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Artificial Intelligence (AI) is an emerging technology and turned into a field of knowledge that has been consistently displacing technologies for a change in human life. It is applied in all spheres of life as reflected in the review of the literature section here. As applicable in the field of libraries too, this study scientifically mapped the papers on AAIL and analyze its growth, collaboration network, trending topics, or research hot spots to highlight the challenges and opportunities in adopting AI-based advancements in library systems and processes.

The study was developed with a bibliometric approach, considering a decade, 2012 to 2021 for data extraction from a premier database, Scopus. The steps followed are (1) identification, selection of keywords, and forming the search strategy with the approval of a panel of computer scientists and librarians and (2) design and development of a perfect algorithm to verify these selected keywords in title-abstract-keywords of Scopus (3) Performing data processing in some state-of-the-art bibliometric visualization tools, Biblioshiny R and VOSviewer (4) discussing the findings for practical implications of the study and limitations.

As evident from several papers, not much research has been conducted on AI applications in libraries in comparison to topics like AI applications in cancer, health, medicine, education, and agriculture. As per the Price law, the growth pattern is exponential. The total number of papers relevant to the subject is 1462 (single and multi-authored) contributed by 5400 authors with 0.271 documents per author and around 4 authors per document. Papers occurred mostly in open-access journals. The productive journal is the Journal of Chemical Information and Modelling (NP = 63) while the highly consistent and impactful is the Journal of Machine Learning Research (z-index=63.58 and CPP = 56.17). In the case of authors, J Chen (z-index=28.86 and CPP = 43.75) is the most consistent and impactful author. At the country level, the USA has recorded the highest number of papers positioned at the center of the co-authorship network but at the institutional level, China takes the 1st position. The trending topics of research are machine learning, large dataset, deep learning, high-level languages, etc. The present information system has a high potential to improve if integrated with AI technologies.

The number of scientific papers has increased over time. The evolution of themes like machine learning implicates AI as a broad field of knowledge that converges with other disciplines. The themes like large datasets imply that AI may be applied to analyze and interpret these data and support decision-making in public sector enterprises. Theme named high-level language emerged as a research hotspot which indicated that extensive research has been going on in this area to improve computer systems for facilitating the processing of data with high momentum. These implications are of high strategic worth for policymakers, library stakeholders, researchers and the government as a whole for decision-making.

The analysis of collaboration, prolific authors/journals using consistency factor and CPP, testing the relationship between consistency (z-index) and impact (h-index), using state-of-the-art network visualization and cluster analysis techniques make this study novel and differentiates it from the traditional bibliometric analysis. To the best of the author's knowledge, this work is the first attempt to comprehend the research streams and provide a holistic view of research on the application of AI in libraries. The insights obtained from this analysis are instrumental for both academics and practitioners.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

This study seeks to build a financial uncertainty index for Saudi Arabia. This index serves as a leading indicator of Saudi economic activity and helps to describe economic fluctuations and forecast economic trends.

This study adopts an extension of the Jurado et al. (2015) procedure by combining financial uncertainty factors with their net spillover effects on GDP and inflation to construct an aggregate financial uncertainty index. The authors consider 13 monthly financial variables for Saudi Arabia from January 2010 to June 2021.

The empirical results show that the constructed financial uncertainty estimates are good leading indicators of economic activity. The robustness analysis suggests that the authors’ proposed financial uncertainty estimators outperform the alternative estimates used by other existing approaches to estimate the financial conditions index.

To the best of the authors’ knowledge, this is the first attempt at constructing a financial uncertainty index for Saudi Arabia. This study extends the empirical literature, from which the authors propose a novel conceptual framework for building a financial uncertainty index by combining the approach of Jurado et al. (2015) and the time-varying connectedness network approach proposed by Antonakakis et al. (2020)

Infrastructures become critical with the emerging threats triggering through disasters. Sri Lanka is a country with a higher risk of disaster impacts, in which the eye-opening has widened towards mitigating the damages towards critical infrastructures. Based on this, the purpose of this paper is to develop an index that identifies the significance of critical infrastructure resilience.

From the initial literature survey, disaster resilience is defined as capacity of three stages, absorptive, adaptive and restorative along with ten indicators to measure capacities. Selected indicators were then checked for suitability for scope of the research based on opinions of seven experts. Subsequently, the critical infrastructure resilience index (CIRI) was introduced such that the numerical values for each indicator are aggregated using the Z score method. Statistical relations between the actual impact against disasters and CIRI calculated for administrative regions in Sri Lanka were used as the final step to validate the developed index.

Resilience index development is presented in this paper with a comprehensive methodology of developing and validation. Further, the case study results imply the weakness and strengths in each resilience capacities, which are important in decision-making.

Unavailability of disaster impact data and centralized data repository were main constrains in the validation process of this research. Hence proxy data was used to validate resilience index in this research.

This research identified and validated a novel approach of defining disaster resilience index for regional decision-making.

This paper aims to explore the ambiguity and limitations of measuring firm-level multinationality (FLM) using theoretical and empirical comparisons of existing methods. The paper puts forward a list of five key aspects that collectively serve as a tool for researchers to select the most appropriate method for future research and as a basis for the future development of methods.

Firstly, the author reviews existing methods of measuring FLM and consolidates findings into five key aspects. Secondly, the author uses the aspects to compare existing methods theoretically, and subsequently, the author groups them into three distinct streams. Thirdly, the author compares existing methods across a sample of the 35 largest European MNEs by sales in 2020 to identify and demonstrate the ambiguity and limitations of these methods.

The author identifies the five key aspects of measuring FLM: framework, aggregation, segmentation, metrics and indicators. Using empirical comparison, the author empirically confirms the limitations highlighted in the literature and shows the differences and inconsistencies among methods, which cause confusion rather than clarity in the extant literature. Additionally, the author emphasises that three distinct streams further drive the debate on the regional/global nature and present further limitations of methods not mentioned in the literature to date.

This paper provides the most comprehensive review of the existing literature on FLM, resulting in five novel aspects of measuring FLM. The analysis of a sample of 35 European firms demonstrates and identifies the ambiguity and limitations of FLM-measuring methods.

The paper empirically investigates the link between banking market structure and funding liquidity risk.

With a panel of Vietnamese commercial banks from 2007 to 2021, the system generalized method of moments (GMM) estimator is applied as the primary regression method, while the random-effect model and the corrected least square dummy variable (LSDVC) technique are also considered in robustness checks.

Competition may increase banks' funding liquidity risk. This finding holds for competition measures derived from the Boone index and concentration ratios but not in the case of the Lerner index as a proxy for market power. Further results indicate that the funding liquidity risk of banks that are larger and have better performance (less credit risk and higher return) tends to be less affected by competition. Besides, the overall impact of bank competition on funding liquidity risk is amplified by the financial crisis and the COVID-19 pandemic.

The study extends the empirical literature by exploring the relationship between bank competition and funding liquidity risk. Additionally, the paper also studies how the impact of bank competition on funding liquidity risk depends on the characteristics of the banking sector and the macroeconomic conditions of the economy, including the moderating effect of the COVID-19 pandemic.

Despite a reduction in poverty the global population in 2015, the incidence of poverty remains very high in Sub-Saharan African countries. Most of the countries in the region are agrarian, with most of their population residing in rural areas, and a majority of the poor in the region are found in Nigeria. This study examined the nexus between participation in nonfarm enterprises (NFEs) and poverty among rural farm households in Nigeria and across the six geopolitical zones.

The Nigerian Living Standard Survey (NLSS) conducted in 2018–2019 by the National Bureau of Statistics was used. We made use of 13,440 farm households with useful information for the purpose of this study. The sample comprises 6,885 households that participated in NFEs and 6,555 nonparticipating households. The data were analyzed with Foster, Greer, and Thorbecke (FGT) (1984) metrics, probit, and fractional probit models at p = 0.05.

The incidence of poverty was lower among the participating households than in the nonparticipating households. Participation in NFEs had a mitigating effect on poverty. We also established that zonal differentials in poverty rates exist among households in all the analyses. Participation in NFEs was influenced by individual, household, and institutional factors and was also able to explain the depth of poverty among the respondents.

It is suggested that poverty alleviation policies should be targeted at improving access to nonfarm economic activities by rural farm households residing in vulnerable geopolitical zones.

This study is the first attempt to profile household poverty based on the type of NFEs they are involved in. The study also provides an insight into the effect of the state of residence on zonal poverty models, which is expedient if the country must achieve Sustainable Development Goal 1 on the eradication of poverty everywhere.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2023-0493

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

The main purpose of the study is to investigate the impact of gender diversity both at the board and workforce level on firm performance (FP) in the Indian context.

This study is based on annual data of 200 companies listed on Bombay Stock Exchange (BSE) for the period 2012–2019. The authors have used the fixed-effects (FE) regression and system generalized method of moments to estimate the impact of board gender diversity and workforce gender diversity (WGD) on FP. The authors have used Blau's Index (BI) and Shannon's Index (SI) to measure gender diversity. Further, the authors have used return on assets and Tobin's Q (TBQ) to measure FP.

The authors' panel regression results suggest that board gender diversity and WGD have a positive and statistically significant impact on FP. The authors' findings are robust across different methods of estimation and alternative measures of FP.

This paper examines the impact of gender diversity both at the board and workforce level on FP of 200 companies listed on BSE. The authors' study contributes to the literature that is sparse in the Indian context and provides new insights on the impact of board and WGD on FP. The findings have useful policy implications. To achieve better performance, it is imperative to appreciate gender diversity at the governance and workforce level in a fast-growing economy like India.

The purpose of this study is to construct a new index to assess the impact of an energy price shock on macroeconomic indicators of India. This paper also shows a comparative analysis of the constructed index along with pre-existing World Bank and International Monetary Fund indices on energy.

This paper uses three vector autoregressions and compute the long-term impact of the indices on the considered macroeconomic variables through impulse response functions.

This paper finds that an energy price shock has a detrimental impact on the macroeconomic indicators of India in the long run. This study also finds that the constructed index acts as a relatively more sensitive index in comparison to the International Monetary Fund and World Bank indices, which is bespoke to a developing economy case. This sensitivity is ascribed to dynamic weighting for a different basket of energy components, which are more pertinent to an Indian context.

The novelty of this research lies in the construction of a new index and its comparison to the existing ones. This study justifies why a developing economy would require a different measure of energy as opposed to the existing indices.