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The purpose of this case study was to complement existing weeding and retention criteria beyond the most used methods in academic libraries and to consider citation counts in the identification of important scholarly works.

Using a small sample of items chosen for withdrawal from a small liberal arts college library, this case study looks at the use of Google Scholar citation counts as a metric for identification of notable monographs in the social sciences and mathematics.

Google Scholar citation counts are a quick indicator of classic, foundational or discursive monographs in a particular field and should be given more consideration in weeding and retention analysis decisions that impact scholarly collections. Higher citation counts can be an indicator of higher circulation counts.

The authors found little indication in the literature that Google Scholar citation counts are being used as a metric for identification of notable works or for retention of monographs in academic libraries.

This article explores whether six broad categories of activities undertaken by Canadian business scholars’ academics: publications record, citations record, teaching load, administrative load, consulting activities, and knowledge spillovers transfer, are complementary, substitute, or independent, as well as the conditions under which complementarities, substitution and independence among these activities are likely to occur.

A multivariate probit model is estimated to take into account that business scholars have to consider simultaneously whether or not to undertake many different academic activities. Metrics from Google Scholar of scholars from 35 Canadian business schools, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to explain the heterogeneities between the determinants of these activities.

Overall, the results reveal that there are complementarities between publications and citations, publications and knowledge spillovers transfer, citations and consulting, and between consulting and knowledge spillovers transfer. The results also suggest that there are substitution effects between publications and teaching, publications and administrative load, citations and teaching load, and teaching load and administrative load. Moreover, results show that public and private funding, business schools’ reputation, scholar’s relational resources, and business school size are among the most influential variables on the scholar’s portfolio of activities.

This study considers simultaneously the scholar’s whole portfolio of activities. Moreover, the determinants considered in this study to explain scholars’ engagement in different activities reconcile two conflicting perspectives: (1) the traditional self-managed approach of academics, and (2) the outcomes-focused approach of university management.

This study aims to examine the scholarly impact of funded and non-funded research published in ten core library and information science (LIS) journals published in 2016.

In total, ten high-impact LIS journals were selected using Google Scholar metrics. The source title of each selected journal was searched in the Scopus database to retrieve the articles published in 2016. The detailed information of all the retrieved articles for every journal was exported in a CSV Excel file, and after collecting all the journal articles’ information, all CSV Excel files were merged into a single MS Excel file for data analysis.

The study analyzed 1,064 publications and found that 14% of them were funded research articles. Funded articles received higher average citation counts (24.56) compared to non-funded articles (20.49). Funded open-access articles had a higher scholarly impact than funded closed-access articles. The research area with the most funded articles was “Bibliometrics,” which also received the highest number of citations (1,676) with an average citation count of 24.64. The National Natural Science Foundation of China funded the most papers (30), while the USA funded the highest number of research publications (36) in the field of LIS.

This study highlights the importance of securing funding, open access publishing, discipline-specific differences, diverse funding sources and aiming for higher citations. Researchers, practitioners and policymakers can use these findings to enhance research impact in LIS.

This study explores the impact of funding on research LIS and provides valuable insights into the intricate relationship between funding and research impact.

In 2000, Cañibano et al. published a literature review entitled “Accounting for Intangibles: A Literature Review”. This paper revisits the conclusions drawn in that paper. We also discuss the intervening developments in scholarly research, standard setting and practice over the past 20+ years to outline the future challenges for research into accounting for intangibles.

We conducted a literature review to identify past developments and link the findings to current accounting standard-setting developments to inform our view of the future.

Current intangibles accounting practices are conservative and unlikely to change. Accounting standard setters are more interested in how companies report and disclose the value of intangibles rather than changing how they are determined. Standard setters are also interested in accounting for new forms of digital assets and reporting economic, social, governance and sustainability issues and how these link to financial outcomes. The IFRS has released complementary sustainability accounting standards for disclosing value creation in response to the latter. Therefore, the topic of intangibles stretches beyond merely how intangibles create value but how they are also part of a firm’s overall risk and value creation profile.

There is much room academically, practically, and from a social perspective to influence the future of accounting for intangibles. Accounting standard setters and alternative standards, such as the Global Reporting Initiative (GRI) and European Union non-financial and sustainability reporting directives, are competing complementary initiatives.

Our results reveal a window of opportunity for accounting scholars to research and influence how intangibles and other non-financial and sustainability accounting will progress based on current developments.

The purpose of this paper is to synthesize insights from the emerging work in accounting on greenwashing and sustainability assurance and propose an agenda for future research in this area.

This article offers an original analysis of papers published on greenwashing and sustainability assurance research in the field of accounting. It adopts a systematic literature review and a narrative approach to analyse the dominant themes and key findings in this new and rapidly evolving field. From this overview, specific avenues for future research are identified.

In the past few years there has been a substantial spike in concern relating to greenwashing among academics, practitioners, regulators and society. This growing concern has only partly been reflected in the research literature. To date, research has primarily focused on: (1) the characteristics of firms adopting sustainability assurance, (2) the challenges facing sustainability auditors, (3) the development of appropriate assurance standards and regulations, and (4) capital market responses to greenwashing and sustainability auditing/assurance. Three key future research issues with respect to greenwashing are identified: (1) the future of standard-setter attempts to regulate greenwashing, (2) professional jockeying in sustainability reporting assurance, and (3) capital market opportunities and challenges relating to greenwashing and assurance.

Despite the profound economic and reputational impact of greenwashing and the rapid development of sustainability assurance services, research in accounting remains fragmented and emergent. This review identifies avenues offering considerable scope for inter-disciplinarity and bridging the divide between academia and practice.

Given the magnitude of the literature, a researcher must be selective of research papers and publications in general. In other words, only papers that meet strict standards of academic integrity and adhere to reliable and credible sources should be referenced. The purpose of this paper is to approach this issue from the prism of scientometrics according to the following research questions: Is it necessary to judge the quality of scientific production? How do we evaluate scientific production? What are the tools to be used in evaluation?

This paper presents a comparative study of scientometric evaluation practices and tools. A systematic literature review is conducted based on articles published in the field of scientometrics between 1951 and 2022. To analyze data, the authors performed three different aspects of analysis: usage analysis based on classification and comparison between the different scientific evaluation practices, type and level analysis based on classifying different scientometric indicators according to their types and application levels and similarity analysis based on studying the correlation between different quantitative metrics to identify similarity between them.

This comparative study leads to classify different scientific evaluation practices into externalist and internalist approaches. The authors categorized the different quantitative metrics according to their types (impact, production and composite indicators), their levels of application (micro, meso and macro) and their use (internalist and externalist). Moreover, the similarity analysis has revealed a high correlation between several scientometric indicators such as author h-index, author publications, citations and journal citations.

The interest in this study lies deeply in identifying the strengths and weaknesses of research groups and guides their actions. This evaluation contributes to the advancement of scientific research and to the motivation of researchers. Moreover, this paper can be applied as a complete in-depth guide to help new researchers select appropriate measurements to evaluate scientific production. The selection of evaluation measures is made according to their types, usage and levels of application. Furthermore, our analysis shows the similarity between the different indicators which can limit the overuse of similar measures.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

The main objective of the present study is to determine the role of citation-based metrics (PageRank and HITS’ authority and hub scores) and non-citation metrics (Goodreads readers, reviews and ratings, textbook edition counts) in predicting educational ranks of textbooks.

The rankings of 1869 academic textbooks of various disciplines indexed in Scopus were extracted from the Open Syllabus Project (OSP) and compared with normalized counts of Scopus citations, scores of PageRank, authority and hub (HITS) in Scopus book-to-book citation network, Goodreads ratings and reviews, review sentiment scores and WorldCat book editions.

Prediction of the educational rank of scholarly syllabus books ranged from 32% in technology to 68% in philosophy, psychology and religion. WorldCat editions in social sciences, medicine and technology, Goodreads ratings in humanities, and book-citation-network authority scores in law and political science accounted for the strongest predictions of the educational score. Thus, each indicator of editions, Goodreads ratings, and book citation authority score alone can be used to show the rank of the academic textbooks, and if used in combination, they will help explain the educational uptake of books even better.

This is the first study examining the role of citation indicators, Goodreads readers, reviews and ratings in predicting the OSP rank of academic books.

The purpose of this paper is to investigate the feasibility of automating Google Scholar searching to harvest citation data of monographs for collection analysis.

This study discusses the creation and refinement of a Scraper application programming interface query structure created to match library collection inventories to their Google Scholar listings to retrieve citation counts.

This paper indicates that Google Scholar is a feasible and usable tool for retrieving monograph citation data.

This study shows that Google Scholar citation data can be harvested for monographs in an automated fashion to serve as a source of bibliographic data, something not typically done outside of individual academics and writers tracking their personal academic impact factors.