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The Overall Equipment Effectiveness (OEE) is considered a standard for measuring equipment productivity in terms of efficiency. Still, Artificial Intelligence solutions are rarely used for analyzing OEE results and identifying corrective actions. Therefore, the approach proposed in this paper aims to provide a new rule-based Machine Learning (ML) framework for OEE enhancement and the selection of improvement actions.

Association Rules (ARs) are used as a rule-based ML method for extracting knowledge from huge data. First, the dominant loss class is identified and traditional methodologies are used with ARs for anomaly classification and prioritization. Once selected priority anomalies, a detailed analysis is conducted to investigate their influence on the OEE loss factors using ARs and Network Analysis (NA). Then, a Deming Cycle is used as a roadmap for applying the proposed methodology, testing and implementing proactive actions by monitoring the OEE variation.

The method proposed in this work has also been tested in an automotive company for framework validation and impact measuring. In particular, results highlighted that the rule-based ML methodology for OEE improvement addressed seven anomalies within a year through appropriate proactive actions: on average, each action has ensured an OEE gain of 5.4%.

The originality is related to the dual application of association rules in two different ways for extracting knowledge from the overall OEE. In particular, the co-occurrences of priority anomalies and their impact on asset Availability, Performance and Quality are investigated.

The current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.

The study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.

The paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.

The authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.

The study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.

The study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

This study aims to try and understand under what cultural conditions entrepreneurship will thrive and prosper, whether under shame or guilt cultures.

The authors use basic game theory to model the conditions under which entrepreneurship will thrive. The authors anticipate that guilt cultures allow for the development of a rules-based culture that allows for the development of impersonal exchange, whereas shame cultures, which are relationship-oriented, focus on strong ties and hence lack the means to expand firms from small and medium family/clan-based businesses.

Empirical results are completed to see whether guilt-dominating cultures are more conducive to having larger firms and whether guilt-dominating cultures have less informality. The authors find support for the latter but lack the right data to test the former.

The authors use a new measure of culture to see how it impacts entrepreneurship.

This paper aims to solve the major assessment problem in matching the satisfaction of psychological gratification and mission accomplishment pertaining to volunteers with the disaster rescue and recovery tasks.

An extended belief rule-based (EBRB) method is applied with the method's input and output parameters classified based on expert knowledge and data from literature. These parameters include volunteer self-satisfaction, experience, peer-recognition, and cooperation. First, the model parameters are set; then, the parameters are optimized through data envelopment analysis (DEA) and differential evolution (DE) algorithm. Finally, a numerical mountain rescue example and comparative analysis between with-DEA and without-DEA are presented to demonstrate the efficiency of the proposed method. The proposed model is suitable for a two-way matching evaluation between rescue tasks and volunteers.

Disasters are unexpected events in which emergency rescue is crucial to human survival. When a disaster occurs, volunteers provide crucial assistance to official rescue teams. This paper finds that decision-makers have a better understanding of two-sided match objects through bilateral feedback over time. With the changing of the matching preference information between rescue tasks and volunteers, the satisfaction of volunteer's psychological gratification and mission accomplishment are also constantly changing. Therefore, considering matching preference information and satisfaction at two-sided match objects simultaneously is necessary to get reasonable target values of matching results for rescue tasks and volunteers.

Based on the authors' novel EBRB method, a matching assessment model is constructed, with two-sided matching of volunteers to rescue tasks. This method will provide matching suggestions in the field of emergency dispatch and contribute to the assessment of emergency plans around the world.

The authors examine how the major board reforms recently implemented by countries around the world affect firms' choice of debt.

Using a quasi-experimental setting of major board reforms around the world that aim to improve board-related governance practices in various areas, this study investigates the impact of effective board monitoring on corporate debt choice. The authors employ difference-in-differences-type quasi-natural experiment method and path analysis for hypotheses testing.

The authors find that the implementation of board reforms is positively associated with firms' preference for public debt financing over bank debt. However, this effect tends to weaken after the fourth year following the implementation of board reforms. In additional analyses, the authors find that “rule-based” reforms have a more pronounced effect on firms' choice of debt than do “comply-or-explain” reforms. Both (1) strengthened firm-level internal governance practices that address concerns about the agency cost of debt and (2) reduced information asymmetries play important roles in facilitating firms' debt choice, but the evidence suggests that the former is the economic mechanism through which country-level reforms affect corporate debt choice.

The study extends the literature examining the heterogeneity of corporate debt choices in a global setting and the literature on the consequences of corporate governance reforms.

The findings demonstrate the effectiveness of the corporate board reforms implemented in countries around the world, addressing concerns from critics about their potential harm or ineffectiveness.

The results indicate that country-level board reforms reduce the extent to which shareholder–creditor conflicts harm shareholders.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.

The purpose of this study is to construct and test a new measure of auditor orientation using two audit quality-related tasks.

The sample consists of 66 Dutch and US graduate auditing students. Participants complete two tasks: one involving a lease classification and another, supplemental experiment involving a contingent liability judgment. The purpose is to construct a new measure for rules-based/ principles-based orientation. Rigorous, psychometric testing confirms that parts of tolerance for ambiguity (TOA) and need for cognition (NFC), together, form a new construct the authors identify as auditor orientation. The authors next conduct a main and supplemental experiment with novice auditor participants from both the USA and the Netherlands.

The authors begin with rigorous, psychometric testing using participants from the USA and the Netherlands. The resulting 10-item scale combines parts of TOA and NFC to reflect auditor orientation. The common themes across scale items are high (low) adaptability to complexity and a substance-over-form (form-over-substance) preference for principles-oriented (PO) (rules-oriented [RO]) auditors. Conducting two experiments, results from two distinct tasks confirm our research question; novice auditors classified as RO (PO) are more (less) likely to recommend a more aggressive/client-favorable disclosure judgment.

Auditor orientation (i.e. rules or principles) has a significant impact on the application of rules-based or principles-based standards. How the standards are applied, therefore, influences auditor decision-making and thus audit quality. However, there is a paucity of auditor orientation research to date, including a validated measure. The study contributes a new measure for future research in the related accounting standards and audit quality literatures, while also identifying a potentially important construct in auditor training.

This paper aims to investigate the association between negative tone in annual report narratives and future performance in the UK context. Under the principle-based approach in the UK, managers tend to bias the tone of narrative reports upward, as the reporting regime is more flexible than the rule-based approach in the USA. Consequently, any negative disclosure not mandated by regulators conveys credible information about a firm’s prospects.

This paper uses a sample of UK FTSE all-share non-financial companies from 2010 to 2019. The authors use the textual-analysis approach based on Loughran and McDonald (2011)’s wordlist (LM) to measure the negative tone in UK annual reports.

The results show a significant negative association between negative tone and future performance. Moreover, our further analyses suggest that only the negativity in the executive section of the annual disclosures correlates significantly with future performance. In summary, this study suggests that negativity does matter under the principle-based approach and can be used as an indicator of future performance.

In contrast to the literature arguing that only positivity has the power to affect a firm’s outcomes under the principle-based approach, the authors provide new empirical evidence suggesting that negativity also matters within the UK context and can be used as an indicator for future performance. Also, to the best of the authors’ knowledge, this is the first study to identify which section of the annual report is more informative about a firm’s future performance.

The purpose of this study is to analyse historical events to argue the improbable prospect of radical accounting reform in corporate financial reporting (CFR) due to the absence of abstract accounting knowledge as part of accountancy professionalisation (AP).

A historical database of CFR and AP events in the UK is categorised and analysed to observe the evolution of accounting in CFR from the perspective of the sociology of professions relating to abstract knowledge in professionalisation.

CFR has always been a statutory function in the UK dependent on arbitrary accounting rules rather than expert measurements based on abstract accounting knowledge. Accounting rules have evolved as part of AP and currently form part of the statutory regulation of CFR. The accountancy profession has eschewed abstract accounting knowledge in a mutually beneficial and uncompetitive relationship with the law profession in CFR.

The study is limited to the history of CFR and AP in the UK and its findings are contrary to the sociology of professions regarding abstract knowledge, consistent with the accountancy profession’s 19th-century experience of court-related services, and indicative of normative accounting research’s redundancy.

Regarding CFR and AP in the UK, the accountancy profession is an expert subordinate branch of the law profession and has no incentive to alter the status quo of statutory accounting rule compliance prevailing over abstract accounting knowledge-based expertise in CFR.

The study questions the optimism of prior research of accounting in CFR that suggests the possibility of radical reform using abstract knowledge.

Cultural heritage archiving is experiencing an increase in digitalisations of artefacts in the last 15 years. The reason behind this trend is a demand for providing information about the artefact in a more accessible way to the audience, for example, through online delivery or virtual reality. Other reasons might be to simplify and automate the management of artefacts. Having a ‘digital copy’ of artefacts, allows one to search an archive and plan its storage and dissemination in a comprehensive manner. With the increased digitalisation comes an increased use of artificial intelligence [AI] applications. AI can be very beneficial in classifying artefacts automatically through machine learning [ML] and natural language processing [NLP]. For example, an algorithm can identify the source and age of artefacts based on an image and can do this much faster for a large collection of photos than a human. Although AI provides many benefits, it also presents challenges: Sophisticated AI techniques require certain insights on how they work, need specialists to customise a solution, and require an existing large dataset to train an algorithm. Another challenge is that typical AI techniques are regarded as black boxes, which means they decide, but it is not obvious why a decision has been made. This chapter describes a project in collaboration with the South Australian Museum [SAM] on the application of AI to extract material lists from a description of artefacts. A large dataset to train an algorithm did not exist, and hence, a customised approach was required. The outcome of the project was the application of NLP in combination with easy-to-customise rules that can be applied by non-IT specialists. The resulting prototype achieved the extraction of materials from a large list of artefacts within seconds and a flexible solution that can be applied on other collections in the future.