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This paper aims to investigate the impact of the implementation of the critical audit matters (CAMs) disclosure requirement and the subsequent relationship between CAM disclosures and audit report lag, as well as audit fees in the USA.

This study used difference-in-differences analyses to investigate the impact that the implementation of the requirement for auditors to report CAMs on their audit report has on the audit process. It also used levels regression models to examine the relationship that CAM disclosures have with audit report lag and audit fees.

This study found that the implementation of the CAM disclosure requirement in the USA reduced audit report lag while not significantly affecting audit fees. This suggests that the CAM disclosure requirement may increase the cooperation between auditors and managers and improve the efficiency of the audit process.

This study’s results are informative for assessing the economic impact of requiring CAM disclosures, which should be of importance to regulators, auditors and accounting researchers.

This study used different approaches to investigate two aspects of the CAM disclosure requirement – the effect of the implementation of the disclosure requirement and the subsequent effects related to CAM reporting outcomes. Unlike many previous studies investigating CAM disclosures, which relied on experiments and questionnaires, this study used actual CAM disclosure data in the USA to investigate the impact on audit report lag and audit fees.

There are limited risk assessment tools validated for use with the internet child abuse material (I/CAM) offender cohort. Developed through a multi-stage process, the purpose of this paper is to present the “Estimated Risk for Internet Child Sexual Offending” (ERICSO), a new tool for I/CAM offender assessment, including demographic, collection, nature of engagement and social domains, plus a structured professional judgement section. Validation studies remain ongoing.

This paper presents a case series analysis of six Australian men, including two Aboriginal men, convicted of I/CAM offences to pilot proposed ERICSO domains and commence validation against the short self-esteem scale, University of California Los Angles loneliness scale, internet sex screening test and the sexual violence risk-20 V2.

Participants of all ages generally reported histories of mental health diagnosis and/or treatment and substance abuse. Two participants reported prior sexual offending, one for I/CAM offences. Participants expressed sexual preferences for female child victims and were convicted of possessing thousands of I/CAM files. Two participants reported accessing I/CAM for over six and 10 years, respectively, before detection by law enforcement.

Preliminary implications indicate ERICSO higher scores are consistent with I/CAM offenders having more online sexual behaviour diversity and more areas of risk/treatment need. For example, participants with problematic self-esteem and loneliness in our data set have higher ERICSO scores. Social connectedness may be a relevant factor though definitive conclusions cannot be drawn from the small sample size.

The ERICSO presents novel assessment of factors in considering treatment targets in addressing both illegal I/CAM and problematic legal sexual behaviours.

This study aims to elaborate on the existing link between maintenance practices and the digital world while also highlighting any unaddressed potential for digital transformation in aircraft maintenance. Additionally, explore how digital technologies contribute to optimizing efficiency within the continuing airworthiness management (CAM) processes.

A literature review was performed to provide a precise review of the authority regulations on CAM processes and existing literature on digital transformation, including artificial intelligence, machine learning, neural network and big data in civil aircraft maintenance and continuing airworthiness processes. This method is used to organize, analyze and structure the body of literature to identify research gaps in the selected scope of the study.

The high position of digital technologies in preventive and predictive maintenance and the need for legislative development for using them in CAM are emphasized. Moreover, it is shown in which area of CAM scientific research has been performed regarding the application of frontier digital technologies. In addition, the gaps between maintenance practices and the digital world, along with the potential scopes of digital transformation which has not been well addressed, are identified. And finally, how digital technologies can effectively increase efficiency in CAM processes is discussed.

To the best of our knowledge, no study comprehensively determined the body of existing knowledge on the aspects of digitalization related to the field of continuing airworthiness management and aircraft maintenance. The results of this study provide a positive contribution to airlines, policymakers, manufacturers and maintenance organizations achieving additional benefits from the implementation of digital technologies in the CAM processes.

This study aims to differentiate crime-related characteristics (such as the number of cases filed against current convictions and criminal history) based on the criminal thinking prevailing among convicts. However, because of the low reliability of subscales and poor structural validity of indigenous and translated versions of international instruments, a new instrument criminal attitude measure (CAM) was extracted to measure criminal thinking patterns among convicts incarcerated in central prisons of Punjab.

A cross-sectional research design was used. Data was collected from 1,949 male convicts (extracting mutually exclusive data from 649 respondents for EFA and 1,300 respondents for confirmatory factor analysis [CFA]). Both data samples were collected from convicts incarcerated in the nine (all) central jails of Punjab, Pakistan.

The results of this study showed poor model fit for both the indigenous criminal thinking scale and the translated version of criminogenic cognition scale. CAM was extracted through principal component analysis and proposed as a 15-item questionnaire with five factors extracted through varimax rotation. Those five factors are power orientation, mollification, entitlement, mistrust toward authorities and short-term orientation. The results of CFA for CAM confirmed the proposed five-factor structure for the construct. Findings based on MANOVA further found that CAM differentiates between the thinking patterns of recidivists, convicts with multiple charges filed against them in current convictions and convicts with a familial criminal record. The findings of this study showed that CAM is a practical, valid and reliable instrument for measuring criminal thinking among convicts.

In this study, using the survey method was inevitable because of the restrictions imposed by the granted permission. However, this time duration was extended because of the courtesy of the Superintendent and Deputy Superintendent of each jail. This study is focused on a male sample only, and the findings cannot be generalized to females. The phenomena proposed (based on large data sets) in this study can further be elaborated using qualitative research designs and methods (using a small sample with an in-depth study). So, it is also suggested to test this new instrument on a comparative study between prisoners and non-prisoners to explore whether scale can differentiate between these two groups.

A short-scale and easy-to-administer instrument was developed for assessing major criminogenic needs among convicts for prison management, i.e. assigning barracks, allocating treatment and also detecting changes in attitude after imprisonment.

To the best of the authors’ knowledge, this study is the first study to explore and validate the construct of criminal attitudes among convicts using both the EFA and CFA. A small and valid instrument facilitates the measurement of criminogenic needs among prisoners. Data was collected from all central jails in Punjab. This study explored comparatively less researched crime characteristics in a relatively large sample.

Different technologies may currently be used to produce dental prostheses, such as additive manufacturing and traditional milling. This study aims to evaluate and improve the fabrication process for hot-pressed porcelain dental prostheses and compare the use of masked stereolithography apparatus (MSLA) casting to computer-aided design/computer-aided manufacturing (CAD/CAM) casting. The cost-benefit analysis of producing dental prostheses through various technologies, including additive manufacturing and traditional milling, has not been fully explored. The cost of materials and processes used to produce prostheses varies based on complexity of design and materials used, and long-term effects, such as durability and wear and tear, must be taken into account.

Using key elements of part costs and estimation cost models, a multivariable approach was used to evaluate the practicality of the recommended strategy and process improvement.

The research found that MSLA casting provides a higher return on investment than CAD/CAM casting, and the optimized production process could be more suitable for the size and annual demand for prostheses.

Overall, this study highlights the need for a more comprehensive understanding of the cost-benefit analysis of different dental prosthesis production methods and emphasises the importance of evaluating long-term effects on the cost-benefit analysis.

Reports from 2016 to 2017 suggest that approximately 870 cases of non-familial child abduction (NFA) are recorded in England and Wales per year. Yet, empirical knowledge of the victims, offenders and offence characteristics is limited in comparison to other forms of child victimisation. Furthermore, much of the available knowledge is constrained by a lack of clarity around the differences between acquaintance and stranger abductors. This systematic literature review aims to develop a comprehensive overview of acquaintance and stranger child abductions, focussing on the similarities and differences in offending behaviours.

Research databases (PsycArticles, Google Scholar, Science Direct, PsycINFO, Criminal Justice Abstracts, MEDLINE and ERIC) and the Grey Literature (ETHOS and EBSCO) were screened for peer-reviewed research published between 1995 and 2021. Sixteen articles met the inclusion criteria and were critically appraised using a modified version of the Joanna Briggs Institute Checklist for Case Reports.

Six key areas within NFA offences and their characteristics were identified as offering potential for differentiating acquaintance and stranger abductors: victim–offender relationship, number of victims and offenders, motives, modus operandi, victim injury, sexual assault and mechanism of death (in fatal cases). The results of this review are discussed with consideration given to investigative implications, limitations and directions for future study.

To the best of the authors’ knowledge, this paper is the first to systematically review the current NFA literature, from which pragmatic recommendations for practice and future academic enquiry are drawn.

The present study aims to examine the relationship between green economic incentives (GEIs) and environmental commitment (EC) as drivers of the circular economy (CE) practices in small and medium enterprises (SMEs).

A cross-sectional study of 594 assistant managers and SME managers was used while data were analyzed through Smart PLS.

The results showed that GEIs positively affect the SMEs' EC and the CE practice model. Furthermore, EC is the mediator between GEIs and the three CE practices: internal environmental management, eco-design and corporate asset management and recovery (CAM&R).

The present study provided a basis for understanding the relevance of SMEs' CE practices and designing a strategic plan for its implementation. Also, it provides insight into that collaboration between triple helix (the government, SMEs and the community) is needed in increasing environmental awareness toward sustainability.

This study enhanced the natural resource-based view (NRBV), describing the mechanisms that view GEIs and EC as pollution prevention in CE practice.

This study aims to identify the intellectual structure of expanded audit reporting (EAR), offers a quantitative summation of prominent themes, contributors and knowledge gaps and provides suggestions for further research.

This research uses various bibliometric techniques, including co-word and co-citation analysis for EAR science mapping, based on 123 papers from Scopus Database between 1991 and 2022.

The results show EAR research is focused on Audit Quality; Auditor Liability and Litigation; Communicative Value and Readability; Audit Fees; and Disclosure. Regarding EAR research, Brasel et al. (2016), article is the most cited paper, Bédard J. is the most cited author, Laval University is the most influential university, The Accounting Review is the most cited journal and USA is the leading country. Furthermore, the results show that in common law countries, in which shareholder rights and litigation risk is high, topics such as disclosure quality and audit litigation have been addressed more; and in civil legal system countries, which usually favor stakeholders’ rights, topics of gender diversity or corporate governance have been more studied.

This research has practical implications for standard setters and regulators, who can identify important, overlooked and emerging issues and consider them in future policies and standards.

This paper contributes to the literature by providing a more objective and comprehensive status of the accounting research on EAR, identifying the gaps in the literature and proposing a direction for future research to continue the discussion on the value-relevance of EAR to achieve more transparency and less audit expectation gap.

Advancements in responsive manufacturing have been supporting companies over the last few decades. However, manufacturers now operate in a context of continuous uncertainty. This research paper explores a mechanism where companies can “elastically” provision and deprovision their production capacity, to enable them in coping with repeated disruptions. Such a mechanism is facilitated by the imitability and substitutability of production resources.

An inductive study was conducted using Gioia methodology for this theory generation research. Respondents from 20 UK manufacturing companies across multiple industrial sectors reflected on their experience during COVID-19. Resource-based view and resource dependence theory were employed to analyse the manufacturers' use of internal and external production resources.

The study identifies elastic responses at four operational levels: production-line, factory, company and supply chain. Elastic responses that imposed variable-costs were particularly well-suited for coping with unforeseen disruptions. Further, the imitability and substitutability of manufacturers helped others produce alternate goods during the crisis.

While uniqueness of production capability helps manufacturers sustain competitive advantage against competitors during stable operations, imitability and substitutability are beneficial during a crisis. Successful manufacturing companies need to combine these two approaches to respond effectively to repeated disruptions in a context of ongoing uncertainties. The theoretical contribution is in characterising responsive manufacturing in terms of resource heterogeneity and resource homogeneity, with elastic resourcing as the underlying mechanism.

Classification of remote sensing images (RSI) is a challenging task in computer vision. Recently, researchers have proposed a variety of creative methods for automatic recognition of RSI, and feature fusion is a research hotspot for its great potential to boost performance. However, RSI has a unique imaging condition and cluttered scenes with complicated backgrounds. This larger difference from nature images has made the previous feature fusion methods present insignificant performance improvements.

This work proposed a two-convolutional neural network (CNN) fusion method named main and branch CNN fusion network (MBC-Net) as an improved solution for classifying RSI. In detail, the MBC-Net employs an EfficientNet-B3 as its main CNN stream and an EfficientNet-B0 as a branch, named MC-B3 and BC-B0, respectively. In particular, MBC-Net includes a long-range derivation (LRD) module, which is specially designed to learn the dependence of different features. Meanwhile, MBC-Net also uses some unique ideas to tackle the problems coming from the two-CNN fusion and the inherent nature of RSI.

Extensive experiments on three RSI sets prove that MBC-Net outperforms the other 38 state-of-the-art (STOA) methods published from 2020 to 2023, with a noticeable increase in overall accuracy (OA) values. MBC-Net not only presents a 0.7% increased OA value on the most confusing NWPU set but also has 62% fewer parameters compared to the leading approach that ranks first in the literature.

MBC-Net is a more effective and efficient feature fusion approach compared to other STOA methods in the literature. Given the visualizations of grad class activation mapping (Grad-CAM), it reveals that MBC-Net can learn the long-range dependence of features that a single CNN cannot. Based on the tendency stochastic neighbor embedding (t-SNE) results, it demonstrates that the feature representation of MBC-Net is more effective than other methods. In addition, the ablation tests indicate that MBC-Net is effective and efficient for fusing features from two CNNs.