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The Dodd–Frank Act of 2010 exempts small, non-accelerated filers from compliance with Sarbanes–Oxley Act (SOX) Section 404b internal control audits. However, these firms are required to comply with other internal control regulations, namely, SOX Sections 302 and 404a, starting in 2002 and 2007, respectively. A small number of these firms also voluntarily adopted (and sometimes dropped) Section 404b during 2004-2010. The purpose of this study is to investigate the impact of a series of internal control regulations introduced by SOX on the financial reporting quality of small firms.

The research design for this study is empirical. Using unsigned and signed discretionary accruals as measures of financial reporting quality, the authors compare the financial reporting quality for adopters and non-adopters across four regulation regimes over the period 2000-2010: PRESOX, SOX 302, SOX 404a and SOX 404b.

The results indicate that most of the adopters and non-adopters benefited from SOX 302 and 404a compared with the PRESOX period. However, only the non-adopters gained incrementally when moving from SOX 302 to SOX 404a. Also, Section 404b benefited firms with material weaknesses, as well as firms without material weaknesses that had the lowest reporting quality, in the PRESOX period.

This study helps inform the important policy debate on whether to increase the threshold that is used for the SOX 404b exemption. It shows incremental benefits for firms that adopted Section 404b audits, even when they were complying with Section 302 and Section 404a. Consequently, extending the exemption to more companies would result in a loss of the reporting quality benefit of 404b.

This study contributes to the literature by focusing exclusively on non-accelerated filers and by examining differences across four regulation regimes over a long window compared to prior studies. It provides evidence that the financial reporting benefit of SOX 404b is not transitional, but rather extends for a few years even after some firms discontinued the 404b audits.

This study examines the relationship between school ties among external auditors and audit committee members, and their joint impact on audit fee. We also examine how the monitoring and executive functions within companies moderate this relationship.

This study employs a regression analysis model on a sample of companies listed on the Indonesia Stock Exchange from 2016 to 2019, followed by additional analyses using high-low growth and tech samples, as well as robustness tests involving coarsened exact matching (CEM) and Heckman’s (1979) theory to address potential causality issues.

This study reveals that school ties between external auditors and audit committees positively influence audit fee. The audit committee size weakens this relationship, while the presence of an internal audit enhances it.

This research contributes to the literature related to the relationship between school ties and audit fee in Indonesian public companies, providing insights for stakeholders and informing company policies. It aims to increase awareness of the significance of school ties among Indonesian companies.

This research fills a knowledge gap by examining the link between audit committee-external auditor relationships and audit fees, aiming to generate new insights and empirical evidence to inform future research and regulatory decisions.

This chapter examines the world of risk management within fintech. It initiates by emphasizing the crucial role of technology and risk assessment in shaping the fintech landscape. It discusses various risk categories prevalent in fintech operations, elucidating the nuances of technology, operational, compliance, strategic, and reputational risks. A comparative analysis across different fintech sub-sectors unveils their distinct risk profiles. The narrative extends to proactive risk management frameworks, contrasting prominent models like the COSO ERM, FAIR Risk Quantification, and NIST Cybersecurity Frameworks. Integral defense measures are scrutinized, encompassing data encryption, access controls, vulnerability assessments, and incident response plans. This chapter underscores the significance of building operational resilience through robust technology infrastructure, regular system updates, disaster recovery planning, and business continuity measures. Ultimately, this chapter culminates in a comprehensive summary, offering pragmatic recommendations to fortify technology risk management in fintech.

Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.

The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one.

These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India.

The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.

By analyzing and discussing the case, students should be able to identify macro environmental factors that impact business decision-making; apply Michael Porter’s five forces framework; evaluate sources of synergy; understand the concept of disruptive innovation; choose sources of competitive advantage; apply the value proposition canvas; and apply tenets of Blue Ocean strategy.

The grocery retail market in India accounts for nearly 70% ($608bn) of the total retail market ($883bn). The brick-and-mortar multi-tiered distribution network for groceries encompasses a million wholesalers and distributors and 12 million retail outlets. These retail outlets serve as customer touch points where bulk of grocery shopping is done. The online grocery industry is a miniscule $5.5bn. High incomes, change in purchase behaviour, inclination towards speed and convenience on the demand side and alacrity on the supply side have paved the way for new format, quick commerce. Trends and forecasts suggest that quick commerce, a high cash burn business, will grow exponentially. Zomato has jumped onto the quick commerce bandwagon with the acquisition of loss-making Blinkit. The case analyses the quick commerce industry through the lens of Michael Porter’s five forces framework and the Blue Ocean strategy. It elaborates the profitability drivers of the industry and also examines the sources of synergy from the acquisition.

This case is suitable for a class on strategy in postgraduate-level courses. It can be used in a session on entrepreneurship and innovation.

Teaching notes are available for educators only.

CSS 11: Strategy.