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This study aims at stirring up the existing research conducted in the field of creative economy (CE) and also in the context of Industry 5.0. CE encompasses all the creative industries/businesses which form a major part of the knowledge-based economy. The functionalities of these setups, their global trends and developments are to be assessed for a better understanding of its present circumstances and its prospective opportunities by augmenting Industry 5.0 and its core principles. This provides a comprehensive illustration to enhance the economic, social, creative and sustainable performances of the creative industry. In addition, the study also seeks to identify the dynamics of creative units and how it could highly contribute to the glorification of the creative and cultural history in the Indian economic backdrop.

The study adopts a systematic literature review process to fulfill the research objective. Four critical databases in Scopus such as Emerald Insight, Springer Link, Sage Publications and Taylor and Francis have been chosen for the review process. Following the critical literature review process, the chosen articles from each database have been retrieved for an exhaustive analysis within a time frame of 2013–2023 to evaluate the research evolution on the subject area.

The paper identified various research dimensions and perspectives of the researchers in the area of study. This gives a platform to extensively evaluate the capabilities and functionalities of the sector for strategy building and enhancing returns from the sector.

As the methodology was restricted to top 5 articles from 5 important databases, the study was limited to only those articles and the other open-access peer-reviewed articles/journals/databases have not been considered which is a major limitation. Alongside, as the time frame was restricted for a period of 10 years and only English language papers were chosen, prior study has not been considered, which is also a key limitation to the study.

Policymakers, i.e. government and institutions, can understand the existence and contribution of the CE in different geographical regions for a specified period of time. This helps them understand the new revolution, Industry 5.0, and how they could merge their concepts to bring innovations in the sector and support in building sustainable cities in the emerging economies.

As the paper works on bringing out the viewpoints of multiple authors and research works, it is considered to be a novel study as none of the previous studies, especially systematic literature review works, have been done only in high-quality journals of Scopus database. Therefore, the study holds high-quality information which can be significantly used by creative business units.

In this chapter, we describe the belief system of Izzat which is central among South Asian families. The idea of forced marriage is based upon the concept of Izzat or honor which is a cornerstone of family life in South Asian communities.

Rai (2006) suggests that South Asian community members are deeply affected by what others say about them. The closest English translations to Izzat and Sharam are honor and shame, respectively. Rai argues that Izzat and Sharam are mechanisms that safeguard patriarchal customs such as arranged marriage which are familiar to us from our own backgrounds as two Asian women. It is our belief that Izzat is the highest “context marker” (Pearce & Cronen, 1980) for forced marriages.

We will illustrate the concept of Izzat through two case vignettes and explicate theoretical ideas, based on Izzat to include Borzemyi-Nagy’s ideas about belief systems.

The research of Ryan Brown (2016) University of Oklahoma on “honour cultures” in the USA draws some parallels in gendered discourses about power of men over women. He suggests that high levels of murder rates as well as reluctance to address mental health issues are present in “honour cultures.” These ideas resonate with the strong influence of Izzat upon South Asian family and community systems which we have met in our practice. The development of our practice was in response to issues arising from our clinical work in these communities (Robinson, 2016).

We will explore the continuum of marriage to include forced, arranged and consensual marriage within the context of Izzat and compare with black African and African-Caribbean families.

We will also consider issues of cultural competence and expertness and how this interplays with strongly held belief systems such as Izzat. We will end with some clinical implications and pointers for practice.

This study aims to promote the need for advanced skills acquisition within the LIS and academic libraries. This study focuses on the importance of library management systems and the need for the graduates to be equipped with analytics skills. Combined with basic data, text mining and analytics, knowledge classification and information audit skills would benefit libraries and improve resource allocation. Agile institutional libraries in this big data era success hinge on the ability to perform depth analytics of both data and text to generate useful insight for information literacy training and information governance.

This paper adopted a living-lab methodology to use existing technology to conduct system analysis and LMS audit of an academic library of one of the highly ranked universities in the world. One of the benefits of this approach is the ability to apply technological innovation and tools to carry out research that is relevant to the context of LIS or other research fields such as management, education, humanities and social sciences. The techniques allow us to gain access to publicly available information because of system audits that were performed. The level of responsiveness of the online library was accessed, and basic information audits were conducted.

This study indicated skill gaps in the LIS training and the academic libraries in response to the fourth industrial technologies. This study argued that the role of skill acquisition and how it can foster data-driven library management operations. Hence, data mining, text mining and analytics are needed to probe into such massive, big data housed in the various libraries’ repositories. This study, however, indicated that without retraining of librarians or including this analytics programming in the LIS curriculum, the libraries would not be able to reap the benefits these techniques provided.

This paper covered research within the general and academic libraries and the broader LIS fields. The same principle and concept is very important for both public and private libraries with substantial usage and patrons.

This paper indicated that librarianship training must fill the gaps within the LIS training. This can be done by including data mining, data analytics, text mining and processing in the curriculum. This skill will enable the news graduates to have skills to assist the library managers in making informed decisions based on user-generated content (UGC), LMS system audits and information audits. Thus, this paper provided practical insights and suggested solutions for academic libraries to improve the agility of information services.

The academic librarian can improve institutional and LMS management through insights that are generated from the user. This study indicated that libraries' UGC could serve as robust insights into library management.

This paper argued that the librarian expertise transcends information literacy and knowledge classification and debated the interwoven of LMS and data analytics, text mining and analysis as a solution to improve efficient resources and training.

The role of personal value systems as antecedents to risk has been largely ignored. Following Gigerenzer's view of ecological rationality, the authors argue an individual's personal value system serves as concrete motivations that guide risky choices and facilitate adaptation to one's environment.

The authors elicit risk attitudes using a satisficing-based risk elicitation method that exploits the idea of worst-case aspiration or minimum portfolio returns given a portfolio comprising a safe and risky prospect. The elicited worst-case aspiration allows for more descriptive and natural ways of characterizing attitudes to risk (i.e. satisficing measures of risk). Using the Schwartz Value Survey, the authors assess the relative importance individuals place on value systems, such as personal focus versus social focus. The authors argue that preference to value systems has linkages with the worst-case aspiration setting emphasized in the satisficing task.

This study’s findings suggest that individuals who are willing to give up higher potential returns to protect their downside risk (by setting higher worst-case aspiration) are positively associated with personal focus—concern about own outcomes than social focus—concern about the outcomes for others or established institutions.

Currently, the study’s setting is in the domain of financial decision-making. Going forward, milestones could be set for studying risky real-world choices by simply changing the risk measure in different contexts, such as job choices, education, health and social interactions.

This study contributes to the discussion on the psychometric structure of risk. Prescriptive benefits of satisficing as a positive heuristic, which is interpreted as setting achievable goals or aspiration levels, are extensive and recognized in various industries ranging from agriculture, airlines, insurance to financial advising. More recently, cognitive processes, such as emotions and personal value systems, are recognized as a type of social cognition that subserve heuristic functions that can guide behavior quickly and accurately.

The purpose of this research study is to explore simple collaborative technique for teaching mathematics and thus improving the problem solving skills of the students. Better pedagogic activities are required to motivate the students to perceive mathematics as a subject that stimulates problem-solving skills required for engineers.

This paper presents a research study on the application of technology-supported collaborative learning in improving the problem-solving skills of first-year engineering students in a mathematics course. The experiment was conducted in a mathematics course “Engineering Calculus” with 286 first-year engineering students in two groups: experimental group (N = 60) and controlled group (N = 226). The academic performance of the students was measured and analyzed with statistical techniques.

From the results obtained, it was found that the academic performance of the experimental group was better than the controlled group. Also, interest shown by the students in the topic that dealt with collaborative learning was more than in other topics taught using conventional teaching methods.

The teachers are required to find effective pedagogical activities to improve the problem-solving skills in mathematics. The research work proposes a collaborative method in mathematics for attaining higher cognitive level in an entry level engineering course. The limitation of this study lies in group formation techniques and the grading policy which deals only with individual assessment scores.

Practice of collaborative learning is made easy with simple technology. A clear strategy for the conduct of collaborative learning sessions has been presented. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

Using technology in mathematics teaching may not be very easy for all teachers. Especially, for an undergraduate engineering fresher, mathematics may not be a very easy task. This work shall bridge the gap with simple technology-assisted collaborative learning. The teachers need not spend too much time and effort in learning technology for mathematics teaching. The effect of this learning is significant in terms of the performance and satisfaction evaluation.

This work presents a systematic implementation of collaborative learning that shall result in improved problem-solving skills and engaging learning sessions. The statistical analysis methods and the visualization of obtained results shall help in understanding the implications of the presented work. Practice of collaborative learning is made easy with simple technology. The solutions recommended are practically feasible and does not require any special infrastructure or specific training.

Introduction: Historically, the corporate governance (CG) framework was designed primarily to safeguard the economic interests of shareholders, as a result of political and legal interventions, developing into an effective instrument for stakeholders and society in general.

Purpose: The core objectives of the study include: identifying journals/publications responsible for publishing CG studies in India, key CG issues covered by CG researchers, the amount of high-impact CG literature across different time periods, sectors/industries covered by CG researchers and different research instruments (quantitative or qualitative) used in CG studies in India.

Design/methodology: The chapter used a sample of 130 corporate governance studies that fulfil the selection criteria, drawn from the repository of over 100 reputed journals that are either recognised by the Australian Business Deans Council (ABDC) or indexed by SCOPUS. A systematic literature review has been carried out pertaining to CG issues in India, based on various statistical tools, data, industries, research outlets & citations, etc.

Findings: The results show an overwhelming number of studies have assessed the relationship between CG variables and firm performance, which could be measured through a variety of performance metrics such as ROA and ROI. Apart from empirical analysis, many conceptual studies use repetitive basic statistical tools like descriptive statistics or regression analysis. The chapter offers insights into current achievements and future development.

Originality/value: This bibliometric study is a useful guide for policymakers, corporate leaders, research organisations and management faculty to draw insights from work produced by eminent researchers in GC in India.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

Introduction: This chapter is intended to link the embracing strategy of ‘socially responsible investment’ with the apparent cause of economic destruction ‘financial crimes’. Today’s financial world is not always associated with ethics and morality, but it does not mean rising investments cause rising financial crimes. Socially responsible investing (SRI) has been rising, and many of today’s investors are interested in tracking ethically sound companies. Investors find a great way to invest around many investment opportunities, while socially responsible investors work with little social cause. This increasing literacy over SRI notably helps to reduce investments in unethical grounds which in turn reduces financial crimes.

Design/methodology: This work is premised on desk research. Conceptual and documentary methods were used in the study. The tertiary data source has been used in the study to develop a template describing the working of SRI in fixing financial crimes.

Findings: Findings of this study detail: a breakdown of industries that comes under SRI, channels of financial crimes, impact of SRI on financial crimes, and design an action plan for more effective environmental, social, and governance (ESG)-based investments to fix problems of financial crimes in the Indian economy.

Practical implications: The model of SRI has unfolded these days. While the purpose of these funds differs, they generally swear off the weapons industry and avoid ‘sin stocks’. In-depth analysis of this study area enables building quality investment strategy among investors and thereby helps to combat financial crimes.

The purpose of this study was to analyze the influence of epichlorohydrin (ECH) concentration and reaction time on the food-grade resistant starch production and its pasting properties by using native cassava starch of Misiones-Argentina origin.

Cassava starch was modified using ECH (0.30 and 0.15 per cent) during 4 or 8 h. Digestibility was evaluated by determining resistant starch as total dietary fiber. Pasting properties and the cross-linking degree were studied using a micro-viscoamylograph (Brabender).

Resistant starch content was not influenced by ECH concentration and reaction time. Cross-linking was detected at higher reaction times (8 h) and ECH concentrations (0.30 per cent), where a decrease in viscosity peaks by more than 80 per cent was observed. Both pasting temperature and breakdown were increased, whereas a decrease in retrogradation was detected.

Starches can be suitable for different food applications. This is because of the ability to modify its pasting properties and the invariability of the in vitro digestibility of cassava starch as a result of using ECH (at concentrations approved by local and regional legislation) and reaction times of 4 and 8 h.

Information related to the modification of cassava starch using ECH is scarce or not available nowadays in literature.

Affective states in learning have gained immense attention in education. The precise affective-states prediction can increase the learning gain by adapting targeted interventions that can adjust the changes in individual affective states of students. Several techniques are devised for predicting the affective states considering audio, video and biosensors. Still, the system that relies on analyzing audio and video cannot certify anonymity and is subjected to privacy problems.

A new strategy, termed rider squirrel search algorithm-based deep long short-term memory (RiderSSA-based deep LSTM) is devised for affective-state prediction. The deep LSTM training is done by the proposed RiderSSA. Here, RiderSSA-based deep LSTM effectively predicts the affective states like confusion, engagement, frustration, anger, happiness, disgust, boredom, surprise and so on. In addition, the learning styles are predicted based on the extracted features using rider neural network (RideNN), for which the Felder–Silverman learning-style model (FSLSM) is considered. Here, the RideNN classifies the learners. Finally, the course ID, student ID, affective state, learning style, exam score and course completion are taken as output data to determine the correlative study.

The proposed RiderSSA-based deep LSTM provided enhanced efficiency with elevated accuracy of 0.962 and the highest correlation of 0.406.

The proposed method based on affective prediction obtained maximal accuracy and the highest correlation. Thus, the method can be applied to the course recommendation system based on affect prediction.