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Debate is growing around the expansion of risk-based regulation. The regulation scholarship provides evidence of regulatory failure of the risk-based approach in different domains, including financial regulation. Therefore, this paper aims to provide cautionary evidence about the risk of regulatory failure of risk-based strategy in the financial regulation while using enterprise risk management (ERM) as a meta-regulatory toolkit.

Based on interview data gathered from 30 risk managers of banks and five regulatory personnel, combined with secondary data, this study mainly explores the challenges for meaningful use of ERM based self-regulation in regulated banks. The evidence helps to assess the risk of regulatory failure of the risk-based regulation while using ERM.

The evidence reflects that regulated banks face diverse challenges arising from both peripheral and internal environments that limit the true internalization of ERM-based self-regulation. Despite this, the regulator uses this self-regulation as a meta-regulatory toolkit under the risk-based regulation to achieve the regulatory aims. However, the lack of true internalization of ERM based self-regulation is likely to raise the risk of regulatory failure of risk-based regulation to achieve the regulatory goals. Risk-based regulation is an evolving strategy in the regulatory regime. Therefore, care should be taken while using ERM as a regulatory toolkit before relying on it substantially.

The paper provides empirical insights about the challenges for effective use of ERM as a meta regulatory toolkit that might be useful practically both to the regulators and regulated firms.

This paper aims to examine the current practice of reporting on translation issues in qualitative, interdisciplinary accounting research. Based on an analysis of the methodological consideration of the translation of quotations from non-English interviews and additional interviews with experienced researchers, the authors aim to develop recommendations for the reporting on such translation procedures in future accounting research relying on interviews not conducted in English.

The analysis is based on papers published in four highly ranked interdisciplinary accounting journals: Accounting, Auditing and Accountability Journal (AAAJ), Accounting, Organizations and Society (AOS), Critical Perspectives on Accounting (CPA) and Qualitative Research in Accounting and Management (QRAM). The subjects of the analysis are publications of non-English-speaking researchers who conducted non-English interviews and therefore were confronted with translation issues when attempting to get published in these English-language journals. Additionally, to gain deeper insights into reporting decisions on language and translation issues, the authors conducted interviews with experienced researchers in the field of qualitative, interdisciplinary accounting research whose mother tongue is not English. The authors combine these empirical insights with current developments in translation studies.

As suggested by translation studies, translation is an act of sense making and reconstruction of meaning, and therefore is a complex task that needs to be carried out with caution. However, the findings suggest that in current interdisciplinary, qualitative accounting research, the reporting of language and translation issues, especially with regards to the translation of quotations from interview data, have so far received only limited attention. The authors therefore call for more awareness of and sensibility toward dealing with language and translation issues, which should be reflected in more transparent reporting on translation processes to support the credibility and authenticity of qualitative accounting studies based on non-English interviews.

This paper is limited to the reporting on the methodological consideration of translating quotations from non-English interviews in papers published in AAAJ, AOS, CPA and QRAM between 2004 and 2015. For future accounting research that relies on such interviews, the authors call for more transparency and provide specific recommendations. This in turn should strengthen the awareness that language and translation are factors to be considered and reported.

This paper is the first to develop recommendations for the reporting of translation processes in accounting research studies, which are based on interviews not led in the English language.

Unlike quantitative studies, interview data generally cannot be validated; yet, they are typically the only evidence of the research. This study develops protocols for using verbatim interview quotations in research and for assessing the quality of interview quotations.

This research reviews 20 empirical papers using in-depth interviews containing 600 interview quotations to examine authors' approaches to verbatim interviewee quotations. The research analyses the sample papers for interview transcript handling, selection of quotations, the number and length of interview quotations, how they are placed and presented, the proportion of interviewee voices reproduced in quotations and the disclosure of protocols for translating and editing quotations. This paper includes illustrative interview quotations as exemplars of best practice.

Given the modest discussion of the principles influencing the reproduction of quotations in research, this study develops a framework for evaluating prior research. Researchers use a wide variety of practices to reproduce interview quotations in accounting research. The issues derived from this review, and their application to interview-based papers, frame an argument for a general set of quality criteria and protocols rather than rigid rules for assessing qualitative work. These criteria can serve as anchor points for qualitative evaluation.

There is little guidance on the use of interview quotations in qualitative research which this study bridges.

The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on these factors, the authors create a framework for the existing literature on blockchain-based SME financing and lay down future research paths.

The review follows a systematic approach. It includes 53 articles encompassing multiple dimensions of blockchain-based SME finance, including peer-to-peer lending platforms, supply chain finance (SCF), decentralized lending protocols and tokenization of assets. The review critically evaluates these approaches' theoretical underpinnings, empirical evidence and practical implementations.

The review demonstrates that blockchain-based SME finance holds significant promise in addressing the credit gap by leveraging blockchain technology's decentralized and transparent nature. Benefits identified include reduced information asymmetry, improved access to financing, enhanced credit assessment processes and increased financial inclusion. However, the literature acknowledges several challenges and limitations, such as regulatory uncertainties, scalability issues, operational complexities and potential security risks.

The article contributes to the growing knowledge of blockchain-based SME finance by synthesizing and evaluating the existing literature. It also provides a framework for the existing literature in the area and future research paths. The study offers insights for researchers, policymakers and practitioners seeking to understand the potential of blockchain technology in filling the SME credit gap and fostering economic development through improved access to finance for SMEs.

Data represents a critical resource that enables construction companies’ success; thus, its management is very important. The purpose of this study is to assess the benefits of construction data risks management (DRM) in the construction industry (CI).

This study adopted a quantitative method and collected data from various South African construction professionals with the aid of an e-questionnaire. These professionals involve electrical engineers, quantity surveyors, architects and mechanical, as well as civil engineers involved under a firm, or organisation within the province of Gauteng, South Africa. Standard deviation, mean item score, non-parametric Kruskal–Wallis H test and exploratory factor analysis were used to analyse the retrieved data.

The findings revealed that DRM enhances project and company data availability, promotes confidentiality and enhances integrity, which are the primary benefits of DRM that enable the success of project delivery.

The research was carried out only in the province of Gauteng due to COVID-19 travel limitations.

The construction companies will have their data permanently in their possession and no interruption will be seen due to data unavailability, which, in turn, will allow long-term and overall pleasant project outcomes.

This study seeks to address the benefits of DRM in the CI to give additional knowledge on risk management within the built environment to promote success in every project.

The proliferation of mobile phones with integrated sensors makes large scale sensing possible at low cost. During mobile sensing, data mostly contain sensitive information of users such as their real-time location. When such information are not effectively secured, users’ privacy can be violated due to eavesdropping and information disclosure. In this paper, we demonstrated the possibility of unauthorized access to location information of a user during sensing due to the ineffective security mechanisms in most sensing applications. We analyzed 40 apps downloaded from Google Play Store and results showed a 100% success rate in traffic interception and disclosure of sensitive information of users. As a countermeasure, a security scheme which ensures encryption and authentication of sensed data using Advanced Encryption Standard 256-Galois Counter Mode was proposed. End-to-end security of location and motion data from smartphone sensors are ensured using the proposed security scheme. Security analysis of the proposed scheme showed it to be effective in protecting Android based sensor data against eavesdropping, information disclosure and data modification.

The purpose of this paper is to examine what types of task information media scholars need while gathering research data to create new knowledge.

The research design is qualitative and user-oriented. A total of 25 media scholars were interviewed about their research processes and interactions with their research data. The interviews were semi-structured, complemented by critical incident interviews. The analysis focused on the activity of gathering research data. A typology of information (task, domain and task-solving information) guided the analysis of information types related to data gathering, with further analysis focusing only on task information types.

Media scholars needed the following task information types while gathering research data to create new knowledge: (1) information about research data (aboutness of data, characteristics of data, metadata and secondary information about data), (2) information about sources of research data (characteristics of sources, local media landscapes) and (3) information about cases and their contexts (case information, contextual information). All the task information types should be considered when building data services and tools to support media scholars' work.

The paper increases understanding of the concept of task information in the context of gathering research data to create new knowledge and thereby informs the providers of research data services about the task information types that researchers need.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

The disability datasets are the datasets that contain the information of disabled populations. By analyzing these datasets, professionals who work with disabled populations can have a better understanding of the inherent characteristics of the disabled populations, so that working plans and policies, which can effectively help the disabled populations, can be made accordingly.

In this paper, the authors proposed a big data management and analytic approach for disability datasets.

By using a set of data mining algorithms, the proposed approach can provide the following services. The data management scheme in the approach can improve the quality of disability data by estimating miss attribute values and detecting anomaly and low-quality data instances. The data mining scheme in the approach can explore useful patterns which reflect the correlation, association and interactional between the disability data attributes. Experiments based on real-world dataset are conducted at the end to prove the effectiveness of the approach.

The proposed approach can enable data-driven decision-making for professionals who work with disabled populations.

The shift toward a circular economy (CE) represents a collaborative endeavor necessitating the presence of efficient frameworks, conducive contexts and a common comprehension. This research serves as a pivotal stride towards this goal, presenting an exclusive prospect for the investigation and fusion of these frameworks, with particular emphasis on the Quintuple Helix Model (5HM), into a unified theoretical framework that underscores the core principles of the CE. This study is centered on three pivotal questions aimed at decoding the CE transition in specific regional settings.

Adopting an abductive approach firmly anchored in a two-stage qualitative process, this study specifically merges the foundational principles from institutional theory, entrepreneurship literature and CE frameworks to provide insights into the dynamics of circular ecosystems, with a specific focus on the Huelva region in Spain.

The findings demonstrate significant potential in the CE, ranging from the integration of product and service systems to innovations in eco-industrial practices. Yet, a notable deficiency exists: the absence of institutional entrepreneurs, highlighting the essential role that universities can play. As recognized centers of innovation, universities are suggested to be key contributors to the transformation toward a CE, aligning with their societal and economic responsibilities.

This study highlights the importance of managing relationships with entities like SMEs and policymakers or academia for effective CE adoption. Policymakers can refine strategies based on the research’s insights, while the impact of university-driven circular ecosystems on sustainable societies is another crucial area for research.

The sustainability models cited in CE literature may not be comprehensive enough to prevent problem shifting, and it can be argued that they lack a sound theoretical and conceptual basis. Furthermore, the connections between sustainability objectives and the three levels of the CE operating system remain vague. Additionally, there is insufficient information on how regions foster the involvement of the environment in fivefold helix cooperation and how this impacts the CE.