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Monitoring of the quality of precast concrete (PC) components is crucial for the success of prefabricated construction projects. Currently, quality monitoring of PC components during the construction phase is predominantly done manually, resulting in low efficiency and hindering the progress of intelligent construction. This paper presents an intelligent inspection method for assessing the appearance quality of PC components, utilizing an enhanced you look only once (YOLO) model and multi-source data. The aim of this research is to achieve automated management of the appearance quality of precast components in the prefabricated construction process through digital means.

The paper begins by establishing an improved YOLO model and an image dataset for evaluating appearance quality. Through object detection in the images, a preliminary and efficient assessment of the precast components' appearance quality is achieved. Moreover, the detection results are mapped onto the point cloud for high-precision quality inspection. In the case of precast components with quality defects, precise quality inspection is conducted by combining the three-dimensional model data obtained from forward design conversion with the captured point cloud data through registration. Additionally, the paper proposes a framework for an automated inspection platform dedicated to assessing appearance quality in prefabricated buildings, encompassing the platform's hardware network.

The improved YOLO model achieved a best mean average precision of 85.02% on the VOC2007 dataset, surpassing the performance of most similar models. After targeted training, the model exhibits excellent recognition capabilities for the four common appearance quality defects. When mapped onto the point cloud, the accuracy of quality inspection based on point cloud data and forward design is within 0.1 mm. The appearance quality inspection platform enables feedback and optimization of quality issues.

The proposed method in this study enables high-precision, visualized and automated detection of the appearance quality of PC components. It effectively meets the demand for quality inspection of precast components on construction sites of prefabricated buildings, providing technological support for the development of intelligent construction. The design of the appearance quality inspection platform's logic and framework facilitates the integration of the method, laying the foundation for efficient quality management in the future.

Quality 4.0 is essential to the Industry 4.0 framework, notably in the electronics sector. It evaluates product quality in real-time using automatic process controls, quality tools and procedures. The implementation of Quality 4.0 criteria in the electronics industry is the subject of this study’s investigation and analysis. In this study, nine Customer Requirements (CRs) and 18 Design Requirements (DRs) have been defined to adopt Quality 4.0, aiming to increase yield while reducing defects. This study has developed a Quality 4.0 framework for effective implementation, incorporating the People, Process and Technology categories.

Many CRs and DRs of Quality 4.0 exhibit interdependencies. The Analytic Network Process (ANP) considers interdependencies among the criteria at various levels. Quality Function Deployment (QFD) can capture the customer’s voice, which is particularly important in Quality 4.0. Therefore, in this research, we use an integrated ANP-QFD methodology for prioritizing DRs based on the customers' needs and preferences, ultimately leading to better product and service development.

According to the research findings, the most critical consumer criteria for Quality 4.0 in the electronics sector are automatic systems, connectivity, compliance and leadership. The Intelligent Internet of Things (IIOTs) has emerged as the most significant design requirement that enables effective control in production. It is observed that robotics process automation and a workforce aligned with Quality 4.0 also play crucial roles.

Existing literature does not include studies on identifying CRs and DRs for implementing Quality 4.0 in the electronics industry. To address this gap, we propose a framework to integrate real-time quality measures into the Industry 4.0 context, thereby facilitating the implementation of Quality 4.0 in the electronics industry. This study can provide valuable insights for industry practitioners to implement Quality 4.0 effectively in their organizations.

The purpose of this study is to assess the implications of auditor–client geographic proximity on audit fees, audit report lag and audit quality in the context of an emerging economy, Bangladesh.

The auditor–client proximity is gauged in kilometers and travel time, consistent with prior research to assess its association with audit fees, audit report lag and audit quality. Analyzing a data set of 469 firm-year observations from 2018 to 2021 through panel regression, the results are then interpreted in accordance with cluster theory and transaction cost theory.

The findings affirm a significant positive association of auditor–client proximity with audit fees and audit report lag. Distant auditors charge lower fees and maintain the timeliness of audit reports to capture and retain distant clients. In addition, the study uncovers a negative association between proximity and audit quality. Geographic proximity can create a familiarity threat between the management team of the client and the local auditor, which can decrease audit quality. These associations are more pronounced in low-risk clients than the high-risk ones.

These findings underscore the intricate interplay between geographic proximity, communication hurdles and their effects on diverse facets of the audit process that both auditor and client should consider in future audit engagement.

This research criticizes the existing literature linking auditor–client proximity with audit quality, fees and report lags and provides novel insight from an emerging economy context.

We live in a data-driven world where success relies on the accuracy of information and data quality assurance (QA). This chapter delves into the intricacies of establishing a robust data QA framework, empowering you to navigate the ever-evolving data landscape with confidence. This chapter starts with a discussion on consequences of poor data quality and then explains what is data quality and how defining data standards, mapping of data, implementation of validation and error checking, conducting audits, and cleansing and leveraging visualization tools can help one to construct data quality within his managerial ecosystems. This chapter underscores cultivation of a data-driven culture through stakeholder involvements, training, continuous improvements, and emphasizing ownership and responsibility. In the end, this chapter provides the reader with an outlook for the future of data QA discussing emerging technologies like artificial intelligence (AI)-powered data cleansing and blockchain-based security. This chapter will help the readers in ensuring data quality and unlock the door to a future of informed decisions, exceptional customer experiences, and lasting competitive advantage.

The need to optimize the triangle formed by “quality, cost and time” culminated in increasing the focus from product to process quality. By analyzing the evolution of quality and the impact of Industry 4.0 on it, this research seeks, through a technical point of view, to comprehend the state of the art of quality 4.0 and intelligent quality management (IQM) by defining concepts, technologies, challenges and applications.

The review was conducted only in English, on IEEE Xplore, Scopus, Engineering Village and Web of Science databases with a backward citation analysis, having technology and quality as main concepts. In total, 109 papers were reduced to 24, and 11 characteristics were extracted.

Although many authors point to the same 4.0 technologies and the importance of quality for Industry 4.0, they differ in the concept of quality 4.0 and the implementation frameworks to achieve it.

This paper is one of the few studies that have searched for the roots of quality 4.0 and IQM. The work also seeks to identify their differences and their relationship with Industry 4.0.

The purpose of this study is to assess the causes of construction projects’ contract terminations, and the impacts and potential mitigation strategies in the context of Ethiopian construction industry.

In this study a mixed method comprising a questionnaire survey and case study were employed. The case study examined 24 public construction projects that have experienced contract termination, where some of the projects had terminated multiple times. In analyzing the data, while descriptive statistics was employed for the quantitative data, thematic analysis was used in analyzing the qualitative data.

The identified client-related major causes are inadequate funding, improper budgeting and payment delays to the contractor. Contractor related major causes are weak contractor capacity, low profit margin, poor cash flow management and ineffective risk and claim management. The external major causes are political and economic instabilities, regulatory challenges and force majeure events. The major impacts of termination on the client are delay of the public service, quality problem and financial damage, whereas the identified impacts on the contractor are financial losses and reputation damage. The potential mitigation strategies are effective contract management, risk management and collaborative project management.

Public construction projects’ contract termination is among the persisting problems of construction industry; and the case is worse in developing countries with multiple causes and consequences. Hence, findings of this study are valuable inputs to the stakeholders to enhance the practice in reducing the likely occurrence and impact of contract terminations. Also, the study indicates the interdependent nature of the causes and the impacts.

This research centres on how digital technologies are revolutionizing agriculture, affording farmers improved access to information, crop forecasts, markets and innovations, in addition to facilitating training and other benefits. The purpose of this investigation is to examine how technologies used in the Agro 4.0 industry facilitate agricultural and livestock practices.

A thorough examination of the existing literature on this subject was conducted, encompassing articles published between 2013 and 2023 that have been catalogued in Scopus and the Web of Science.

The analysis of these studies reveals the growing significance of innovations such as artificial intelligence, blockchain, precision agriculture, the Internet of Things (IoT) and robotics in the transformation of agriculture and livestock farming. The implementation of these technologies is occurring across various sectors of agricultural production, including livestock production, shrimp farming, vertical farming, supply chains, irrigation, grain inspection, the dairy sector and smart farms. The impacts identified include improvements in productivity, intelligent analysis systems, operational efficiency, transparency and reliability, management per square metre, optimization, environmental sustainability, animal welfare, enhancement of food security and risk reduction.

Therefore, the contributions of technologies are associated with data-based decision-making, digital skills to maximize agribusiness performance, digital transformation in the field and competitiveness in the global market.

The purpose of this study is to examine the effect of audit committee (AC) tenure on corporate governance, a topic that has been long debated. Social capital theory explains how directors’ effectiveness varies through tenure. Consistent with this theory, this paper argues that AC tenure has an inverted U-shaped relationship with AC governance.

This paper estimates a quadratic function that regresses constructs for AC governance on the average AC, the AC chair, and nonchair tenure, and their respective square terms. The constructs for AC governance include financial reporting quality measures and perceived auditor independence measures.

This paper finds that average AC, AC chair, and nonchair tenure have inverted U-shaped relationships with financial reporting quality, consistent with social capital theory. This paper also finds similar associations when examining perceived auditor independence. The results are generally consistent with AC directors accumulating knowledge and social capital, which improves AC governance to an optimal level, following which entrenchment and familiarity occur and AC governance declines.

To the best of the authors’ knowledge, this is the first study in AC governance literature to show a nonlinear relationship between AC tenure and AC governance. This paper extends Huang and Hilary (2018) by demonstrating that a nonlinear effect is also present in the AC, a key board committee responsible for monitoring financial reporting quality and appointing auditors and approving their services. This paper further documents that the AC subsumes the effect of the overall board in some areas of AC oversight, and reconciles the inconclusive findings of prior research by showing a nonlinear relationship between AC tenure and AC governance.

Industry 4.0 is based on cyber-physical systems (CPSs); therefore, the risks of cyberattacks tend to increase. A cyberattack is any malicious act that could jeopardize the security of production, accounting and financial systems. This review hypothesizes quality management is in a position to play an important role in preventing and detecting hacker attacks.

To verify this hypothesis, Lakato’s research program was utilized in a bibliometric review. This review was separated into two stages: the first was a descriptive analysis and the second was a scientific mapping, done using a technique known as text mining. This technique was guided by factorial and cluster analysis.

The analysis revealed that this subject is emerging and relevant. It was observed that understanding the vulnerabilities of connected systems is crucial for combating cyberattacks. Furthermore, the study highlighted the potential of multivariate control charts, such as Hotelling’s T-squared charts and Exponentially Weighted Moving Average (EWMA) control charts, in efficiently detecting anomalies in connected systems.

This article introduces an approach integrating quality management, statistical analysis and cybersecurity to safeguard CPSs. By leveraging advanced statistical techniques, such as Hotelling T² charts and EWMA charts, in conjunction with the foundational principles of quality management systems (QMS), such as continuous improvement and process standardization, this could be a potentially robust defense against cyberattacks. The integration of these tools allows for the early detection of anomalies and potential cyber threats, ensuring the prevention of integrity and security of interconnected industrial processes.

Social media data contains a wealth of content related to customers’ reactions to, and comments on, firms’ performance. Through the lens of signaling theory, this paper aims to investigate the use of social media data as a knowledge resource in communicating firms’ noncompliance risk to regulatory agencies.

This paper proposes a two-step social media analytics framework to detect noncompliant firms. First, it creates a context-specific dictionary that contains keywords relevant to firms’ noncompliant behaviors. Next, it extracts those keywords from customer reviews, customer sentiment and emotions to predict firm noncompliance. It tests these ideas in the context of food safety regulations.

It identified over 100 words that are related to restaurants’ hygiene deficiencies. Using the occurrence of these words in customer reviews, as well as sentiments and emotions expressed within them, the author’s best-performing model can identify nearly 90% of the restaurants that severely violated regulations.

After being processed by appropriate machine learning algorithms, customer reviews serve as valuable knowledge resources, enabling regulatory agencies to identify noncompliant firms. Regulatory agencies can use this model to complement the current compliance monitoring scheme.

This research contributes a novel methodology for creating a context-specific dictionary that keeps only the relevant words customers use when discussing firms’ noncompliant acts. In the absence of such an approach, numerous irrelevant signals would be included in the modeling process, thereby increasing the cost of social media analytics.