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.

Purpose: 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. Design/methodology/approach: 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. Findings: 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. Practical implications: This study offers practical solutions to ESG rating problems that have implications for investors, ESG raters and socially responsible investments. Social implications: 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. Originality/value: 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. 

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.

We use machine learning with a cross-sectional research design to predict governance controversies and to develop a measure of the governance component of the environmental, social, governance (ESG) metrics. Based on comprehensive governance data from 2,517 companies over a period of 10 years and investigating nine machine-learning algorithms, we find that governance controversies can be predicted with high predictive performance. Our proposed governance rating methodology has two unique advantages compared with traditional ESG ratings: it rates companies' compliance with governance responsibilities and it has predictive validity. Our study demonstrates a solution to what is likely the greatest challenge for the finance industry today: how to assess a company's sustainability with validity and accuracy. Prior to this study, the ESG rating industry and the literature have not provided evidence that widely adopted governance ratings are valid. This study describes the only methodology for developing governance performance ratings based on companies' compliance with governance responsibilities and for which there is evidence of predictive validity.

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 empirically examines whether an auditor's perceived ability to negotiate discretionary accounting issues with clients (auditor negotiation self-efficacy) is related to auditor objectivity, and whether an auditor's negotiation self-efficacy has a greater impact on her objectivity when the auditor's accuracy motive (professional identity) is strong rather than weak. We tested the hypotheses using a cross-sectional survey design and obtained 146 responses from among 800 surveyed experienced Swedish auditors. The findings indicate that auditors with higher negotiation self-efficacy were more likely to make decisions on a material and discretionary accounting issue contrary to their clients’ desires compared to auditors with lower self-efficacy. The relationship between negotiation self-efficacy and auditor objectivity was not moderated by professional-identity strength. These research findings suggest that recruiting and training auditors to increase their negotiation self-efficacy may be an effective method to enhance auditor objectivity without the problems inherent in other methods, such as auditor rotation. Our sample was obtained in Sweden, which allows long auditor tenures. We caution that, although our analysis controlled for auditor tenure, the effect of auditor negotiation self-efficacy may not be generalizable to countries that limit tenure through regulation.

Purpose: The purpose of this paper is to investigate the connection between the type of negotiation tactics auditors use when they ask their clients to make adjustments to their financial reports, focusing on three distributive and two integrative negotiation tactics, and whether the auditors identify with their clients.

Design/methodology/approach: A survey was used to capture 152 experienced Swedish audit partners’ perspectives on what type of negotiation technique they would use thinking about their largest client in a hypothetical situation.

Findings: The results show that the more auditors identify with their clients, the more likely they are to adopt two of the distributive negotiation tactics, conceding and compromising.

Originality/value: Building on the findings in the accounting literature that auditors’ identification with clients constrains their judgments, this study finds that auditors’ identification with clients also has an impact on the auditors’ initial selection of negotiation tactics. 

The purpose of this paper is to investigate whether transformational leadership affects auditor objectivity. Design/methodology/approach The investigation is based on a field survey of 198 practicing auditors employed by audit firms operating in Sweden. Findings This study finds that transformational client leadership negatively affects auditor objectivity and that the effect is only partially mediated by client identification. Given these results, suggesting that auditors are susceptible to influence by their clients? perceived exercise of transformational leadership, leadership theory appears relevant to the discussion of auditor objectivity in the accounting literature. Originality/value Previous accounting research has applied the social identity theory framework and found that client identification impairs auditor objectivity. However, the effect of transformational client leadership on auditor objectivity, which reflects an intense auditor-client relationship, has been neglected before this study.