Scholarly Impact: a Bibliometric and Altmetric study of the Journal of Community Informatics

Demonstrating scholarly impact is a matter of growing importance. This paper reports on a bibliometric and altmetric analysis conducted on the Journal of Community Informatics (JOCI). Besides the bibliometric analysis the study also looked into JOCI article-level metrics by comparing usage metrics (article views), alternative metrics (Mendeley readership), and traditional citation metrics (Google Scholar citations). The main contribution is to provide more insight into the metrics that could influence the citation impact in Community Informatics research. Furthermore, the study used article-level metrics data to identify, compare and rank the most impactful papers published in JOCI over a 12-year period

Evaluating interpretable machine learning predictions for cryptocurrencies

This study explores various machine learning and deep learning applications on financial data modelling, analysis and prediction processes. The main focus is to test the prediction accuracy of cryptocurrency hourly returns and to explore, analyse and showcase the various interpretability features of the ML models. The study considers the six most dominant cryptocurrencies in the market: Bitcoin, Ethereum, Binance Coin, Cardano, Ripple and Litecoin. The experimental settings explore the formation of the corresponding datasets from technical, fundamental and statistical analysis. The paper compares various existing and enhanced algorithms and explains their results, features and limitations. The algorithms include decision trees, random forests and ensemble methods, SVM, neural networks, single and multiple features N-BEATS, ARIMA and Google AutoML. From experimental results, we see that predicting cryptocurrency returns is possible. However, prediction algorithms may not generalise for different assets and markets over long periods. There is no clear winner that satisfies all requirements, and the main choice of algorithm will be tied to the user needs and provided resources

Blockchain in accounting research : current trends and emerging topics

Purpose This paper provides a structured literature review of blockchain in accounting. The authors identify current trends, analyse and critique the key topics of research and discuss the future of this nascent field of inquiry. Design/methodology/approach This study’s analysis combined a structured literature review with citation analysis, topic modelling using a machine learning approach and a manual review of selected articles. The corpus comprised 153 academic papers from two ranked journal lists, the Association of Business Schools (ABS) and the Australian Business Deans Council (ABDC), and from the Social Science Research Network (SSRN). From this, the authors analysed and critiqued the current and future research trends in the four most predominant topics of research in blockchain for accounting. Findings Blockchain is not yet a mainstream accounting topic, and most of the current literature is normative. The four most commonly discussed areas of blockchain include the changing role of accountants; new challenges for auditors; opportunities and challenges of blockchain technology application; and the regulation of cryptoassets. While blockchain will likely be disruptive to accounting and auditing, there will still be a need for these roles. With the sheer volume of information that blockchain records, both professions may shift out of the back-office toward higher-profile advisory roles where accountants try to align competitive intelligence with business strategy, and auditors are called on ex ante to verify transactions and even whole ecosystems. Research limitations/implications The authors identify several challenges that will need to be examined in future research. Challenges include skilling up for a new paradigm, the logistical issues associated with managing and monitoring multiple parties all contributing to various public and private blockchains, and the pressing need for legal frameworks to regulate cryptoassets. Practical implications The possibilities that blockchain brings to information disclosure, fraud detection and overcoming the threat of shadow dealings in developing countries all contribute to the importance of further investigation into blockchain in accounting. Originality/value The authors’ structured literature review uniquely identifies critical research topics for developing future research directions related to blockchain in accounting.© Tatiana Garanina, Mikko Ranta and John Dumay. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcodefi=vertaisarvioitu|en=peerReviewed

Neural networks for estimating Macro Asset Pricing model in football clubs

The recent crisis caused by COVID-19 directly affected consumption habits and thestability sof financial markets. In particular, the football industry has been hit hard bythis pandemic and therefore has more volatile stock prices. Given this new scenario,further research is needed to accurately estimate the value of the shares of footballclubs. In this paper, we estimate an asset pricing model in football clubs with differentcompositions of risk nature using non-linear techniques of artificial neural networks.Usually, asset pricing models have been estimated with linear methods such as ordi-nary least squares (...

Trends in Russian research output indexed in Scopus and Web of Science

Trends are analysed in the annual number of documents published by Russian institutions and indexed in Scopus and Web of Science, giving special attention to the time period starting in the year 2013 in which the Project 5-100 was launched by the Russian Government. Numbers are broken down by document type, publication language, type of source, research discipline, country and source. It is concluded that Russian publication counts strongly depend upon the database used, and upon changes in database coverage, and that one should be cautious when using indicators derived from WoS, and especially from Scopus, as tools in the measurement of research performance and international orientation of the Russian science system.Comment: Author copy of a manuscript accepted for publication in the journal Scientometrics, May 201

Scientometric analysis in the field of big data and artificial intelligence in industry

Big Data and Artificial Intelligence (BD&AI) in Industry have grown so prevalent, and the potential they provide is so revolutionary that they are seen as critical for competitive growth. Because the number of organizations BD&AI on Industry technology is increasing exponentially, so is the need for BD&AI on Industry practitioners. Until we conducted this research, only 1399 academic documents on BD&AI in Industry found from 2002 to 2020 were obtained by searching the Scopus database. BD&AI in the industrial sector is examined in-depth in this paper. This study uses bibliometric analysis and indexed digital methods to map scientific publications worldwide. This study uses the Scopus database to collect information and online analysis via the Scopus website and VOSViewer to demonstrate bibliometric network mapping. We use the article selection process, starting with the keywords to be searched for, the year limitation, then the database is exported into RIS and CSV format files. From the database, we also perform network mapping using VOSViewer. Researchers in China have the most articles published and indexed by Scopus among the most prolific authors (373), followed by the United States (239) and India with 125 academic publications. Data analysis reveals an upward trend in the number of worldwide publications in BD&AI in Industry, as measured by the Scopus index

Accounting, accountability, social media and big data: Revolution or hype?

Purpose: The purpose of this paper is to outline an agenda for researching the relationship between technology-enabled networks â such as social media and big data â and the accounting function. In doing so, it links the contents of an unfolding area research with the papers published in this special issue of Accounting, Auditing and Accountability Journal. Design/methodology/approach: The paper surveys the existing literature, which is still in its infancy, and proposes ways in which to frame early and future research. The intention is not to offer a comprehensive review, but to stimulate and conversation. Findings: The authors review several existing studies exploring technology-enabled networks and highlight some of the key aspects featuring social media and big data, before offering a classification of existing research efforts, as well as opportunities for future research. Three areas of investigation are identified: new performance indicators based on social media and big data; governance of social media and big data information resources; and, finally, social media and big dataâs alteration of information and decision-making processes. Originality/value: The authors are currently experiencing a technological revolution that will fundamentally change the way in which organisations, as well as individuals, operate. It is claimed that many knowledge-based jobs are being automated, as well as others transformed with, for example, data scientists ready to replace even the most qualified accountants. But, of course, similar claims have been made before and therefore, as academics, the authors are called upon to explore the impact of these technology-enabled networks further. This paper contributes by starting a debate and speculating on the possible research agendas ahead

Can we trust ESG Ratings? Some insights based on a bibliometric analysis of ESG data quality and rating reliability

The aim of this research is to investigate the quality and reliability of ESG data provided by companies, as well as the accuracy and objectivity of ESG ratings produced by sus- tainability rating agencies (SRAs). Since SRAs use companies’ non-financial information as input data when formulating their ESG ratings, these two topics appear to be strictly interconnected. Drawing on the Shanon and Weaver (1949) model of communication, we have addressed these issues by means of a systematic literature review combined with a bibliometric anal- ysis. In our investigation we run: i) the co-citation analysis to detect the seminal papers; ii) a keyword co-occurrence analysis to explore how the main features of the academic debate have unfolded in the last five years; iii) a keyword co-occurrence analysis to obtain a network visualisation map to explore how the research broad scope was articulated in different clusters (i.e., themes of research). Among the clusters that emerged from the mapping, we have decided to delve into the streams of research we consider most relevant and deal with: the relationships between ESG and Artificial Intelligence (AI). Namely, we deem that AI may allow us to process massive amounts of data that contain crucial infor- mation for ESG investing. However, even if computer algorithms are able to analyse all information available efficiently, and in a timely manner, managers and investors should be aware of their opportunities and criticisms, while scholars should list propositions for advancing the research on these topics