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Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.

This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.

Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.

This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.

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.

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.

The purpose of this paper is to present data that begin to detail the deficiencies of log file analytics reporting methods that are commonly built into institutional repository (IR) platforms. The authors propose a new method for collecting and reporting IR item download metrics. This paper introduces a web service prototype that captures activity that current analytics methods are likely to either miss or over-report.

Data were extracted from DSpace Solr logs of an IR and were cross-referenced with Google Analytics and Google Search Console data to directly compare Citable Content Downloads recorded by each method.

This study provides evidence that log file analytics data appear to grossly over-report due to traffic from robots that are difficult to identify and screen. The study also introduces a proof-of-concept prototype that makes the research method easily accessible to IR managers who seek accurate counts of Citable Content Downloads.

The method described in this paper does not account for direct access to Citable Content Downloads that originate outside Google Search properties.

This paper proposes that IR managers adopt a new reporting framework that classifies IR page views and download activity into three categories that communicate metrics about user activity related to the research process. It also proposes that IR managers rely on a hybrid of existing Google Services to improve reporting of Citable Content Downloads and offers a prototype web service where IR managers can test results for their repositories.

This paper aims to compare the nature of three different business and management education (BME) research streams (online/blended learning, entrepreneurship education and experiential learning), along with their citation sources to draw insights on their support and legitimacy bases, with lessons on improving such support and legitimacy for the streams and the wider BME research field.

The authors analyze the nature of three BME research streams and their citation sources through tests of differences across streams.

The three streams differ in research foci and approaches such as the use of managerial samples in experiential learning, quantitative studies in online/blended education and literature reviews in entrepreneurship education. They also differ in sources of legitimacy recognition and avenues for mobilization of support. The underlying literature development pattern of the experiential learning stream indicates a need for BME scholars to identify and build on each other’s work.

Identification of different research bases and key supporting literature in the different streams shows important core articles that are useful to build research in each stream.

Readers will understand the different research bases supporting the three research streams, along with their targeted audience and practice implications.

The discovery of different support bases for the three different streams helps identify the network of authors and relationships that have been built in each stream.

According to the authors’ knowledge, this paper is the first to uncover differences in nature and citation sources of the three continuously growing BME research streams with recommendations on ways to improve the support of the three streams.

Despite the general recommendation of using a combination of multiple criteria for research assessment and faculty promotion decisions, the raise of quantitative indicators is generating an emerging trend in Business Schools to use single journal impact factors (IFs) as key (unique) drivers for those relevant school decisions. This paper aims to investigate the effects of using single Web of Science (WoS)-based journal impact metrics when assessing research from two related disciplines: Business and Economics, and its potential impact for the strategic sustainability of a Business School.

This study collected impact indicators data for Business and Economics journals from the Clarivate Web of Science database. We concentrated on the IF indicators, the Eigenfactor and the article influence score (AIS). This study examined the correlations between these indicators and then ranked disciplines and journals using these different impact metrics.

Consistent with previous findings, this study finds positive correlations among these metrics. Then this study ranks the disciplines and journals using each impact metric, finding relevant and substantial differences, depending on the metric used. It is found that using AIS instead of the IF raises the relative ranking of Economics, while Business remains basically with the same rank.

This study contributes to the research assessment literature by adding substantial evidence that given the sensitivity of journal rankings to particular indicators, the selection of a single impact metric for assessing research and hiring/promotion and tenure decisions is risky and too simplistic. This research shows that biases may be larger when assessment involves researchers from related disciplines – like Business and Economics – but with different research foundations and traditions.

Consistent with the literature, given the sensibility of journal rankings to particular indicators, the selection of a single impact metric for assessing research, assigning research funds and hiring/promotion and tenure decisions is risky and simplistic. However, this research shows that risks and biases may be larger when assessment involves researchers from related disciplines – like Business and Economics – but with different research foundations and trajectories. The use of multiple criteria is advised for such purposes.

This is an applied work using real data from WoS that addresses a practical case of comparing the use of different journal IFs to rank-related disciplines like Business and Economics, with important implications for faculty tenure and promotion committees and for research funds granting institutions and decision-makers.

The purpose of this paper is to critically analyze the influence of the algorithm used on scholarly search engines (Garfield’s algorithm) and propose metrics to improve it so that science could be based on a more democratic way.

This paper used a snow-ball approach to collect data that allowed identifying the history and the logic behind the Garfield’s algorithm. It follows on excerpting the foundation of existing algorithm and databases of major scholarly search engine. It concluded proposing new metrics so as to surpass restraints and to democratize the scientific discourse.

This paper finds that the studied algorithm currently biases the scientific discourse toward a narrow perspective, while it should take into consideration several researchers’ characteristics. It proposes the substitution of the h-index by the number of times the scholar’s most cited work has been cited. Finally, it proposes that works in languages different than English should be included.

The broad comprehension of any phenomena should be based on multiple perspectives; therefore, the inclusion of diverse metrics will extend the scientific discourse.

The improvement of the existing algorithm will increase the chances of contact among different cultures, which stimulate rapid progress on the development of knowledge.

The value of this paper resides in demonstrating that the algorithm used in scholarly search engines biases the development of science. If updated as proposed here, science will be unbiased and bias aware.

This paper examines the socio-political affordances of metrics in research evaluation and the consequences of epistemic injustice in research practices and recorded knowledge.

First, the use of metrics is examined as a mechanism that promotes competition and social acceleration. Second, it is argued that the use of metrics in a competitive research culture reproduces systemic inequalities and leads to epistemic injustice. The conceptual analysis draws on works of Hartmut Rosa and Miranda Fricker, amongst others.

The use of metrics is largely driven by competition such as university rankings and league tables. Not only that metrics are not designed to enrich academic and research culture, they also suppress the visibility and credibility of works by minorities. As such, metrics perpetuate epistemic injustice in knowledge practices; at the same time, the reliability of metrics for bibliometric and scientometric studies is put into question.

As metrics leverage who can speak and who will be heard, epistemic injustice is reflected in recorded knowledge and what we consider to be information.

This paper contributes to the discussion of metrics beyond bibliometric studies and research evaluation. It argues that metrics-induced competition is antithetical to equality and diversity in research practices.

Research into the interpretability and explainability of data analytics and artificial intelligence (AI) systems is on the rise. However, most recent studies either solely promote the benefits of explainability or criticize it due to its counterproductive effects. This study addresses this polarized space and aims to identify opposing effects of the explainability of AI and the tensions between them and propose how to manage this tension to optimize AI system performance and trustworthiness.

The author systematically reviews the literature and synthesizes it using a contingency theory lens to develop a framework for managing the opposing effects of AI explainability.

The author finds five opposing effects of explainability: comprehensibility, conduct, confidentiality, completeness and confidence in AI (5Cs). The author also proposes six perspectives on managing the tensions between the 5Cs: pragmatism in explanation, contextualization of the explanation, cohabitation of human agency and AI agency, metrics and standardization, regulatory and ethical principles, and other emerging solutions (i.e. AI enveloping, blockchain and AI fuzzy systems).

As in other systematic literature review studies, the results are limited by the content of the selected papers.

The findings show how AI owners and developers can manage tensions between profitability, prediction accuracy and system performance via visibility, accountability and maintaining the “social goodness” of AI. The results guide practitioners in developing metrics and standards for AI explainability, with the context of AI operation as the focus.

This study addresses polarized beliefs amongst scholars and practitioners about the benefits of AI explainability versus its counterproductive effects. It poses that there is no single best way to maximize AI explainability. Instead, the co-existence of enabling and constraining effects must be managed.

This study demonstrates that aggregated data from the Repository Analytics and Metrics Portal (RAMP) have significant potential to analyze visibility and use of institutional repositories (IR) as well as potential factors affecting their use, including repository size, platform, content, device and global location. The RAMP dataset is unique and public.

The webometrics methodology was followed to aggregate and analyze use and performance data from 35 institutional repositories in seven countries that were registered with the RAMP for a five-month period in 2019. The RAMP aggregates Google Search Console (GSC) data to show IR items that surfaced in search results from all Google properties.

The analyses demonstrate large performance variances across IR as well as low overall use. The findings also show that device use affects search behavior, that different content types such as electronic thesis and dissertation (ETD) may affect use and that searches originating in the Global South show much higher use of mobile devices than in the Global North.

The RAMP relies on GSC as its sole data source, resulting in somewhat conservative overall numbers. However, the data are also expected to be as robot free as can be hoped.

This may be the first analysis of aggregate use and performance data derived from a global set of IR, using an openly published dataset. RAMP data offer significant research potential with regard to quantifying and characterizing variances in the discoverability and use of IR content.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-08-2020-0328