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In the digital era, the dynamics of the communication process in classrooms have changed significantly. With the help of computer-mediated communication techniques, especially animation, teachers can deliver a distinct learning experience to students that can be fun, while presenting complex ideas in simpler forms. The purpose of this paper is to assess the effectiveness of animation in education, in the context of developing Asian countries, using Karnataka, an Indian state, as a study area.

This paper uses the field experimental method to assess animation’s effectiveness in education. Attempts are made to neutralize the influence of extraneous factors, such as psychological conditions, and the socio-economic background of students, while assessing academic performance. To achieve this, a fuzzy-set-theory-based two-sample statistical hypothesis test is used.

Results indicate that animation can be used as an effective tool for communication in pedagogy and, if used properly, can improve students’ academic performance in primary education, even in developing countries such as India.

The paper’s limitations are explored, and point to how future research could use more advanced statistical tools to identify the motivational, behavioral, cognitive and psychological factors influencing students, when animation is used in education, and should perform a comparative analysis of the performance of students in developed and developing countries.

As the current study proves that animation is effective in education, even in developing countries such as India, efforts should be made to convert existing curricula into animated multimedia content. Currently, most government-run schools in India use traditional chalk-and-talk methods for teaching. The use of animated instructional material will help improve the standard of educational communication in classroom, and maintain consistency in delivering the curriculum.

This study aims to investigate the key factors that motivate learners to use handheld devices to access library resources. To do so, this study integrates the technology acceptance model (TAM) and the DeLone and McLean information systems success (D and M-ISS) model.

The relationship between the causes and the outcomes may not be symmetrical. To test this proposition, data were collected from 210 respondents in a Gulf country and analysed using structural equation modelling (SEM) and complemented by fuzzy set qualitative comparative analysis (fsQCA).

The SEM results revealed that three constructs – perceived ease of use (PEOU), service quality (SQ) and system quality (SEQ) are strong drivers of students’ continuous intention to use handheld devices to access library resources. However, perceived usefulness (PU) and information quality (IQ) do not significantly influence students’ intentions. Besides, SQ and PEOU are positively related to PU. Furthermore, fsQCA results show that two different conjunctions, PU*PEOU*IQ*SEQ and PEOU*SQ*IQ*SEQ, cause the students to show a continuous intention to use handheld devices to access library resources.

Unlike previous studies on mobile library resource utilization, this analysis extends TAM to investigate the linear additive influence of two basic TAM constructs: PEOU and PU, and three constructs, namely, SEQ, SQ and IQ of the ISS model, on students’ library resource utilization. Furthermore, the findings of SEM were complemented by a set theory-based configuration method, fsQCA, to investigate the asymmetrical, equifinal and configurational causation leading to the desired outcome. The findings of this study have theoretical and practical implications.

The purpose of this study is to analyze the combined effect of eight factors – performance expectancy (PE), effort expectancy (EE), hedonic motivation (HM), system quality (SQ), information quality (IQ), service quality (SEQ), digital literacy (DL) and computer anxiety (CA) on learners’ behavioral intention (BI) toward the adoption of e-learning in higher education institutions (HEIs) in India.

The study used factors from two theoretical models, the extended Unified Theory of Acceptance and Use of Technology and the DeLone and McLean Information Systems Success model. The study also considered DL and CA as additional factors because they could affect a learner’s intention in a developing country like India. Data were collected from three HEIs in Southern India and analyzed using fuzzy qualitative and comparative analysis (fsQCA).

The results of the study emphasize the importance of considering both individual and technological factors in e-learning adoption and provide evidence for the significance of integrating multiple theories in understanding the complex relationship between factors and learners’ BI. Four different configurations of the eight factors: EE*HM*SQ*IQ*SEQ*DL*∼CA; PE*EE*HM*SQ*IQ*DL*CA; PE*EE*HM*IQ*SEQ*DL*CA; and PE*EE*SQ*IQ*SEQ*DL*CA found to be sufficient to cause learners’ BI to use e-learning.

This study explores the complex relationship between different factors and learners’ intention to adopt e-learning using the fsQCA method. These findings may need further validation in HEIs across different geographical locations.

This study provides practical insights for HEIs in India and other developing countries on how different factors combine and interact to determine e-learning adoption in multiple contexts.

Using fsQCA as a novel and rigorous method, this study uncovers the complex and nonlinear causal relationships among various factors that affect e-learning adoption. This study provides a holistic and contextualized understanding of e-learning adoption in a developing country’s scenario. This study can inform educators and policymakers on how to design and implement effective e-learning strategies that suit different learner profiles and contexts.

Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar.

CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI).

The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers.

The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.

Purpose: The aim of this chapter is to provide a quantitative literature review on machine learning (ML) and artificial intelligence (AI) in the Insurance Sector.

Need for the Study: The current study maps the literature regarding AI and ML in the insurance sector through bibliometric tools to identify the significant gaps in the available literature, considerable insights that emerged, and a scientific evaluation of AI and ML in the Insurance sector.

Methodology: The VOS viewer method was used to conduct the depth and quantitative analysis of the AI and ML in Insurance. The study of 450 articles has been retrieved through the Scopus database from 2012 to 2021. The implication of performance analysis methods has helped to explore influential journals, authors, countries, Keywords, and affiliations, elevating the literature in AI and the Insurance Sector.

Finding: This study conducts an exploratory analysis and identifies the prominent authors, sources, countries, affiliations, and articles using modern bibliometric analysis (BA) tools. The geographic scattering of the study indicates that the USA and the UK have highly influential publications and contribute to AI and Insurance. East and Southern Asia countries are far behind.

Practical Implication: Furthermore, this chapter can be used as a reference paper to explore the new field of study in the insurance sector using AI. The search criteria were set in the study to limit the sample published papers/articles included in Scopus data based on the AI and ML in Insurance.