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For over a decade now, various stakeholders in accounting education have called for the integration of technology competencies in the accounting curriculum (Association to Advance Collegiate Schools of Business (AACSB), 2013, 2018; Accounting Education Change Commission (AECC), 1990; American Institute of Certified Public Accountant (AICPA), 1996; Behn et al., 2012; Lawson et al., 2014; PricewaterhouseCoopers (PWC), 2013). In addition to stakeholder expectations, the inclusion of data analytics as a key area in both the business and accounting accreditation standards of the AACSB signals the urgent need for accounting programs to incorporate data analytics into their accounting curricula. This paper examines the extent of the integration of data analytics in the curricula of accounting programs with separate accounting AACSB accreditation. The paper also identifies possible barriers to integrating data analytics into the accounting curriculum. The results of this study indicate that of the 177 AACSB-accredited accounting programs, 79 (44.6%) offer data analytics courses at either the undergraduate or graduate level or as a special track. The results also indicate that 41 (23.16%) offer data analytics courses in their undergraduate curriculum, 61 (35.88%) at the graduate level, and 12 (6.80%) offer specialized tracks for accounting data analytics. Taken together, the findings indicate an encouraging trend, albeit slow, toward the integration of data analytics into the accounting curriculum.

This study aims to investigate the relationships between big data analytics, management accounting practices and corporate sustainability and, more precisely, the impact of the integration between big data analytics and management accounting on corporate sustainability performance development.

A qualitative case study approach is used in this study with multiple collecting data tools as in-depth interviews and observations, in addition to the content analysis used of the annual reports for the year 2021, of Almarai manufacturing corporate (one of the leaders of food and beverage manufacturing corporates in Saudi Arabia and other countries).

Research findings provide good insights about the significant impact of the effective integration between big data analytics and management accounting on corporate sustainability performance development, big data can assist management accounting to form corporate value-added strategies and activities.

The study is limitedly applied to one manufacturing corporate as a study case; therefore, the findings cannot be generalized. Thus, future research can examine the association between the current study variables with wide-scale applications and with different approaches and in different contexts to enrich the findings. Moreover, future research may focus on the integration between big data analytics and management accounting reports in the meta-verse environment to explore the benefits that corporates could gain from the features and capabilities of meta-verse technology.

There is a research gap regarding the impact of the integration between big data analytics and management accounting practices on corporate sustainability development, as most of the previous studies focused on two variables only of the current study variables; therefore, this study tries to investigate and give important insights about it.

This study aims to focus on the five most relevant and discursive emerging technologies in accounting (cloud computing, big data and data analytics, blockchain, artificial intelligence (AI) and robotics process automation [RPA]). It investigates the adoption and use of these technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey.

The study investigates the adoption and use of emerging technologies based on data collected from accounting professionals in a technology-developed country – Canada, through a survey. This study considers the said nature and characteristics of emerging technologies and proposes a model using the factors that have been found to be significant and most commonly investigated by existing prior technology-organization-environment (TOE)-related technology adoption studies. This survey applies the TOE framework and examines the influence of significant and most commonly known factors on Canadian firms’ intention to adopt the said emerging technologies.

Study results indicate that Canadian accounting professionals’ self-assessed knowledge (about these emerging technologies) is more theoretical than operational. Cloud computing is highly used by Canadian firms, while the use of other technologies, particularly blockchain and RPA, is reportedly low. However, firms’ intention about the future adoption of these technologies seems positive. Study results reveal that only the relative advantage and top management commitment are found to be significant considerations influencing the adoption intention.

Study findings confirm some results presented in earlier studies but provide additional insights from a new perspective, that of accounting professionals in Canada. The first limitation relates to the respondents. Although accounting professionals provided valuable insights, their responses are personal views and do not necessarily represent the views of other professionals within the same firm or the official position of their accounting departments or firms. Therefore, the exclusion of diverse viewpoints from the same firm might have negatively impacted the results of this study. Second, this study sample is limited to Canada-based firms, which means that the study reflects only the situation in that country. Third, considering the research method and the limit on the number of questions the authors could ask, respondents were only asked to rate the impact of these five technologies on the accounting field and to clarify which technologies are used.

This study’s findings confirm that the organizational intention to adopt new technology is not primarily based on the characteristics of the technology. In the case of emerging technology adoption, the decision also depends upon other factors related to the internal organization. Furthermore, although this study found no support for the effect of environmental factors, it fills a gap in the literature by including the factor of vendor support, which has received little attention in prior information technology (IT)/ information system (IS) adoption research. Moreover, in contrast to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study findings may serve as a guide for researchers, practitioners, firms and other stakeholders, particularly technology providers, interested in learning about emerging technologies’ adoption and use in Canada and/or in a relevant context. Contrary to most prior adoption studies, this study elaborates on accounting professionals’ experience and perceptions in investigating the organizational adoption and use of emerging technologies. Thus, the findings of this study are valuable, providing insights from a new perspective, that of professional accountants.

The study provides insights into the said technologies’ actual adoption and improves the awareness of firms and stakeholders to the effect of some constructs that influence the adoption of these emerging technologies in accounting.

Prior research (Harrast, Olsen, & Sun, 2023) analyzes the eight emerging topics to be included in future CPA exams and discusses their importance to career success and appropriate teaching locus in light of survey evidence. They find that the general topic of data analytics is the most important of the eight emerging topics. To further understand the topics most important to career success, this study analyzes subtopics underlying the eight emerging topics. The results show that advanced Excel analysis tools, data visualization, and data extraction, transformation, and loading (ETL) are the most important data analytics subskills for career success according to professionals and that these topics should be both introduced and emphasized in the accounting curriculum. The results provide useful information to educators to prioritize general emerging topics and specific subtopics in the accounting curriculum by taking into account the most pressing needs of the profession.

The purpose of this paper is twofold. First, to combine a holistic model – in our case the balanced scorecard – with the time-driven activity-based costing model. The inspiration for this stems both from Kaplan and Norton and from the intense discussions and use of business analytics (BA) and performance management (PM). Second, to use numerical experiments – more specifically Monte Carlo simulation – to design and explore four hypothetical scenarios within such a holistic model. The paper also aims to emphasise the role played by statistics in increasing the confidence in using such a framework.

The author runs four numerical experiments using different assumptions to show how a decision-maker can improve the outcome by making small changes in the key performance indicator (KPI) input variables.

The paper gives recommendations for the assumptions that each decision-maker has to consider when setting out to conduct this kind of analysis. Small changes in some input variables may completely change the output and hence the decision result.

The paper shows why practitioners and researchers need to better understand the limitations of deterministic analysis to make realistic models when combining more accounting models. To choose the relevant probability distributions for the input resources is an important issue for the decision-maker as they have a very large impact on the result.

The real value of the paper lies in making students and practitioners as well as researchers aware of the opportunities for stochastic modelling and also to point at the problems and limitations of combining elements from BA with performance measurement and management.

This paper aims to explore the changing role of the accountant amid multiple drivers, responses of accountants and situated rationality in a multinational firm, Max-choice Lanka.

It adopts the single-site case study approach under the qualitative methodology and leans on institutional theory, specifically Ter Bogt and Scapens (2019) framework.

The case study findings reveal that the role of the accountant has undergone change amid local and broader institutions linked to organizational culture/norms, the influence of the parent company, global trends and technological advancements. Based on evolving situated rationalities, the contemporary accountant performs an agile role as a value-adding business partner; data scientist; strategic decision-maker; and a cross-functional team member.

At the practice level, identifying drivers influencing the changing role of accountants enables organizations to shape their accounting functions attuned to evolving needs by implementing appropriate strategies and recruiting competent personnel. In the realm of education, it calls for incorporating areas such as big data analytics, artificial intelligence, reporting nonfinancial information and integrated accounting software to the accounting curricular and upskill students based on industry expectations catering to changing roles.

This paper adds to the ongoing debate on the contemporary role of the accountant. Capitalizing on case study data, this research illuminates the influence of multiplicity of institutions, different forms and situated rationality within this changing role and extends the Ter Bogt and Scapens (2019) framework.

The purpose of this study is to develop a method to assess social performance. Traditionally, environment, social and governance (ESG) rating providers use subjectively weighted arithmetic averages to combine a set of social performance (SP) indicators into one single rating. To overcome this problem, this study investigates the preconditions for a new methodology for rating the SP component of the ESG by applying machine learning (ML) and artificial intelligence (AI) anchored to social controversies.

This study proposes the use of a data-driven rating methodology that derives the relative importance of SP features from their contribution to the prediction of social controversies. The authors use the proposed methodology to solve the weighting problem with overall ESG ratings and further investigate whether prediction is possible.

The authors find that ML models are able to predict controversies with high predictive performance and validity. The findings indicate that the weighting problem with the ESG ratings can be addressed with a data-driven approach. The decisive prerequisite, however, for the proposed rating methodology is that social controversies are predicted by a broad set of SP indicators. The results also suggest that predictively valid ratings can be developed with this ML-based AI method.

This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments.

The proposed ML-based AI method can help to achieve better ESG ratings, which will in turn help to improve SP, which has implications for organizations and societies through sustainable development.

To the best of the authors’ knowledge, this research is one of the first studies that offers a unique method to address the ESG rating problem and improve sustainability by focusing on SP indicators.

The purpose of this research paper is to explore and offer insightful information on the useful use of Google Tag Manager (GTM) in the context of library websites and to bridge the gap between GTM’s technical features and the practical requirements of libraries. It gives libraries the ability to use GTM’s capabilities to increase user engagement, data-driven decision-making and improve online services.

This study reviews existing literature on GTM in the context of websites and libraries. The methodology involves identifying keywords and searching terms related to GTM, digital marketing, user engagement, Web analytics and library websites. Sources and databases were consulted, including library science journals, marketing journals, academic databases, publications on digital marketing and search platforms such as Google Books, Google Scholar, Google Search Engine, JSTOR and library associations like the American Library Association. Initial screening was done based on titles and abstracts, followed by a thorough-text review, categorization and synthesizing of the findings.

GTM provides libraries with a potent tool to improve their online presence, customize user experiences and collect insightful real-time data. Libraries may harness GTM’s potential to better engage people and provide services by properly implementing it and maintaining it over time. It can be a flexible instrument that supports contemporary library services in the digital era. The findings of this study indicate that GTM technology may be used in library services; nevertheless, there are several barriers, such as librarians’ attitudes and technical abilities, that prevent GTM acceptance in library services.

This study covers the implementation of a free GTM tool in library websites that will help the library and information professionals to leverage the GTM in the library’s online presence. Furthermore, this study recommends that libraries and librarians should develop guidelines and policies for the critical adoption of a free GTM tool in the library environment, which will support improving the library’s user engagement and tracking of library website traffic.

This study aims to scrutinize the relationship between the perception of big data (BD) features and the primary outcomes of financial accounting. Likewise, it explores whether financial accounting practices moderate the relationship between BD features and firm sustainability.

The study used a questionnaire survey based on the Likert scale for two distinct groups of participants: academic scholars and industry practitioners operating in the BD era within the energy sector.

The results reveal significant positive associations between BD features and firm performance, reporting quality, earnings determinants, fair value measurements, risk management, firm value, the efficiency of the decision-making process, narrative disclosure and firm sustainability. Besides, the path analysis indicates an indirect impact of BD on firm sustainability via financial accounting practices. The results suggest that energy firms should consider incorporating BD analysis into their financial accounting processes to improve their sustainability performance and create long-term value for their stakeholders.

The findings are particularly interesting to academics in accounting and business to improve the accounting curriculums to fit the technological revolution, especially in the field of BD analytics. Practitioners within energy industries must also refine their skills and knowledge to meet the challenges of BD in the foreseeable future. The results provide important implications for policy setters to revise current financial accounting standards to cope with technological innovation.

The study makes a valuable contribution by critically examining the impact of BD on various financial accounting practices neglected in prior research. It highlights the transformative power of BD in the domain of financial accounting and provides insights into its potential implications for energy firms.

Auditors’ skills and knowledge of data analytics and big data can influence their judgment at the audit planning stage. At this stage, the auditor will determine the level of audit risk and estimate how long the audit will take. This study aims to test whether big data and data analytics affect auditors’ judgment by adopting the cognitive fit theory.

This was an experimental study involving 109 accounting students as participants. The 2 × 2 factorial design between subjects in a laboratory setting was applied to test the hypothesis.

First, this study supports the proposed hypothesis that participants who are provided with visual analytics information will rate audit risk lower than text analytics. Second, participants who receive information on unstructured data types will assess audit risk (audit hours) higher (longer) than those receiving structured data types. In addition, those who receive information from visual analytics results have a higher level of reliance than those receiving text analytics.

This research has implications for external and internal auditors to improve their skills and knowledge of data analytics and big data to make better judgments, especially when the auditor is planning the audit.

Previous studies have examined the effect of data analytics (predictive vs anomaly) and big data (financial vs non-financial) on auditor judgment, whereas this study examined data analytics (visual vs text analytics) and big data (structured and unstructured), which were not tested in previous studies.