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Know your customer (KYC), accounting standards, issuance, clearing, and trade settlement became the major barrier to implement accounting, accountability and assurance process in supply chain finance (SCF). Blockchain technology features have the potential to solve accounting problems. This research focuses on exploring how blockchain technology provides solutions to overcome the barriers of accounting process in SCF. The benefits, opportunities, costs and risks related to blockchain adoption are also explored.

Multi-case study and qualitative methods are used with a framework based on blockchain role to overcome the accounting process barriers. Ten blockchain projects in SCF and 29 interviews of participants as a unit of analysis are considered.

The findings indicate that blockchain technology offers solutions to solve accounting, accountability and assurance problems in SCF. Validity, verification, smart contracts, automation and enduring data on trade transactions potentially solve those barriers. However, it is also necessary to consider costs such as implementation, technology, education and integration costs. Then there are possible risks such as regulatory compliance, operational, code development and scalability risk. This finding reflects the current status of blockchain technology roles in SCF.

This study unveils blockchain's SCF accounting potential, emphasizing multi-case method limitations and future research prospects. Diverse contexts challenge findings' applicability, warranting cross-industry studies for deeper insights. Addressing selection bias and integrating quantitative measures can enhance understanding of blockchain's accounting impact.

Accounting professionals can get an idea of the future direction and impact of blockchain technology on accounting, accountability and assurance processes.

This study provides initial findings on the potential, costs and risks of blockchain that is beneficial for parties involved in SCF, especially for banks and insurance underwriters. In addition, the findings also provide direction for the contribution of blockchain technology to accounting theory in the future.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

Qualitative research that involves the use of human participants calls for the need to protect those participants to give their honest view during data collection. This is an important part of every primary data collection in qualitative studies using interviews. This paper aims to investigate all available ethical considerations that need to be observed by the researcher when conducting primary data collection through interview and to explore the theories that underpin the ethics in qualitative studies.

This paper systemically reviewed existing qualitative data on ethics and gathered information that were analysed and presented on the topic area.

The findings show that ethical considerations deal with the various approaches adopted by the researcher to make the participants feel safe to participate in any given researcher. During an interview process in qualitative research, the findings show that anonymity, voluntary participation, privacy, confidentiality, option to opt out and avoiding misuse of findings are ethical considerations that must be observed by the researcher. The outcome of the investigation also shows that deontology and utilitarianism, rights and virtue are the main theories that underpin ethical considerations in research.

The rights of the research participants need to be respected in qualitative research to assist in gathering accurate information to achieve the objectives of study. This and other ethical principles such as anonymity, privacy, confidentiality, voluntary participation and option to opt out guide the researcher to systematically adhere to data collection approaches that yield valid results in qualitative data collection using interviews.

This study aims to investigate records management practices in Malawian private universities with a focus on University of Livingstonia.

The study used embedded case study design using a mixed methods approach. A survey questionnaire, semi-structured interview guide and a document review were used to collect data from University of Livingstonia staff.

The study found that University of Livingstonia created records such as minutes, grades, mails, theses, policy documents and reports. Some of the challenges related to records management at the institution were uncoordinated records management practices due to, among others, lack of records management policy, retention and disposal schedules among others. It further reported a number of factors frustrating the success of records management practices at UNILIA such as lack of management support, poor funding and lack of information communication technology infrastructure. Nevertheless, the study acknowledged the role of records management at University of Livingstonia.

Previous studies on records management in Malawi focused on public sector. However, this is a novel study undertaken in the private higher education. Therefore, it forms a basis for conceptualising records management phenomena in the private higher education in Malawi.

This paper seeks to investigate the factors influencing auditors' behavioural intentions towards blockchain technology (BT) adoption in Iraqi government banks. It also highlights the relationships between these factors to determine if the proposed model can provide a more comprehensive means of comprehending how auditors in government banks have adopted BT.

The study uses the unified theory of acceptance and use of technology and expands it by incorporating five external constructs: “system trust”, “cost”, “transparency”, “security” and “auditor's skill.” This study employed a quantitative and exploratory methodology through the gathering and examination of data from 300 auditors. For the evaluation of the measurement and structural models, the authors adopted the partial least squares structural equation modelling approach with SmartPLS v4.

The findings demonstrate that “auditor's skill and four government features of BT adoption” are major factors in government bank auditors' adoption of BT. Additionally, the findings indicate that social influence is a potent indicator of one's intention to adopt BT in the banking industry.

One limit of this study is the selection of governmental perspective. This study is limited to auditors' opinions, who work at the government banks. Further studies may consider other perspectives in order to provide an in-depth analysis of blockchain.

This paper offers valuable insights into the factors influencing the adoption of blockchain technology in Iraqi governmental banks. It provides empirical evidence supporting auditing units and internal auditors in enhancing their job performance through the adoption of such technology.

This study contributes to the existing literature on technology adoption within the audit profession, specifically examining the use of blockchain technology. By exploring the features of technology adoption within government institutions in the auditing field, it introduces a new perspective, emphasizing the importance of auditor skills.

The purpose of this study is to identify the possible efficiency gains in using tokenization for the execution of public expenditure on governmental investments.

Through design science research methodology, the exploratory research produced a tokenized prototype in the blockchain, through the Ernst and Young OpsChain traceability solution, allowing automated processes in the stages of public expense. A focus group composed of auditors from the public sector evaluated the possibility of improving the quality of information available in the audited entities, where the tokens created represent and register the actions of public agents in the blockchain Polygon.

The consensus of the experts in the focus group indicated that the use of tokenization could improve the quality of the information, since the possibility of recording the activities of public agents in the metadata of the tokens at each stage of the execution of the expenditure allows the audited entities the advantages of the information recorded on the blockchain, according to the following ranking: first the immutability of audited data, followed by reliability, transparency, accessibility and efficiency of data structures.

This research makes an empirical contribution to the real use of tokenization in blockchain technology to the public sector through a value chain in which tokens were created and moved between the wallets of public agents to represent, register and track the operations regarding public expense execution.

This study aims to describe key elements that are critical to virtual qualitative research especially while working with practitioners as participants.

This paper takes a reflexive researcher approach using a case study to explore how researchers adopted a qualitative research approach using digital technology. We use five principles suggested by Boland et al. (2022) as a framework to consider and reflect on our experiences as researchers and those of our participants.

We highlight the gatekeeper’s support, trusted relationship with the organisations, interpersonal skills of interviewers, stringent measures of securing data and shared experiences of interviewee and interviewers helped complete virtual research. We recommend that four key factors such as digital competency, feasibility, flexibility and resilience should be considered while undertaking or commissioning virtual, qualitative research studies.

Social care practitioners and qualitative researchers increasingly negotiate with digital technologies to undertake their work. In this paper, we evidence how online qualitative approaches can be effective provided challenges are dealt with diligently in each stage of the research process.

This study examines the cognitive factors of adopting blockchain technology in various supply chain scenarios and its role in reframing the distinctive values of supply chain financing. Based on expectancy theory, this study explores the different profiles underlying the components of expectancy, valence and instrumentality.

This is a multiple-case study of four Fintech companies using blockchain technology to promote the performance of supply chain operations and financing.

The results show that blockchain-enabled supply chain finance (BSCF) can be classified into four scenarios based on the scope and purpose of blockchain technology applications. The success of BSCF depends on the profiles of BSCF expectancy (the recognized purpose and scope of BSCF), instrumentality (identified blockchain attributes and other technology combinations) and valence (the perceived distinctive value of BSCF). Blockchain attributes help solve information asymmetry problems and enhance financing performance in two ways: one is supporting transparency, traceability and verification of transmissions and the other entails facilitating a transformation to new business models.

This research applies a new perspective based on expectancy theory to study how cognitive factors affect Fintech companies' blockchain solutions under a given supply chain operation or financing activity. It explains the behavioral antecedents for applying blockchain technology, the situations appropriate for the different roles of blockchain technology and the profiles for realizing the value of blockchain technology.

Construction activities conducted in urban areas are often a source of significant noise disturbances, which cause psychological and health issues for residents as well as long-term auditory impairments for construction workers. The limited effectiveness of passive noise control measures due to the close proximity of the construction site to surrounding neighborhoods often results in complaints and eventually lawsuits. These can then lead to delays and cost overruns for the construction projects.

The paper proposes a novel approach to integrating construction noise as an additional dimension into scheduling construction works. To achieve this, a building information model, including the three-dimensional construction site layout object geometry, resource allocation and schedule information, is utilized. The developed method explores further project data that are typically available, such as the assigned equipment to a task, its precise location, and the estimated duration of noisy tasks. This results in a noise prediction model by using noise mapping techniques and suggesting less noisy alternative ways of construction. Finally, noise data obtained from sensors in a case study contribute real values for validating the proposed approach, which can be used later to suggest solutions for noise mitigation.

The results of this study indicate that the proposed approach can accurately predict construction noise given a few available parameters from digital project planning and sensors installed on a construction site. Proactively integrating construction noise control measures into the planning process has benefits for both residents and construction managers, as it reduces construction noise-related disturbances, prevents unexpected legal issues and ensures the health and well-being of the workforce.

While previous research has concentrated on real-time data collection using sensors, a more effective solution would also involve addressing and mitigating construction noise during the pre-construction work planning phase.