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This study aims to provide insights into different aspects of the extant literature on the effects of dividend announcements. Along with other outputs of a bibliometric study, this study provides deeper insights into the concentration of the extant literature and suggest future research agendas.

This study uses the bibliometric, network and content analysis of the dividend announcement literature indexed in Scopus. This study presents the temporal analysis, the network of authors, countries, author citations and the co-occurrence of author keywords. This study provides the concentration of the extant literature in three clusters and unearth some key future research areas. This study uses the latent Dirichlet allocation method for robustness.

A total of 54 documents examining the US sample have received 1,804 citations. Interestingly, the first article on emerging markets was published in 2002, when at least 34 articles on developed markets had already been published from 1982 to 2001. The content analysis of top-cited literature unveils diverse insights into dividend announcements’ effects on financial markets. Contagion effects negatively impact non-announcing banks, particularly larger ones. Dividend maintenance affects stock market momentum, influencing loser returns. While current dividend/earnings news may not predict future company performance, information content dominates bond market reactions to post-dividend announcements. Concomitantly, while financially constrained firms exhibit short-term gains but worse long-term performance following dividend increases, larger stock dividends send stronger market signals in China.

This study significantly contributes to the bibliometric and content analysis literature by analyzing the sample documents based on the sample examined. To the best of the authors’ knowledge, no previous bibliometric study in this domain has been conducted to explore the markets (developed and emerging) to which the samples examined belong and the quality of publications from developed and emerging markets.

This bibliometric study is steered to compute the impact of literature published on cloud computing within the fields of information science and library science. The research has been conducted on concentrating the term “Cloud Computing” to search the literature published in both fields, i.e. information science and library science from the time span 2007 to August 2023. This study aims to investigate the top productive country, organizations and highly cited publications.

The period of the exploration was from 2007 to August 2023 for bibliometric analysis and data was collected from the ISI Web of Science. Total 401 documents were retrieved and analyzed to highlight the year-wise distribution of documents type, year-wise most cited articles, prominent journals of the subjects, productivity of organizations, impact of countries and cooccurrences of keywords. The results are grounded on the basis of documents types (articles, early access articles, proceeding papers, book review, editorial material, news items and reviews).

The findings reveal that the most productive year of publication on cloud computing services was 2013. The top productive source is “International Journal of Information Management.” The articles entitled “Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors” found as the most cited article and the significant increase in citations is also noteworthy. The most productive organizations on the topic include “Islamic Azad University of Iran,” “University Cologne of Germany” and “University Nova Lisboa of Portugal.” The results confirmed that the USA dominates in the production of research on “Cloud Computing Services” and the most repeated keyword in the literature is cloud computing. The research articles are the most cited sources of research.

This bibliometric research is an original piece of work that has been conducted to measure the research production in the field of information science and library science during 2007−2023. This piece of work is valuable for those who want to study the literature on cloud computing in the area of information science and library science.

People with disabilities (PwDs) encounter several impediments to employment prospects, contributing to India's huge employment gap. Entrepreneurship offers a different avenue for PwDs to overcome these constraints. Entrepreneurship Support Organizations (ESOs) play a significant role in assisting PwD entrepreneurs. The present study aims to explore challenges encountered by entrepreneurs with disabilities (EwDs) in starting or sustaining a business in an Indian ecosystem. The study further aims to examine the status of entrepreneurial outcomes while comprehending the intersection of the challenges of disability and entrepreneurship. The study draws on stakeholder and social identity theories to understand this critical connection.

A qualitative approach is applied to study the phenomenon. Semi-structured interviews were conducted with 13 EWDs and 5 ESOs to explore the challenges faced and the available support in the Indian context. The study explores the level of coordination and roles these organizations play in fostering an entrepreneurial culture for PWDs.

The research findings explore novel challenges and facilitators of entrepreneurial pursuits of EwDs. The study further offers implications for policymakers to foster inclusive infrastructure, financial institutions to provide accessible credit options, and ESOs to amplify tailored assistance. Importantly, these dynamics have implications for advancing relevant Sustainable Development Goals.

The intersectional lens of disability and entrepreneurial challenges offered to study PwD entrepreneurship highlights new directions for exploring the phenomenon in deeper detail. Further, the theoretical underpinnings of stakeholder and social identity theories provide practical and indispensable insights into the phenomenon.

The pandemic brought by COVID-19 in March 2020 shook the entire world, compelling everyone to remain indoors. Most B-Schools were unprepared for such a situation and did not have the resources to carry out the teaching and learning activities. B-schools then adopted online and hybrid modes of learning to impart education to their students. The purpose of this study is to identify factors affecting the quality of education along with lessons learnt and lessons to be left behind, using qualitative method.

The factors affecting the quality of education were drawn from the literature, and 18 faculty members were interviewed. The study uses a descriptive method, where interviews were conducted, and each interview was recorded, with an explicit permission of respective faculty member and coded and categorized to identify themes.

The significant contribution of this study is that it highlights? Through the learnings and experiences of the pandemic? What will work in the future for business schools. The use of online teaching-learning sessions and softwares, namely, Turnitin and Grammarly will not fade away. Faculty will use various engagement tools such as quizzes and simulations to improve the learning and quality of education in the post-pandemic era. Various interactive and online tools emerged during the pandemic which allowed faculty to use diagrams and infographics in their teaching, and this helped the faculty to cater to students with different learning styles.

This study will provide B-Schools, faculties and leaders an input for improving the quality of online education. The present study provides an empirical contribution to the factors affecting online education and its quality, by highlighting the perspective of faculty members with the help of qualitative study. These factors make a clear and strong indication that education in the future will be partly online, wherein a lot of e-learning resources will be used by faculty to impart quality education.

Natural calamities like earthquakes, floods and epidemics/pandemics like COVID-19 significantly disrupt almost all the supply networks, ranging from medicines to numerous daily/emergency use items. Supply Chain Resilience is one such option to overcome the impact of the disruption, which is achieved by developing supply chain factors with Artificial Intelligence (AI) and Big Data Analytics (BDA).

This research examines how organizations using AI and BDA can bring resilience to supply chains. To achieve the objective, the authors developed the methodology to gather useful information from the literature studied and developed the Total Interpretive Structural Modeling (TISM) by consulting 44 supply chain professionals. The authors developed a quantitative questionnaire to collect 229 responses and further test the model. With the analysis, a conceptual and comprehensive framework is developed.

A major finding, this research advocates that supply chain resilience is contingent upon utilizing supply chain analytics. An empirical study provides further evidence that the utilization of supply chain analytics has a positive and favorable effect on the flexibility of demand forecasting to inventory management, resulting in increased efficiency.

Few studies demonstrate the impact of advanced technology in building resilient supply chains by enhancing their factors. To the best of the authors' knowledge, no earlier researcher has attempted to infuse AI and BDA into supply chain factors to make them resilient.

The coronavirus disease 2019 (COVID-19) pandemic adversely affected small and medium-sized enterprises (SMEs) in India. Amongst the challenges faced were the adjustments required in leadership skills to address pandemic-induced technological changes and the necessity for employee skill upgrading. This study examined the factors that influenced organisational performance in Indian SMEs, particularly in the context of the digital transformations that were brought about by the pandemic.

The study employed a cross-sectional design to investigate a set of hypotheses that were formulated to understand the relationships amongst digital leadership, digital training, empowerment and organisational performance. The data were collected during the pandemic from 487 employees who were working in various SMEs in India. Questionnaires were distributed through email and social media platforms, and electronic consent was obtained from each participant.

The study's findings indicated positive associations amongst digital leadership, digital training, empowerment and organisational performance. They also highlighted the mediating role of empowerment in these relationships. Furthermore, organisational resilience was found to positively moderate the relationship between empowerment and performance.

The study stands amongst the pioneers in exploring the role of digital leadership and digital training during the pandemic and their impact on employee empowerment amongst SMEs in a developing country.

This study examines the relationship between servant leadership and its dimensions on firm performance, with big data playing the role of a mediator.

Survey responses used for analysis in this study have been taken from business managers associated reputed private sector organizations in India. A conceptual model is proposed grounded to the Conservation of Resource Theory (COR). Structural equation modeling has been used to test the proposed model.

Servant leadership significantly relates to firm performance, whereby Big Data is seen to play the role of a mediator. The results also indicate that none of the dimensions of servant leadership independently affect firm performance.

The study adds to extant research by examining the mediating mechanism of Big Data in servant leadership and firm performance. It also suggests that each dimension of servant leadership gets reflected in overall servant leadership. Here it is important to note that Big Data analytics partially mediate the effectiveness of servant leadership.

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 study aims to examine the impact of the COVID-19 pandemic on knowledge-sharing drivers in small- and medium-sized family firms within the restaurant and fast-food industry. The pandemic has led to significant changes in business culture and consumer behaviour, accelerating digital transformation, disruptions in global supply chains and emerging new business opportunities. These changes have also influenced knowledge sharing (KS) and its underlying drivers.

To address the research objectives, a two-phase study was conducted. In the first phase, an exploratory analysis using the Delphi method was used to identify the essential drivers and factors of KS in family businesses (FBs). This phase aimed to establish a conceptual model for the study. In the second phase, confirmatory factor analysis was conducted to analyse the impact of the COVID-19 pandemic on the identified knowledge-sharing drivers. The study examined both the pre-pandemic and post-pandemic periods to capture the shifts in attitudes towards KS.

The findings indicate a significant shift in attitudes towards knowledge-sharing drivers. Before the pandemic, organisational drivers played a central role in KS. However, after the emergence of the pandemic, technological drivers became more prominent. This shift highlights the impact of the COVID-19 pandemic on KS within FB.

The research contributes to understanding knowledge-sharing in the context of FBs and sheds light on the specific effects of the COVID-19 pandemic on knowledge-sharing drivers. The insights gained from this study can inform strategies and practices aimed at enhancing KS in similar organisational settings.