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This paper aims to examine the relationship between board structure and risk-taking, exploring how this association is influenced by advanced technologies in the banking sector.

This study uses a panel sample of 22 Pakistani banks from 2011 to 2018. To test the authors’ hypothesis, the authors use regression analysis with two-way cluster robust standard errors. Further, the authors also check the robustness of the authors’ findings using alternate proxies of board structure and bank risk-taking behavior. To address endogeneity concerns, the authors use the two-stage least square technique.

In the era of the Fourth Industrial Revolution, Pakistani banks’ digitalization is modeled by the presence of Temenos-T24/Oracle as their core banking system (software providing end-to-end operational integration). Its interactional effect with corporate governance is evaluated to implicate informed risk-taking by the board as a result of improved information access and analysis. The authors find that board size has a positive association with risk-taking, and the use of modern technology reshapes this association in the banking sector.

The contribution of this paper is twofold. First, the impact of board structure on bank risk-taking has not been extensively researched in Pakistan – a highly volatile and unpredictable economy. Second, the evaluation of the role of technology on bank risk is being researched for the very first time – a uniqueness of this paper.

This study aims to examine whether information asymmetry (IA) mediates the relationship between women directors and the cost of equity (COE). Specifically, this study posits that women directors tend to lower the COE through the channel of IA.

This study uses the US-listed firms’ data from 2002 to 2014, comprising 11,189 firm-year observations. This study measures the COE by aggregating the four unique market-based COE models and apply pooled ordinary least square to estimate our results.

This study documents that women directors are linked to IA, and that IA is linked to the COE. Furthermore, in the mediation test, IA fully mediates the relationship between women directors and the COE. This study's results also validate the critical mass hypothesis, as the IA shows full mediation between the critical mass of women directors and COE. This study also discusses the limitations and major implications of the results along with possible future directions.

This study also supports the positive role of females in improvising the economic performance of the firms and supporting the sustainable development goals-5 (gender equality).

The originality of this study lies in its theoretical as well as empirical contributions. First, this study follows the line of inquiry of the mediation analysis, thereby contributing by examining whether the relationship between women directors and financial value, i.e. COE, is indirect. Second, in addition to ex post measures of the COE, this study used four ex ante unique market-based models to measure the COE. Most of the prior studies just rely on book-based measures or use a single market-based mode. Third, the findings contribute insights into how women directors add value and benefits firms.

Migrants have been posed with less familiarity in their new environment during COVID times, faced with various social, psychological and emotional traumas. Research indicates that they may have been originating from apprehension of being abandoned by their neighbours. Most of them were precarious with little wages, competing for their basic need fulfilment, putting them under more mental stress. Looking forward to the ancient principle, ‘Athithi Devo Bhavah’ (Visitors are Gods), and immediate response, measures were taken to promote community shelters and kitchens, maintaining social distancing and ensuring physical safety. The governments of different countries took proactive initiatives to study and assess their needs. The study here includes the cases being evaluated regarding migrants' need for surveillance and psycho-social support given by the government and non-profit associations during the pandemic at the global level. Remarkably, certain countries proved the working of their sustainable poverty reduction model by evaluating various factors. The study started by introducing who migrants were, diagnosing their problems faced during the pandemic, how Sustainable Development Goals can be implemented and various measures taken at government and institutional levels to protect the migrant workforce.

The present study is centric on the organic food sector and explores the role of social media in pushing local farmers onto the market shelves. The study is intended to analyse the ground reality of the producers and the innate challenges of the traditional market structure. The study aims to explore the impact of social media in establishing small-scale producers in the organic food sector.

The study is pragmatic in approach and with the help of a semi-structured personal investigation it delves deeper and uses an interpretative phenomenological approach to analyse the anecdotes shared by the subjects.

The study finds that access to social media windows leads to a corresponding jump in the sales and brand image of the producers.

The study is novice and original in approach confirming the impact of social media on local farmers and producers. It showcases the power of social media in leveraging the state of the economy of the marginalised sections.

This study aims to introduce an innovative approach to predictive maintenance by integrating time-series sensor data with event logs, leveraging the synergistic potential of deep learning models. The primary goal is to enhance the accuracy of equipment failure predictions, thereby minimizing operational downtime.

The methodology uses a dual-model architecture, combining the patch time series transformer (PatchTST) model for analyzing time-series sensor data and bidirectional encoder representations from transformers for processing textual event log data. Two distinct fusion strategies, namely, early and late fusion, are explored to integrate these data sources effectively. The early fusion approach merges data at the initial stages of processing, while late fusion combines model outputs toward the end. This research conducts thorough experiments using real-world data from wind turbines to validate the approach.

The results demonstrate a significant improvement in fault prediction accuracy, with early fusion strategies outperforming traditional methods by 2.6% to 16.9%. Late fusion strategies, while more stable, underscore the benefit of integrating diverse data types for predictive maintenance. The study provides empirical evidence of the superiority of the fusion-based methodology over singular data source approaches.

This research is distinguished by its novel fusion-based approach to predictive maintenance, marking a departure from conventional single-source data analysis methods. By incorporating both time-series sensor data and textual event logs, the study unveils a comprehensive and effective strategy for fault prediction, paving the way for future advancements in the field.

This study aims to conduct the domain mapping of consumer behaviour research in the context of solar energy. The study can help in understanding the intellectual structure, evolution of keywords and key research producers (at the author, institutional and source level) related to the domain of solar energy consumer research.

This study uses R-studios’ bibliometrix package for analysing the bibliographical data collected from the Scopus database. Analysis has been conducted at the descriptive level (summary, author, institution and source) and analytical level (co-citation analysis, co-occurrence analysis, thematic maps and historiography).

This study finds out the most relevant authors, institutions and sources using criteria such as production, citations and H-index. Relevant research clusters have been identified using the clustering of authors, co-citations and keywords. Thematic mapping has identified the basic and motor themes. Historical citation analysis shows the direct linkage of previous studies. Overall, this study reports the most relevant bibliometric indicators in the domain of solar energy consumer research.

Identified patterns can help policymakers, business experts, social marketers and energy conservation organisations to study consumer behaviour.

Thiis bibliometric study can effectively assess sustainable development goals and suggest improved action plans.

This study examined bibliometric analysis in solar energy products (SEPs), recognised varied domains of research work on consumers’ intention to purchase solar household products and mapped them into six groups. This study provides an overview of 40 years of research on consumer behaviour towards SEPs and discusses its findings to identify the research gap.

This study delves into the challenges obstructing the integration of blockchain-enabled smart contracts (BESC) in the construction industry. Its primary objective is to identify these barriers and propose a roadmap to streamline BESC adoption, thereby promoting sustainability and resilience in building engineering.

Employing a unique approach, this study combines the Technology-Organization-Environment-Social (TOE + S) framework with the IF-Delphi-HF-DEMATEL-IFISM methodology. Data is collected through surveys and expert interviews, enabling a comprehensive analysis of BESC implementation barriers.

The analysis reveals significant hindrances in the construction industry’s adoption of BESC. Key obstacles include economic and market conditions, insufficient awareness and education about blockchain technology among stakeholders, and limited digital technology integration in specific cultural and societal contexts. These findings shed light on the complexities faced by the industry in embracing blockchain solutions.

The research makes a significant contribution by combining the TOE + S framework with the IF-Delphi-HF-DEMATEL-IFISM methodology, resulting in a comprehensive roadmap to address barriers in implementing BESC in Sustainable Construction Projects. Noteworthy for its practicality, this roadmap provides valuable guidance for construction stakeholders. Its impact extends beyond the industry, influencing both academic discourse and practical applications.

This review paper delves into the comprehensive understanding of Ashwagandha, spanning its botanic occurrence, conventional applications, extraction techniques and pivotal role in addressing various disorders.

Introduction Ashwagandha, also known as Withania somnifera, is a remarkable botanical resource with a rich history of use in traditional medicine.

In botany, Withania somnifera thrives in diverse ecosystems, particularly in tropical and subtropical regions. Its extensive distribution across regions, the Canary Islands, South Africa, the Middle East, Sri Lanka, India and China underscores its adaptability and resilience. The traditional uses of Ashwagandha in Ayurvedic and indigenous medicine systems have persisted for over 3,000 years. With over 6,000 plant species utilized historically, India, often regarded as the “botanical garden of the world,” has firmly established Ashwagandha as a cornerstone in traditional healing practices.

Extraction methods play a pivotal role in harnessing the therapeutic potential of Ashwagandha. Ultrasonic-assisted extraction and high-performance liquid chromatography are among the techniques employed to obtain the key bioactive compounds. Ashwagandha’s significance in modern medicine is underscored by its potential to address a spectrum of health issues. The multifaceted bioactivity of Ashwagandha is attributed to its antioxidant, anti-inflammatory, heart conditions, metabolic disorders, renal ailments, hepatic diseases and adaptogenic properties, making it a subject of increasing interest in contemporary medical research. This review synthesizes the assorted perspectives of Ashwagandha, from its botanical roots and conventional employments to its advanced extraction strategies and its intention to basic well-being challenges, advertising important bits of knowledge for analysts, specialists and healthcare experts alike.