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Vaccine hesitancy is a long-standing issue among both the general population and health communicators. This study aims to ascertain the inclination and the reasons for vaccine hesitancy by conducting content analysis and sentiment analysis of the perspectives expressed in comments on videos related to vaccine hesitancy uploaded from India on YouTube.

The assessment of the sentiments of the vaccine-hesitant population is done using Valence Aware Dictionary and sEntiment Reasoner sentiment analysis module implemented with Python’s NLTK library to automatically determine the sentiments of the comments. Manual content analysis was performed on 60.09% viewer comments randomly selected from the total comments in 238 videos on vaccine hesitancy originated from India and labelled each comment with labels “Anti”, “Pro”, “Confused”, “Not Applicable” and “Unrelated” labels.

The study found “Mistrust-Government policies”, “Fear-health related consequences”, “Mistrust-Scientific research”, “Vaccine effectiveness and efficacy” and “Misinformation/myths” as the top five determinants for vaccine hesitancy, whereas “Religious beliefs”, “Fear-Economic consequences”, “Side Effects- short-term” and “Fear-mode of administration” found to be the lesser cited reasons for vaccine hesitancy. However, the study also investigates changes in the inclination of Indian commenters towards vaccine hesitancy and revolving issues over time.

Public health policymakers and health communicators may find the study useful in determining vaccine hesitancy factors in India.

The originality of this study lies in its approach. To date, no sentiment analysis has been conducted on the content released on YouTube by Indian content creators regarding pro- and anti-vaccination videos. This inquiry seeks to fill this research gap.

This study aims to examine the impact of board human capital diversity, measured by educational qualification diversity and gender diversity on the financial performance of Indian firms after controlling corporate governance (CG) and firm-specific variables.

This study is based on a panel data set of top 100 listed Indian firms for a period of five years. The authors use Blau index and Shannon index to compute qualification diversity. The authors use three-stage least square (3SLS) model to deal with the potential endogeneity issue in the association of human capital diversity variables and other CG variables with firm performance. Further, the authors adopt generalized estimating equation (GEE) model for robustness check.

The authors find a significant positive impact of board’s educational diversity as well as gender diversity on the financial performance of firms. Additionally, they extricate highly significant positive interaction impact of board’s educational diversity and gender diversity on the financial performance of firms. Further, the results indicate a significant positive impact of board size, board independence, ownership concentration, family ownership and audit committee independence on firm performance, while CEO duality exhibits a significant negative impact on firm performance.

This study fills the existing gap in literature by extending the performance implications of board’s human capital diversity for top listed Indian firms.

This paper aims to examine the impact of gender diversity both at operational and leadership levels on the financial performance of firms in India.

The study is based on a panel data set of 100 large Indian corporate firms. This study uses the Blau index and Shannon index to compute gender diversity. First, this paper uses system generalized method of moments model to deal with the potential endogeneity issue in the association between gender diversity and firm performance. Second, to unveil heterogeneity in such a relationship, the study applies panel data quantile regression model. Finally, the study adopts a generalized estimating equation model to investigate such relationships for group affiliated and standalone firms.

This study finds a significant positive impact of workforce gender diversity and board gender diversity on the financial performance of firms. Further, the results of the quantile regression model indicate that the impact of gender diversity (workforce and board) on firm performance is more pronounced at higher quantiles of the conditional distribution of firm performance. However, the study fails to extricate any significant impact of audit committee gender diversity on firm performance. Finally, the study also finds a significant positive impact of gender diversity at both workforce and board level for a group affiliated, as well as standalone firms.

The present study makes a novel contribution to the extant literature on the association between gender diversity and financial performance of firms by examining such diversity at both operational and leadership levels in the context of an emerging country such as India that captures the complex realities pertaining to gender issues. Further, the study contributes to the empirical literature regarding the heterogeneous impact of gender diversity on firm performance in the Indian context.

Given the relevance of female directors in the governance of any firm, this paper aims to examine their effect on firms’ financial performance by investigating their general impact and segregating the same into different subgroups based on Kanter’s theory.

To achieve the purpose, this study selects a sample of the top 100 listed Indian firms for the period of 2014–2018 and gathers the data pertaining to the variables under consideration from the respective firms’ annual report and corporate database Capitaline Plus. For undertaking the investigation, the authors have segregated the sample into three groups, i.e. firms with boards having less than 10% of female directors are called skewed boards; firms with boards having female directors that range from 10% to 20% are called as tilted board; and firms with boards having sizable representation of female directors of above 20%. To examine the performance impact of overall female directors and their different subgroups, the authors have used a generalized estimating equation model. For the robustness test, the authors have used the fixed-effect model.

The authors find a significant positive impact of the overall percentage of female directors on the financial performance of firms. Additionally, the results indicate that boards with a titled group of female directors and boards with a sizable representation of female directors significantly positively impact firms’ performance. However, the authors fail to extricate any significant performance impact of boards with a skewed group of female directors.

First, the study reveals that despite prevailing nepotism in India, female directors, owing to their core characteristics, can create a favorable perception of firms in the market. Second, it also works as an eye-opener for regulators by revealing the minimum threshold for female directors that a board should have to exploit the benefits of a gender quota rather than mere compliance with the requirements of the Companies Act, 2013. Third, it implies that more gender-diverse boards can improve a firm’s financial performance only if female directors range between the thresholds of 10% to 20%. Finally, the finding is significant for changing the business culture in India, where institutions are traditionally less supportive of women than in other emerging countries.

Departing from existing studies, which provide evidence on the performance impact of the overall percentage of female directors, the study unveils the differential impact of female directors on firms’ financial performance depending on their level of representation on the board. To the best of the authors’ knowledge, this is the first study in the context of an emerging market to test Kanter’s theory.

Pakistan's present war against extremists has many folds and sheds. The country's initial participation in the Afghan War in 1979 later gave birth to different extremist trends in the country. State patronage of the extremist Wahabi Islamists during the Afghan jihad opened another conflict in Pakistan, and things became more complicated. The combination of external and internal factors gave birth to the worst kind of conflict, which now has not only become dangerous for the country's own existence but also a major threat for global peace. The Afghan jihad initially started as a war against Soviet occupation and later became the hub of global jihad-war against infidels.

This chapter analyzes how external factors promoted internal contradictions in Pakistan due to which the country became not only an exporter of jihadis for the world but also the worst kind of sectarian conflicts, including. Shia–Sunni, Deobandi–Wahabi clashes, entered into in the past two decades. Such a strong link exists with Pakistan's official support to global jihad. Draft sectarian groups now head to head with their opponents have killed thousands of members of rival sectors, have strong support from external sympathizers, and have spread in the country. The well planned terrorist activities of these groups reflect the fact that support to these groups in the past is now leading to a severe crisis in Pakistan. The nexuses of these indigenous extremists like Lashkar-e-Jhangvi, Lashkar-e-Taiba, Tehrik-e-Taliban Pakistan, and Hizb-ul-Mujahideen with external terrorist organizations like Al-Qaeda, the Taliban, and Islamic Movement of Uzbekistan of Tahir Yuldasher Chechen Guerilla War has led to several bloody clashes in the country and outside.

The aim of the research is to identify and prioritise the implementation challenges of blockchain technology and suggests ways for its implementation in supply chains.

Underlined by the technology, organisational, and external environment model, a conceptual framework with four challenge categories and sixteen challenges is proposed. Data collected from three stakeholder groups with experience in the implementation of blockchain technology in India is analysed by employing an analytical hierarchy process method-based case study. Further, a criticality–effort matrix analysis is performed to group challenges and suggest ways for implementation.

The analysis revels that all stakeholders perceive complexity challenge associated with the technology, organisational structure, and external environment, and issues of compatibility with existing systems, software, and business practices to be high on the criticality and effort scales, which thus require meticulous planning to manage. Likewise, top-management support issues related to insufficient understanding of how technology fits with the organisation’s policy and benefits offered by the technology requires high effort to address this challenge.

The results were obtained by focusing on the Indian context and therefore may not apply to other nations’ contexts.

By investigating the challenges that the developers, consultants, and client organisations need to address, this study assists managers in developing plans to facilitate coordination among these organisations for successful blockchain implementation.

To the authors’ knowledge this study is the first to identify and prioritise the challenges from the perspectives of multiple stakeholder groups with experience in blockchain technology implementation.

In the past few years, Haar wavelet-based numerical methods have been applied successfully to solve linear and nonlinear partial differential equations. This study aims to propose a wavelet collocation method based on Haar wavelets to identify a parameter in parabolic partial differential equations (PDEs). As Haar wavelet is defined in a very simple way, implementation of the Haar wavelet method becomes easier than the other numerical methods such as finite element method and spectral method. The computational time taken by this method is very less because Haar matrices and Haar integral matrices are stored once and used for each iteration. In the case of Haar wavelet method, Dirichlet boundary conditions are incorporated automatically. Apart from this property, Haar wavelets are compactly supported orthonormal functions. These properties lead to a huge reduction in the computational cost of the method.

The aim of this paper is to reconstruct the source control parameter arises in quasilinear parabolic partial differential equation using Haar wavelet-based numerical method. Haar wavelets possess various properties, for example, compact support, orthonormality and closed form expression. The main difficulty with the Haar wavelet is its discontinuity. Therefore, this paper cannot directly use the Haar wavelet to solve partial differential equations. To handle this difficulty, this paper represents the highest-order derivative in terms of Haar wavelet series and using successive integration this study obtains the required term appearing in the problem. Taylor series expansion is used to obtain the second-order partial derivatives at collocation points.

An efficient and accurate numerical method based on Haar wavelet has been proposed for parameter identification in quasilinear parabolic partial differential equations. Numerical results are obtained from the proposed method and compared with the existing results obtained from various finite difference methods including Saulyev method. It is shown that the proposed method is superior than the conventional finite difference methods including Saulyev method in terms of accuracy and CPU time. Convergence analysis is presented to show the accuracy of the proposed method. An efficient algorithm is proposed to find the wavelet coefficients at target time.

The outcome of the paper would have a valuable role in the scientific community for several reasons. In the current scenario, the parabolic inverse problem has emerged as very important problem because of its application in many diverse fields such as tomography, chemical diffusion, thermoelectricity and control theory. In this paper, higher-order derivative is represented in terms of Haar wavelet series. In other words, we represent the solution in multiscale framework. This would enable us to understand the solution at various resolution levels. In the case of Haar wavelet, this paper can achieve a very good accuracy at very less resolution levels, which ultimately leads to huge reduction in the computational cost.

The purpose of this study is to explore how big data analytics (BDA) as a potential information technology (IT) innovation can facilitate the retail logistics supply chain (SC) from the perspective of outbound logistics operations in the United Kingdom. The authors' goal was to better understand how BDA can be integrated to streamline SCs and logistical networks by using the technology, organisational and environmental model.

The authors applied existing theoretical foundations for theory building based on semi-structured interviews with 15 SC and logistics managers.

The perceived benefits of using BDA in outbound retail logistics comprised the strongest predictor amongst technological, organisational and environmental issues, followed by top management support (TMS). A framework was proposed for the adoption of BDA in retail logistics. Contextual concepts from previous literature have helped us understand how environmental changes impact BDA decision-making, as such: (i) SC maturity levels and connectivity affect BDA utilisation, (ii) connected SCs improve data accessibility and information exchange, (iii) the benefits of BDAs also affect adoption and (iv) outsourcing complex tasks to experts allows companies to focus on core businesses instead of investing in IT infrastructure.

Outside the key findings listed, this study shows that there is no one-size-fits-it-all approach for use within all organisational settings. The proposed framework reveals that the perceived benefit of BDA is non-transferrable and requires top-level management support for successful implementation.

The existing literature focusses on the approaches to applying BDA in SC and logistics but fails to present a deep dive into retail outbound logistics activity. This study addresses the “how” and proposes a social-inclusive framework for a technology-enabled topic.

This paper aims to analyze and give directions for advancing research in stock market volatility highlighting its features, structural breaks and emerging developments. This study offers a platform to research the benchmark studies to know the research gap and give directions for extending future research.

The author has performed the literature review, and, reference checking as per the snowballing approach. Firstly, the author has started with outlining and simplifying the significance of the subject area, the review illustrating the various elements along with the research gaps and emphasizing the finding.

This work summarizes the studies covering the volatility, its properties and structural breaks on various aspects such as techniques applied, subareas and the markets. From the review’s analysis, no study has clarified the supremacy of any model because of the different market conditions, nature of data and methodological aspects. The outcome of this research work has delivered further magnitude to research the benchmark studies for the upcoming work on stock market volatility. This paper has also proposed the hybrid volatility models combining artificial intelligence with econometric techniques to detect noise, sudden changes and chaotic information easily.

The author has taken the research papers from the scholarly journal published in the English language only and the author may also consider other nonscholarly or other language journals.

To the best of the author’s knowledge, this research work highlights an updated and more comprehensive framework examining the properties and demonstrating the contemporary developments in the field of stock market volatility.