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Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

This research uses contingency theory and Venkatraman’s concept of moderating fit to explore how key project stakeholders (clients, consultants and suppliers) influence project performance from the perspective of small and medium contractors in the Jordanian construction industry.

An anonymous structured survey was performed comprising 200 key informants including senior project managers, construction managers, engineers and general managers working for small- and medium-sized contractors in the Jordanian construction industry. The Partial Least Squares Structural Equation Modelling (PLS-SEM) was used to analyse the data.

The results of this study show that consultant-related factors (quality of documentation produced, ability to communicate and technical competencies) are perceived to have the most significant direct effect on project performance, followed by client-related factors (payment promptness, decision certainty and documentation control) and supplier-related factors (supplier performance, defects control and logistics management).

The results contribute new theoretical, empirical and practical insights to existing construction project performance research by highlighting the key performance factors which need to be managed for each stakeholder group to ensure effective project performance from a small- and medium-sized contractor perspective.

We develop a conceptual framework as a basis for thinking about the impact of extractive industries and emancipatory potential of alternative accounts. We then review selected alternative accounts literature on some contemporary issues surrounding the extractive industries and identify opportunities for accounting, auditing, and accountability research. We also provide an overview of the other contributions in this special issue.

Drawing on alternative accounts from the popular and social media as well as the alternative accounting literature, this primarily discursive paper provides a contemporary literature review of identified issues within the extractive industries highlighting potential areas for future research. The eight papers that make up the special issue are located within a conceptual framework is employed to illustrate each paper’s contribution to the field.

While accounting has a rich literature covering some of the issues detailed in this paper, this has not necessarily translated to the extractive industries. Few studies in accounting have got “down and dirty” so to speak and engaged directly with those impacted by companies operating in the extractive industries. Those that have, have focused on specific areas such as the Niger Delta. Although prior studies in the social governance literature have tended to focus on disclosure issues, it is questionable whether this work, while informative, has resulted in any meaningful environmental, social or governance (ESG) changes on the part of the extractive industries.

The extensive extractive industries literature both from within and outside the accounting discipline makes a comprehensive review impractical. Drawing on both the accounting literature and other disciplines, this paper identifies areas that warrant further investigation through alternative accounts.

This paper and other contributions to this special issue provide a basis and an agenda for accounting scholars seeking to undertake interdisciplinary research into the extractive industries.

The purpose of this study is to depict the value added by digital transformation to intellectual capital (IC) by virtue of the studies reached by the literature review on different databases are examined.

Journal of Intellectual Capital, which has the highest number of records from the resources included in the “Web of Science” content and covering the title of “intellectual capital” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “digital transformation” and “intellectual capital” should be searched for simultaneously in each and every article published in the journal between the years 1975 and 2022.

A bibliometric analysis and citation mapping process are carried out considering all dimensions to reach the results and interpretation of findings. VOSviewer is used to visualize the bibliometric networks of results and findings in the form of scientific mapping, as well as to visualize the co-authorship analysis of keywords, co-authorship analysis and citation networks.

Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. This circumstance is elaborately described as a limitation of this study. Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. One limitation is that bibliometric analysis is based on quantitative metrics, such as citation counts, which do not take into account the quality of the research. Therefore, bibliometric analysis alone may not provide a complete picture of the validity of a single journal. In addition, bibliometric analysis is based on the number of times a paper is cited, which can be influenced by factors such as the prestige of the journal, the field of research and the time since the publication. In conclusion, bibliometric analysis can be used to evaluate the performance of a single journal, but it is important to consider its limitations.

This study identified contributions, gaps and limits based on the results of a bibliometric analysis. Italy is the most influential country and the issue is structured around four clusters: IC; digital transformation; human capital; and knowledge management. As previously unexplored issues are addressed in an innovative manner, it is acceptable to underline the paper’s originality.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

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.

This study aims to conduct a bibliometric analysis on knowledge management from journals in the Scopus database between 1988 and 2021. The paper covered the past three decades of publications and carried out performance analysis and science mapping analysis of articles.

The study uses bibliometrics, performance analysis and science mapping analysis of 1,016 articles extracted from the Scopus database. The study examined the scientific productivity of articles, productive authors, citable documents, most relevant institutions, cited countries, co-occurrence of keywords, thematic mapping, co-citations and collaboration of authors and countries. The study used Biblioshiny as a tool to carry out the performance analysis and science mapping analysis.

The results show that the number of publications has significantly increased in the past decade, 88.4% of authors contribute at least a single article, 8.3% of authors published two articles, 2% of the authors published three documents and 0.6% of the authors contribute four papers. The USA, China and Australia were the most productive countries in terms of the total number of citations and foreign collaborations. Journal of Knowledge Management, Knowledge Management Research and Practice, VINE Journal of Information and Knowledge Management and International Journal of Technology Management are the top outlets in the knowledge management literature.

Over the past decade, the research on knowledge management construct has exploded because of the growing interest of researchers and practitioners in the field. Despite being a well-developed field, few studies have applied bibliometric analysis in the knowledge management literature. The study is more comprehensive in terms of the actors and methods involved in analyzing the scientific production of articles in the area of knowledge management.

This study aims to investigate the research productivity in terms of publications count of the top four premiers Indian Institute of Management (IIM) institutions and to explore the current research trends.

Bibliometric techniques were employed to assess the performance in terms of research productivity of authors affiliated with IIMs. The Elsevier Scopus database was selected as a tool to extract the prospective publications data limiting the time frame for 2010–2021. The IIM-Ahmedabad, IIM-Bangalore, IIM-Calcutta and IIM-Lucknow have been selected for the study. The harvested data were analyzed by using the standard bibliometric indicators and scientometric parameters to measure the research landscape such as average growth rate, compound average growth rate, relative growth rate, doubling time, degree of collaboration, collaborative index, collaborative coefficient and modified collaborative coefficient. VOSviewer 1.6.17, BibExcel and Microsoft Excel were used for data analysis and visualization.

The research productivity of selected four IIMs has shown an upward trend during the study period from 2010–2021 and accrued 4,397 publications with an average of 366 publications per year. The authorship patterns demonstrate the collaborative trends as most of the publications were produced by the multiple-authors (81.03%). IIM-Ahmedabad has produced the maximum number of publications (32.20%). The research productivity of IIMs has come out in collaboration with the 125 nations across the world and the USA, the UK, Canada, Germany and China are the front runners with IIMs in the collaborative network. The high magnitude and density of collaboration are evident from the calculated mean values of the degree of collaboration (0.82). The mean values of the collaborative index (2.64), collaborative coefficient (0.51) and modified collaborative coefficient (0.51) demonstrated a positive trend, but indicate the fluctuation in the collaborative pattern as time proceeds.

The study is limited to the publications data indexed in the Scopus database, therefore the outcome may not be generalized across other databases available in the public domain like Web of Science (WoS), PubMed, Dimensions and Google Scholars.

The findings of the study may aid academics and library professionals in identifying research trends, collaboration networks and evaluating other academic and research institutions by using the current advancement in data analysis.

The present study is the first effort to evaluate the research productivity of IIMs. The expanding literature will make an important contribution to identifying patterns and evaluating current research trends on a worldwide scale.

This study aims to investigate whether social investment (SI) policies improve employment among single mothers.

This paper analyzes the potential effects of SI policies on vulnerable individuals and workers at the macro level by using the employment position of single mothers as a dependent variable. Time-series cross-national data from 18 OECD countries between 1998 and 2017 are analyzed. Multilevel model analysis is also used for robustness check.

I find that public spending on education and family support is positively associated with the employment rates of single mothers. In contrast, active labor market policy (ALMP) spending is negatively associated. ALMP’s negative effects stand out particularly with public spending on job training. Of all family support policies, family allowances are positively associated with single mothers’ employment, which runs counter to the conventional argument that family allowances are a disincentive for women’s or mothers’ employment. Paid leave (length and generosity) is also associated with higher employment for single mothers. There is also some tentative evidence that public spending on maternity leave benefits (spending level) may raise the odds of single mothers being employed, when individual-level factors are controlled for in multilevel analysis we implement for robustness check.

This paper does not analyze the effects of the qualitative properties of SI policies. Future research is necessary in this respect.

The effects of SI policies on employment among single mothers have not yet been examined in the literature. This paper seeks to be a first cut at measuring the effects.