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Post-pandemic sovereign authorities in several economies have nudged primary education institutions to adopt platform-based teaching. The shift to platform technology attempts to ensure continuity in the teaching–learning process. In the context of predominantly digitally mediated teaching process, this shift may exacerbate disparities and social injustice by limiting access to primary education in resource-constrained developing economies. The purpose of this study is to explore the efficacy of such a digital framework provided by government and private partners and the challenges faced by the teachers in absence of proper scaffolding.

Using an integrative theoretical framework that is composed of capability theory, technology adoption theories and the scaffolding framework, this paper analyses the challenges faced by primary school teachers when adapting to platform-based teaching. Social media analytics along with text analytics using Natural Language Processing and latent Dirichlet allocation-based topic modelling approach to extract latent topics or themes used by users during their tweets related to e-teaching.

The findings of this study highlight that adopting a platform-based and hybrid approach improves access to education and flexibility and highlights the importance of scaffolds in achieving desired learning outcomes. EdTech companies can play a significant role through private-public partnership models to offer technical scaffold. Collaborative efforts between educational institutions and EdTech service providers are essential for ensuring the long-term sustainability of platform-based teaching and learning.

After the pandemic, there has been no published literature available which examined the role of scaffolds and EdTech companies in ensuring digital ecosystem for better teaching–learning outcome through platforms.

This chapter explores the multifaceted relationship between quantum computing (QC) and sustainability, with a focus on the Quadratic Unconstrained Binary Optimisation (QUBO) framework. The manuscript delves into the theoretical underpinnings of QUBO and its formulation as a quantum annealing problem, identifying the quantum principles that facilitate the resolution of such optimisation challenges. It offers a critical analysis of the suitability of QUBO for unconstrained problems and its efficacy in consistently locating the global minimum – a pivotal concern in optimisation tasks. Further, this study provides a nuanced discussion on the intersection of QC and sustainability. It delineates the types of optimisation problems within sustainability initiatives that are amenable to formulation as QUBO problems, while also highlighting sustainability challenges that elude the QUBO framework. It argues for the integration of quantum solutions into business operations, highlighting the potential for QC to play a transformative role in achieving sustainability objectives. The critique of the current hype surrounding QC provides a balanced viewpoint, ensuring a grounded approach to the adoption of quantum technologies in tackling pressing global issues.

As technology becomes more widely accessible, there is a growing concern about internet gaming disorder (IGD) around the world. The aim of this study is to evaluate the prevalence of IGD and also assess the effects of depression, anxiety, stress and insomnia levels on the IGD of youths in Bangladesh.

A cross-sectional quantitative study design was used to collect data from the youths of different locations in Bangladesh between October 21, 2023 and January 15, 2024. A total of 501 samples were collected using the convenience sampling technique. The following measurement scales were Internet Gaming Disorder Scale short form, depression, anxiety and stress scales and insomnia severity index, which were used to assess scores for IGD, psychological distress and insomnia, respectively.

The study found that the prevalence of IGD was 9.8%. IGD was shown to be significantly associated with depression, anxiety and stress. Aside from that, IGD and insomnia had a significant association, as did a friendly family environment.

The generalizability of the results could be improved by conducting additional studies with a more diverse sample, such as the general population or a different age group.

The study will help the government reduce the prevalence of IGD, improving the mental and physical health of youth.

No research has been conducted on youth and different professions in Bangladesh. There has also been very little research on the prevalence of gaming addiction and mental health.

In the modern financial landscape, Artificial Intelligence (AI) is gaining prominence, offering significant economic advantages. This research paper aims to investigate the impact of Behavioural Biases (BB) such as Overconfidence Bias (OCB), Fear of Missing Out (FOMO), Herding Bias (HB) and Regret Aversion Bias (RAB) on Investment Decision-Making (IDM). Additionally, it explores how the AI-led Adoption of Digital Advisory Services (ADAS) moderates these biases among Gen Z investors in India.

The study utilized a convenience sampling method, gathering 457 responses from Gen Z investors in India through an online survey questionnaire. The data was analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM).

The results confirm a significant relationship between OCB, FOMO, HB and RAB on IDM. The study also found that ADAS significantly moderated the relationship between FOMO and IDM, as well as between HB and IDM. However, the moderation effect of ADAS was not supported for the relationships between OCB and IDM, and RAB and IDM.

This research offers valuable insights for academics, individual investors, fintech companies and policymakers. It highlights how behavioural biases affect IDM and underscores the importance of AI-enabled digital services in helping Gen Z investors recognize and manage these biases. Policymakers can use these insights to establish standards for AI use, ensuring regulatory compliance and promoting ethical conduct in AI-driven investment decisions.

The novelty of this study lies in its conceptual approach, particularly in examining the moderation role of ADAS in addressing behavioural biases among Gen Z investors.

The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field.

This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas.

The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan.

This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their operational environments.

A secondary research method was used to collect data for this case. The authors have made use of newspaper articles and articles by experts published in the public domain.

This case discusses the dilemma faced by Amazon Prime Video in India regarding content. Amazon Prime Video attained success and rapid growth in India ever since its entry into the Indian over the top (OTT) market in 2016. However, the pursuit of attractive and bold content landed Amazon Prime Video in a legal tangle in India. Amazon Prime Video was accused of hurting the religious and political sentiments of Indians by broadcasting bold shows like Tandaav, Family Man, Mirzapur, Family Man 2, etc. Litigations against Amazon Prime Video were filed in the Indian courts by members of religious and political organizations. Protests and online campaigns on Twitter caught the attention of internet influencers in India. The key dilemma faced by the protagonist in this case is whether to continue streaming attractive content that may be controversial and may occasionally hurt the religious/political sentiments of some Indians or stream only safe content that may be deemed as boring by its young target audience.

Undergraduate and postgraduate students studying marketing management and international business courses in business management and commerce streams can use this case.

Generative artificial intelligence (GAI) can disrupt how consumers search for information on retail products/services online by reducing information overload. However, the risk associated with GAI is high, and its widespread adoption for product/service information search purposes is uncertain. This study examined psychological drivers that impact consumer adoption of GAI platforms for retail information search.

We conducted 31 in-depth, semi-structured interviews with the lead GAI users regarding product/service information search. The data were analysed using a grounded theory paradigm and thematic analysis.

Results show that consumers experience uncertainty about GAI’s functioning. Their trust in GAI impacts the adoption and usage of this technology for information search. GAI provides unique settings to investigate potential additional factors, leveraging UTAUT as a theoretical basis. This study identified three overarching themes – technology characteristics, technology readiness and information characteristics – as possible drivers of adoption.

Consumers seek exhaustive and reliable information for purchase decisions. Due to the abundance of online information, they experience information overload. GAI platforms reduce information overload by providing synthesized and customized product/service search results. However, its reliability, trustworthiness and accuracy have been questioned. The functioning of GAI is opaque; the popular technology adoption model such as UTAUT is general and is unlikely to explain in totality the adoption and usage of GAI. Hence, this research provides the adoption drivers for this unique technology context. It identifies the determinants/antecedents of relevant UTAUT variables and develops an integrated conceptual model explaining GAI adoption for retail information search.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

This study aims to investigate the effects of three dimensions of ChatGPT strategic value – organization support (OS), managerial productivity (IM) and decision aids (DA) – on the business sustainability (BS) of Malaysian public universities.

A quantitative methodology was adopted for this study to examine the relationships between ChatGPT strategic value and the BS of Malaysian public universities.

The study found that two dimensions of ChatGPT strategic value, namely, OS and IM, influence BS, whereas DA do not.

To the best of the author’s knowledge, this study is the first to address the relationship between ChatGPT strategic value and BS in a specific context – Malaysian public universities – providing new contributions to theory by extending the literature on the topic.

The findings are expected to guide universities in Malaysia in leveraging ChatGPT strategic value for enhancing BS.

To the best of the author’s knowledge, this empirical study is the first in the literature to examine the relationships between ChatGPT strategic value and BS in the education sector. Supported by an original conceptual model, the insights provided should extend the literature dedicated to ChatGPT strategic value and BS in the context of a South Asian economy.