A machine learning analysis of citation impact among selected Pacific Basin journals

Jones, Stewart; Alam, Nurul

doi:10.1111/acfi.12584

Cited by 11 publications

(4 citation statements)

References 169 publications

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“…To test the validity and reliability of this result, we applied several other types of analysis suggested by researchers working with literature reviews. For example, Dumay and Cai (2014) and Jones and Alam (2019) argue that citation impact factors are increasingly important because they identify the most influential articles. Highly cited articles represent a “corpus of scholarly literature” that can help “develop insights, critical reflections, future research paths and research questions” (Massaro et al , 2016, p. 767).…”

Section: Methodsmentioning

confidence: 99%

Blockchain in accounting research: current trends and emerging topics

Garanina

Ranta

Dumay

2021

AAAJ

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PurposeThis 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/approachThis 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.FindingsBlockchain 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/implicationsThe 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 implicationsThe 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/valueThe authors’ structured literature review uniquely identifies critical research topics for developing future research directions related to blockchain in accounting.

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Section: Methodsmentioning

confidence: 99%

Blockchain in accounting research: current trends and emerging topics

Garanina

Ranta

Dumay

2021

AAAJ

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“…The gradient-boosting algorithm (Friedman, 2001(Friedman, , 2002Friedman et al, 2000) implements a gradient descent algorithm to the boosting model, significantly improving its efficiency for applications. In accounting and finance research, ensemble learning algorithms have been used, for example, to predict financial failure (Jiang & Jones, 2018;Jones, 2017), to identify and predict features that influence citation impacts (Jones & Alam, 2019), to detect fraud (Bao et al, 2020;Gepp et al, 2021), to model the conditional risk premium (Hoang & Faff, 2021), to improve managerial estimates in financial reports (Ding et al, 2020), to predict corporate bond default (Lu & Zhuo, 2021), and to examine the value and relative importance of various board and firm characteristics in predicting workplace diversity (Ranta & Ylinen, 2023). The gradient boosting algorithm was introduced in the works of Friedman et al (2000), Friedman (2001), and Friedman and Popescu (2003).…”

Section: A Tree-based Gradient-boosting Modelmentioning

confidence: 99%

Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach

Ylinen

Ranta

2023

Accounting & Finance

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This study examines the ability of crowdsourced employee opinions about their workplace to reveal value‐relevant information about corporate culture. We investigate the employee‐friendly (EF) corporate culture values that are strongly associated with firm value and operating performance using a unique social media dataset of approximately 250,000 crowdsourced employee reviews to evaluate 18 distinct characteristics of a firm's corporate culture. The explainable machine learning model is used to examine the nonlinear associations and relative importance of employee‐friendly cultural values. We find that several employee‐friendly corporate culture features are associated with firms' value (Tobin's Q) and operating performance (ROA). Our findings reveal two features whose association is clearly superior to other EF culture variables in our explainable machine learning model: pride in the company for Tobin's Q and job security for ROA. Based on the SHAP values, their effects are positive, significant, and relatively linear.

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“…Given that such methods are still only emerging, their applications in the fields of accounting or finance are limited; however, some initial literature reviews are emerging ( e.g. Cai et al, 2019;Jones and Alam, 2019). However, we are able to draw upon developments in fields of machine learning techniques which are increasingly focussed not just on static word frequency measures but also on topic detection algorithms (Marrone, 2020).…”

Section: Note(s)mentioning

confidence: 99%

Trends in environmental accounting research within and outside of the accounting discipline

Marrone

Linnenluecke

Richardson

et al. 2020

AAAJ

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PurposeThe purpose of this article is to track the emergence of topics and research trends in environmental accounting research by using a machine learning method for literature reviews. The article shows how the method can track the emergence of topics and research trends over time.Design/methodology/approachThe analysis of the emergence of topics and shifts in research trends was based on a machine learning approach that allowed the authors to identify “topic bursts” in publication data. The data set of this study contained, 2,502 records published between 1972 and 2019, both within and outside of accounting journals. The data set was assembled through a systematic keyword search of the literature.FindingsFindings indicated that research studies within accounting journals have addressed sustainability concerns in a general fashion, with a recent focus on broad topics such as corporate social responsibility (CSR) and stakeholder theory. Research studies published outside of accounting journals have focussed on more specific topics (e.g. the shift to a low-carbon or circular economy, the attainment of the sustainable development goals [SDGs], etc.) and new methodologies (e.g. accounting for ecosystem services).Research limitations/implicationsThe method provides an approach for identifying “trending” topics within accounting and non-accounting journals and allows to identify topics and areas that could benefit from a greater exchange of ideas between accounting and non-accounting journals.Originality/valueThe authors provide a much needed review of research on the vitally important topic of environmental accounting not only in accounting journals but also in the broader research community.

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A machine learning analysis of citation impact among selected Pacific Basin journals

Cited by 11 publications

References 169 publications

Blockchain in accounting research: current trends and emerging topics

Blockchain in accounting research: current trends and emerging topics

Employer ratings in social media and firm performance: Evidence from an explainable machine learning approach

Trends in environmental accounting research within and outside of the accounting discipline

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