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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 study aims to propose a framework to assess the scientific productivity performance of a country in terms of its international visibility and national capabilities.

In a given subject, all publications with at least one author from the target country as well as the received citations are counted as quantitative and qualitative indicators, respectively. The ratios of these counts to their expected values, which are estimated according to the global gross domestic product (GDP) and population percentages of the country are used to assess international visibility. Also, in certain publications, all authors are from the target country, therefore, their publication and citation proportions are provided as metrics of national competence.

As a sample, this study analyzes Turkey’s performance in “Business, Economics & Management” and “Engineering & Computer Science” in the top 20 publication venues of the regarding subject areas according to Google Scholar Metrics taxonomy. This study shows that in some subfields, Turkey’s performance is 2.73–6 times as per expectations. This study also provides the international visibility assessment of all countries for the past two decades in “Theoretical Computer Science” which shows that Israel is a leading country based on this framework.

This paper introduces new indices to evaluate a country’s national competence and international visibility on a subject field based on the number of published papers affiliated with the country and their citations by considering the global GDP and population share.

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.

The purpose of this study is to explore to which extent data mining research would be associated with the library and information science (LIS) discipline. This study aims to identify data mining related subject terms and topics in representative LIS scholarly publications.

A large set of bibliographic records over 38,000 was collected from a scholarly database representing the fields of LIS and the data mining, respectively. A multitude of text mining techniques were applied to investigate prevailing subject terms and research topics, such as influential term analysis and Dirichlet multinomial regression topic modeling.

The findings of this study revealed the relationship between the LIS and data mining research domains. Various data mining method terms were observed in recent LIS publications, such as machine learning, artificial intelligence and neural networks. The topic modeling result identified prevailing data mining related research topics in LIS, such as machine learning, deep learning, big data and among others. In addition, this study investigated the trends of popular topics in LIS over time in the recent decade.

This investigation is one of a few studies that empirically investigated the relationships between the LIS and data mining research domains. Multiple text mining techniques were employed to delineate to which extent the two research domains would be associated with each other based on both at the term-level and topic-level analysis. Methodologically, the study identified influential terms in each domain using multiple feature selection indices. In addition, Dirichlet multinomial regression was applied to explore LIS topics in relation to data mining.

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.

This paper explores who among the AACSB categorization of academics conducts the scholarship of teaching and learning (SoTL) research within business schools and how AACSB-accredited business schools capture SoTL research as part of their portfolio of intellectual contributions.

This study adopts a qualitative-method research design by collecting primary data through surveys, semi-structured interviews and secondary data in policy documents focused on AACSB-accredited business schools in Canada and the United States.

The findings establish that scholarly and practice academics who possess rigorously acquired research skills due to their terminal degrees are most likely to conduct SoTL research. The results also reveal an even split among respondents regarding whether their AACSB-accredited business school captures SoTL with their journal ranking frameworks.

Based on the findings, two recommendations are offered to foster more SoTL research at AACSB-accredited schools. First, higher education leaders (e.g. business school deans) can further inculcate a culture of SoTL research at the department and institutional levels by creating communities of practice (CoPs). Second, AACSB-accredited business schools could adopt more inclusive journal ranking frameworks to capture better and incentivize SoTL research.

This is the first known study to explore how AACSB Standards 3 and 8 are implemented and operationalized regarding SoTL research. Understanding how these standards are adopted and implemented could help institutional leaders, standard setters and administrators better facilitate SoTL research.

Driving economic development at the bottom of the pyramid (BOP) is an enduring global challenge. While the market-based approach places hope on entrepreneurship as a major impetus to drive the underdeveloped economy, the performance of entrepreneurial businesses and their impact on poverty reduction are sometimes below expectations. This paper seeks to examine the factors that may be hindering entrepreneurship within the BOP context. This paper presents preliminary answers and provides research suggestions related to this question.

In order to identify the reasons behind the underperformance of entrepreneurship at the BOP, a comprehensive literature review was conducted to see what is already known about this puzzle.

By reviewing extant literature, four clusters of factors were found to shape entrepreneurial activities at BOP: (1) Individual-level factors may be restraining entrepreneurial activities within BOP context, (2) gender inequality at BOP is hindering female entrepreneurship, (3) insufficient institutional support is holding back entrepreneurial activities in BOP and (4) business development initiatives are making multi-faceted impacts on entrepreneurial activities in BOP.

This paper contributes to theory in that it is the first comprehensive review of literature on constraints of entrepreneurship in the context of BOP. In investigating influential factors of entrepreneurial success in the BOP context, the authors recognize four major influential forces that are shaping entrepreneurial processes at the bottom of the pyramid and further propose three directions of future research that are worthy for further exploration.

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.