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This paper evaluates the different digital libraries (DLs) in India developed in the past two decades. These DLs help advance scholarship and facilitate the reading habits of their users. Many of these DLs have a rich collection of vernacular literature depicting India’s diverse cultural heritages and traditions. DLs in India also help in outreaching global researchers and knowledge seekers. Many diaspora communities use these DLs frequently and other stakeholders such as the international scholars interested in Indic civilization. This paper finally suggests a way forward to make operational DL initiatives discoverable to humans and machines with the adaptation of FAIR principles that make e-resources findable, accessible, interoperable and reusable for their discovery beyond respective DL portals.

This study used a desk survey of DL initiatives in India. Their salient features are obtained from their respective Web portals and social media profiles.

This study identified twelve operational DL initiatives in India. Out of them, the newest five DL initiatives are described in this paper.

This study reflects original findings on the newest five DL initiatives of India. These findings were not earlier reported in a journal article.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

This study proposes a methodology based on data source triangulation to measure the “strategic fit” for the automotive supply chain.

At first, the authors measured the responsiveness of the Indian automobile supply chain, encompassing the top ten major automobile manufacturers, using both sentiment and conjoint analysis. Second, the authors used data envelopment analysis to identify the frontiers of their supply chain. The authors also measured the supply chain's efficiency, using the balance sheet. Further, the authors analyzed the “strategic fit” zone and discussed the results.

The results indicate that both the proposed methods yield similar outcomes in terms of strategic fitment.

The study outcomes facilitate measuring the strategic fit, thereby leveraging the resources available to align. The methodology proposed is both easy to use and practice. The methodology eases time and costs by eliminating hiring agencies to appraise the strategic fit. This valuable method to measure strategic fit can be considered feedback for strategic actions. This methodology could also be incorporated possibly as an operative measurement and control tool.

Data triangulation meaningfully enhances the accuracy and reliability of the analyses of strategic fit. Data triangulation leads to actionable insights relevant to top managers and strategic positioning of top managers within a supply chain.

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.

The research investigates the determinants of corporate social responsibility (CSR) spending and CSR disclosures by the Bangladeshi commercial banks. In the process, it explains the relationship between CSR disclosures and CSR expenditure by Bangladeshi commercial banks.

Legitimacy theory has been used to explain the motivation for such expenditure and disclosure. For purpose of analysing the determinants, ordinary least square (OLS) regression analysis has been used for the first test with CSR expenditures and ordered PROBIT regression analysis has been used for test with CSR disclosures.

The result found that CSR expenditure depend on banks’ size, age and government ownership, whilst CSR disclosures depend on CSR expenditure, profitability, age, government ownership and Islamic compliance.

The practical contribution of this study includes the assistance for the public policy development by providing better understanding of extent and credibility of CSR reporting by the Bangladeshi banking sector.

The study contributes to the academic literature by presenting preliminary findings from different focus on a developing economy like Bangladesh. The study leads to draw a standard for the developing country to find out the differences compared to developed country.

Today, industrial revolutions demands advanced technologies, means, mediums, tactics and so forth for optimizing their operating behavior and opportunities. It is probed that the effectual results can be seized into system by not only developing advance means and technologies, but also capably adapting these developed technologies, their user interface and their utilization at optimum levels. Today, industrial resources need perfect synchronization and optimization for getting elevated results. Accordingly, present study is furnished with the purpose to expose quality-driven insights to march toward excellence by optimizing existing resources by the industrial organizations. The present study evaluates quality attributes of mechanical machineries for seizing performance opportunities and maintaining competitiveness via synchronizing and reconfiguring firm's resources under quality management system.

In the present study, Kano’s integrated approach is implemented for supporting decision rational concerning industrial assets. The integrative Kano–analytic hierarchy process (AHP) approach is used to reflect the relative importance of quality attributes. Kano and AHP tactics are integrated to define global relative weight and their computational medium is adapted along with ratio analysis, reference point theory and TOPSIS technique for understanding robust decision. The study described an interesting idea for underpinning quality attributes for benchmarking system substitutes. A machine tool selection case is discussed to disclose the significant aspect of decision-making and its virtual qualities.

The decision executives can realize massive benefits by streaming quality data, advanced information, technological advancements, optimum analysis and by identifying quality measures and disruptions for gaining performance deeds. The study determined quality measures for benchmarking machine tool substitute for industrial applications. Momentous machine alternatives are evaluated by means of technical structure, dominance theory and comparative analysis for supporting decision-making of industrial assets based on optimization and synchronization.

The study linked financial, managerial and production resources under sole platform to present a technical structure that may assist in improving the performance of the manufacturing firms. The study provides a decision support mechanism to assist in reviewing the momentous resources to imitate a higher level of productive strength toward the manufacturing firms. The study endeavors its importance toward optimizing resources, which is an evident requirement in industries as the same not only saves money, escalates production, improves profit margins and so forth, but also gratifies the consumption of scarce natural resources.

The study stressed that advance information can be sought from system characteristics in the form of quality measures and attributes, which can be molded for gaining elevated outcomes from existing system characteristics. The same demands decision supports tools and frameworks to utilize data-driven information for benchmarking operations and supply chain activities. The study portrayed an approach for ease of utilizing data-driven information by the decision-makers for demonstrating superior outcomes. The study originally conceptualized multi-attributes appraisement framework associated with subjective cum objective quality measures to evaluate the most significant machine tool choice amongst preferred alternatives.

The authors develop a methodology to select appropriate sustainable supply chain indicators (SSCIs) to measure Sustainable Development Goals (SDGs) in the global supply chain.

SSCIs are identified by reviewing the extant literature and topic modeling. Further, they are evaluated based on existing SDGs and ranked using the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method. Notably, the evaluation of indicators is a multi-criteria decision-making (MCDM) process within a fuzzy environment. The methodology has been explained using a case study from the automobile industry.

The case study identifies appropriate SSCIs and differentiates them among peer suppliers for gaining a competitive advantage. The results reveal that top-ranked sustainability indicators include the management of natural resources, energy, greenhouse gas (GHG) emissions and social investment.

The study outcome will enable suppliers, specialists and decision makers to understand the criteria that improve supply chain sustainability in the automobile industry. The analysis provides a comprehensive understanding of the competitive package of indicators for gaining strategic advantage. This proactive sustainability indicator selection promotes and enhances sustainability reporting while fulfilling regulatory requirements and increasing collaboration potential with trustworthy downstream partners. This study sets the stage for further research in SSCIs’ competitive strategy in the automobile industry along with its supply chains.

This study is unique as it provides a framework for determining relevant SSCIs, which can be distinguished from peer suppliers, while also matching economic, environmental and social metrics to achieve a competitive advantage.

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.

This study examines how board characteristics influence air, water and renewable energy (AWR) disclosures in an emerging economy. It argues for the necessity of separating these disclosures to address unique environmental impacts and stakeholder concerns.

Using longitudinal data from environmentally sensitive firms (2014–2022), a disclosure index based on the Global Reporting Initiative (GRI) framework was developed to quantify AWR separately. To address potential statistical issues such as endogeneity and selection bias, the analysis employed a set of robust regression models, including the industry fixed effects (FE) model, a lagged model and a two-stage least squares (2SLS) model.

Board size and audit committees positively influence all AWR disclosures, while foreign directors significantly impact air and renewable energy disclosures. Board meetings negatively affect water disclosures. Surprisingly, board independence shows no significant impact, and gender diversity has no notable relationship. Post-amendment, firms increased AWR disclosures, though participation remains limited.

Grounded in legitimacy theory, this study contributes to the literature by demonstrating how separating the unique characteristics of AWR disclosures offers stakeholders more precise insights into how firms manage specific environmental concerns. The findings are based on data from listed firms in Bangladesh and may not be generalisable to unlisted firms or other regions.

The study emphasises the importance of distinct AWR reporting, offering valuable insights for regulators and corporate boards to improve transparency and sustainability practices.

Separating AWR disclosures provides stakeholders with clearer assessments of firms' environmental performance, promoting accountability and informed decision-making.

This study uniquely emphasises the need for disaggregating air, water and renewable energy disclosures in emerging economies. By focussing on each environmental issue separately, the research highlights how distinct disclosures offer clearer insights into how firms address specific environmental challenges, such as air pollution, water management and the transition to renewable energy sources. This disaggregation is essential for stakeholders – particularly regulators, investors and policymakers – to assess and respond to firms' sustainability efforts accurately.

A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation.

Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool.

The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number.

Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.