Mid Sweden University

This handbook collects the most up-to-date scholarship, knowledge, and new developments of big data and data analytics by bringing together many strands of contextual and disciplinary research. In recent times, while there has been considerable research in exploring the role of big data, data analytics, and textual analytics in accounting, and auditing, we still lack evidence on what kinds of best practices academics, practitioners, and organizations can implement and use. To achieve this aim, the handbook focuses on both conventional and contemporary issues facing by academics, practitioners, and organizations particularly when technology and business environments are changing faster than ever. All the chapters in this handbook provide both retrospective and contemporary views and commentaries by leading and knowledgeable scholars in the field, who offer unique insights on the changing role of accounting and auditing in today’s data and analytics driven environment. Aimed at academics, practitioners, students, and consultants in the areas of accounting, auditing, and other business disciplines, the handbook provides high-level insight into the design, implementation, and working of big data and data analytics practices for all types of organizations worldwide. The leading scholars in the field provide critical evaluations and guidance on big data and data analytics by illustrating issues related to various sectors such as public, private, not-for-profit, and social enterprises. The handbook’s content will be highly desirable and accessible to accounting and non-accounting audiences across the globe. 

Big data and analytics offer new opportunities and challenges for academics and practitioners in all business disciplines including accounting and auditing. In the backdrop of increasing growth of emerging technologies, the organizations in public, private and not-for-profit sectors are embracing digital economy and the fourth industrial revolution journey. This requires knowledge of better practice examples, lessons learned and future directions in addressing the new challenges and seizing new opportunities. In this chapter, we discuss the implications of data analytics, artificial intelligence and machine learning on the accounting and auditing practices. We focus on the technological, social, political, economic, institutional, and behavioral aspects of these technologies in the public, private, non-governmental and hybrid contexts. We present state-of-the-art research directions on philosophical, theoretical, methodological, and practical issues, new developments and innovations of big data, analytics, artificial intelligence, machine learning, blockchain, cryptocurrencies and other emerging technologies related to accounting and auditing.

We develop a new methodology for computing environmental, social, and governance (ESG) ratings using a mode of artificial intelligence (AI) called machine learning (ML) to make ESG more transparent. The ML algorithms anchor our rating methodology in controversies related to non-compliance with corporate social responsibility (CSR). This methodology is consistent with the information needs of institutional investors and is the first ESG methodology with predictive validity. Our best model predicts what companies are likely to experience controversies. It has a precision of 70–84 per cent and high predictive performance on several measures. It also provides evidence of what indicators contribute the most to the predicted likelihood of experiencing an ESG controversy. Furthermore, while the common approach of rating companies is to aggregate indicators using the arithmetic average, which is a simple explanatory model designed to describe an average company, the proposed rating methodology uses state-of-the-art AI technology to aggregate ESG indicators into holistic ratings for the predictive modelling of individual company performance. Predictive modelling using ML enables our models to aggregate the information contained in ESG indicators with far less information loss than with the predominant aggregation method. 

Institutional investors seek to make environmentally sustainable investments using environment, social, governance (ESG) ratings. Current ESG ratings have limited validity because they are based on idiosyncratic scores derived using subjective, discretionary methodologies. We discuss a new direction for developing corporate environmental performance (CEP) ratings and propose a solution to the limited validity problem by anchoring such ratings in environmental controversies. The study uses a novel machine learning approach to make the ratings more comprehensive and transparent, based on a set of algorithmic approaches that handle nonlinearity when aggregating ESG indicators. This approach minimizes the rater subjectivity and preferences inherent in traditional ESG indicators. The findings indicate that controversies as proxies for non-compliance with environmental responsibilities can be predicted well. We conclude that environmental performance ratings developed using our machine learning framework offer predictive validity consistent with institutional investors’ demand for socially responsible investment screening. 

This study examines how managers and partners in audit firms perceive the moral intensity of various reduced audit quality (RAQ) acts, and whether perceived moral intensity affects the likelihood of these acts being committed. We surveyed managers and partners employed by audit firms operating in Sweden, measuring their perceptions of the moral intensity of seven RAQ acts using Jones' (1991) moral intensity scale and their self-reported frequencies of these acts. The study finds that managers and partners regard RAQ acts as morally serious, and that the moral intensity of an RAQ act is negatively related to the frequency of the act's occurrence for three of the seven acts. This suggests that managers' and partners' moral intensity perceptions do not unequivocally discourage auditors from committing these offences.

This study examines whether charismatic client leadership constrains the objectivity of auditor judgment. Previous accounting research has found that auditors who identify with their clients suffer from objectivity impairment because they agree with their clients more than do other auditors. Related to this, leadership research claims that followers’ identification with a collective makes them susceptible to charismatic leader influence. Based on leadership theory, we anticipate that auditor objectivity may be constrained when leadership is perceived as charismatic, even disregarding the fact that the auditor is not a member of the client’s organization. A cross-sectional design was used and responses from Swedish auditors were analyzed statistically. The findings indicate that perceived charismatic leadership is associated with constrained auditor objectivity.