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This paper addresses the role of principles-based accounting standards as a potential mechanism for reducing firms' time delay of annual reporting disclosure while improving the timeliness of accounting information. The paper also contributes to the existing literature by addressing the mediating effects of the financial reporting complexity and the audit workload on the link between principles-based accounting standards and the time delay of annual reporting disclosure.

The focus is placed on an unbalanced panel of 20,943 samples over the period of 2007–2017.

The results show that the more principles-based the accounting standards are, the lower the time delay of annual reporting disclosure is, and the timelier the disclosure of accounting information is. The relationship between the two is more significant especially in the first two months after the end of the fiscal year. The findings are all robust after controlling for a series of sensitivity checks and endogenous concerns. From the mediating effect results, the authors find that principles-based accounting standards decrease the financial reporting complexity and the audit workload which in turn can help lower time delay of annual reporting disclosure. In addition, the negative effect of principles-based accounting standards on the time delay of annual reporting disclosure is more significant in the case that the company has “good news” including with no losses and receiving the standard auditing opinions. The results confirm the law of “good news announces early, bad news announces late.” Furthermore, the moderating effect results show that the higher the economic policy uncertainty index and the legal environment index, the lower the benefit of principles-based accounting standards to the timeliness of annual reports. The results of the economic consequences of timeliness suggest that the timely disclosure of accounting reporting will bring greater market reaction and contain more information, and the information of companies that disclose annual reports timely are more transparent.

This paper studies the impact of accounting standards on the timeliness of annual report disclosure, which enriches the literature in the field of macro policies and micro-enterprise behaviors.

The primary objective of this research is to investigate the impact of task‐technology fit on users’ performance when using artificial intelligence systems for auditing tasks. Four artificial intelligence auditing systems, two problem‐solving programs, and four questionnaires were developed. A laboratory experiment was performed with 292 undergraduate auditing students. The results suggested that the effect of task‐technology fit on accuracy in solving problems was marginal for case‐based reasoning with unstructured tasks. No significant effect was found on problem‐solving accuracy for rule‐based reasoning with structured tasks. The task‐technology fit, however, marginally increased users’ certainty of the correctness of their solutions.

The examination of revisit intentions in hospitality is integral to relationship marketing and customer loyalty. Its measurement and determination have largely been done through closed-ended measures in surveys of customers. However, vast troves of consumer-generated media in the form of open-ended text reviews can also serve as sources for the determination of revisit intentions. The purpose of this paper is to develop and test a rule-based classification model from big data to extract revisit intentions.

Data for this came from 116,241 reviews scraped from Tripadvisor.com using a stratified sampling technique comprising hotels in major cities in the USA. A sample comprising 1,800 reviews was randomly drawn from this larger pool of reviews and manually annotated. A manual-set rule-based model, supervised machine learning (ML) models and hybrid models were developed to extract revisit intention.

The hybrid model of the MSRB method complemented by the gradient boosting ML method performed the best to classify revisit intentions in reviews.

This study’s rule-based classification model can be used by hotels to evaluate revisit intentions from the ever-growing pool of consumer-generated reviews. This can enable hotels to identify drivers of re-patronage and enhance relationship marketing initiatives.

This study is the first to propose an analytical model that taps big data to extracting revisit intentions. In the past, revisit intentions have been assessed using closed-ended questions using traditional survey-based methods.

The purpose of this research is to show how institutional factors affect buyer–supplier relationships. Specifically, the authors examine a model of relationship quality and its antecedents across rule-based, relation-based and family-based governance environments.

A conceptual model and accompanying research hypotheses are tested on data from a survey of 169 US (rule-based), 110 Brazilian (family-based) and 100 Chinese (relation-based) managers and buyers. Structural equation modeling is used to test the relationship quality framework and the hypothesized moderation of governance environment.

Results suggest that the informal institutions which shape a nation’s governance environment impact the relationship building process between buyers and suppliers. Communication quality was found to influence relationship quality more in developed economies where relationships are protected and managed under rule-based governance. Interaction frequency was found to be more relevant in emerging market firms characterized by relation-based societies. relationship benefits are applied more to relationships in emerging markets operating under family-based governance. No differences were found across governance environments for the influence that conflict resolution has on relationship quality.

Results provide insight into how the fairness and effectiveness of political and economic institutions surrounding a buyer’s nation of operation impact “rules of the game” differently for developed and emerging market firms.

This study extends research on cross-cultural relationship marketing to more than just communications context and cultural heritage. Results demonstrate that a buyer’s quest for legitimacy impacts its sensitivity to what supplier behaviors matter the most.

Expert systems are being effectively applied to a variety of engineering problems. A growing number of languages and development tools are available for their building. Expert systems building tools (shells) are not so flexible as the high‐level languages, but they are easier to use. The problem is that there are too many development tools on the market today, no standards for their evaluation are available, so it is quite difficult to choose the ‘best’ tool for the developer's/user's needs. This paper is an attempt to review the situation on the confused market. Eighty‐six development tools are described in a table form for easy comparisons. Tools implemented on the AI machines only are not included in this survey.

The accurate specification of user’s real requirements from manufacturing planning and control (MPC) systems is carried out very poorly throughout the industry. This paper describes a rule‐based system for the specification of MPC system activities. The rule‐base provides detailed recommendations on the suitability of system activities to individual companies based on company characteristics and management concerns. It also provides supporting reasons for the recommendations. This paper describes the development of the rule‐base from the initial conceptual framework to the fully tested and validated PC‐based application. The rule‐base has been fully tested and validated in ten small to medium sized enterprises (SMEs) engaged in batch manufacturing. This paper describes the experiences of two of these case studies by way of example.

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.

According to the SEC, the proposed roadmap for adopting principles-based International Financial Reporting Standards (IFRS) is still a priority. The adoption of IFRS will ultimately demand greater emphasis on practitioner judgment (Mintz, 2010). This chapter focuses on the need for building the judgment skills of the practitioner. Our methodology follows a three-step process. We start with accounting standards, reviewing similarities and differences between “rules-based” and “principles-based” standards and conclude that, while applying any standard requires judgment, applying principles-based standards requires more judgment. We then focus on preparer incentives that can influence this requisite judgment. We use the “fraud triangle” to analyze the influence of incentives on judgment under each standards setting approach. Our third and most important step involves equipping practitioners to make judgments in the presence of incentives. We present and discuss a model that considers economic, social (legal), and ethical dimensions for making principled judgments in the presence of incentives and advocate-improved education for accountants in implementing that model.

This chapter explores the link between virtue and representational faithfulness in making judgments in a principles-based environment. The motivation for the chapter is the impending adoption of International Financial Reporting Standards (IFRS) in the United States and its principles-based approach to accounting. Even in a rules-based system, there are principles that provide a foundation for making decisions about the selection and implementation of accounting standards, financial statement presentation, estimates, and the sufficiency of evidence. A model is presented that reflects these judgments informed by virtue considerations that support substance over form decisions and a true and fair view. Implications for accounting education are discussed including the readiness of faculty to incorporate IFRS into the curriculum.