. Thus, looking at the dynamic nature and growth of the platform, this chapter outlines the past research trends in the literature of Instagram marketing as the primary objective. This structured review undertakes the study of 76 articles based on defined inclusion and exclusion criteria and analyzes the literature in different contexts. This chapter includes the hybrid review (narrative and framework-based), and findings from the literature highlight a significant influence of Instagram marketing on users. During the brief description of the literature in a different context, the numerous research gaps are outlined with future research directions based on the ADO framework. Lastly, it also presents a broader overview of the managerial and theoretical implications based on the literature of Instagram marketing.

Using the most recent dataset from 2013–2014 to 2017–2018, the study examines the efficiency of 75 coal-fired power plants in the Indian thermal power sector. The authors obtained robust estimates of efficiency scores by employing Seiford and Zhu’s (2002) DEA-based classification invariance technique to account for CO₂ emissions as an undesirable output. Meta-frontier analysis and the Tobit regression are used to compute technology heterogeneity across power plants belonging to public and private groups and investigate the factors driving carbon-adjusted efficiency, respectively. The results reveal that, on average, the efficiency of power plants during the study period is 78.26%, showing significant room for reduction in CO₂ emissions alongside augmentation in electricity generation. Private plants are more efficient than public ones, and relative performance inefficiency is the primary source of inefficiency in the thermal power sector. Regression analysis indicates that domestic-equipped plants perform with lesser levels of efficiency, and plants with more units are more inefficient than plants with fewer units. Carbon productivity significantly improves efficiency since fewer fossil fuels with high carbon will generate more electricity.

Recent research advances in artificial intelligence, machine learning, and neural networks are becoming essential tools for building a wide range of intelligent applications. Moreover, machine learning helps to automate analytical model building. Machine learning based frameworks and approaches allow making well-informed and intelligent choices for improving daily eating habits and extension of healthy lifestyle. This book chapter presents a new machine learning approach for meal classification and assessment of nutrients values based on weather conditions along with new and innovative ideas for further study and research on health care-related applications.