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This study aims to investigate the quality of disclosure of a cutting-edge reporting tool – integrated reporting (<IR>) – in terms of its effectiveness to report on COVID-19 pandemic information, its ability to provide forward-looking information and risk impact implications, and its quality determinants in challenging times.

Thanks to a content analysis of 247 <IR> for FY20, an integrated reporting disclosure score was developed to assess the disclosure quality provided by the sampled companies. Three research questions were tested through logistic regressions.

Non-financial disclosure activities struggle to provide adequate information in terms of potential future scenarios, risk assessment and forward-looking analyses. However, companies incorporated in “Anglo-Saxon” territories drafted integrated reports of higher quality. More recently, incorporated companies have made a greater effort to measure and report COVID-19 pandemic impacts on environmental, social and governance and business activities, also increasing their risk assessment and mitigation efforts. Concerning the determinants of disclosure quality, leverage, corporate governance structures, country of incorporation and belonging to “high impact” industries all lead to a higher quality of <IR> disclosure.

Examining in detail corporate social responsibility activities and corporate governance integrity is pivotal to orienting strategy towards sustainable trajectories: to do so, corporate reporting and disclosure practices are essential tools. In this context, corporate governance systems that emphasize board diversity are proven, even in disruptive circumstances, to play a crucial role in providing corporate reports of higher quality. High disclosure quality that goes beyond mere financial results is considered to be necessary to remain competitive strategically, socially and environmentally.

This study aimed to analyze existing problems in the dissemination and management of emergency information on social media platforms, improve social media users' experience regarding such information, increase the efficiency of emergency information dissemination and curb the spread of misinformation.

In this study, the emergency information quality on social media platforms was examined. Based on the evaluation principles of the quality of mature information, social media information characteristics and the rules of emergency information dissemination, combined with relevant academic research results, an index to evaluate the quality of emergency information on social media was constructed. In addition, the authors have introduced cloud theory as an information quality evaluation method and used social media users' emotional characteristics to assess information quality evaluation results. A comprehensive system for evaluating emergency information quality, including indexes, methods and detection strategies was established. Based on a comprehensive system, a case study was conducted on the forest fires in Sichuan Province and the African swine fever events as reported on the Zhihu platform. In accordance with the results of the case study, the authors expanded the research and introduced the emotional characteristics of social media users as an independent evaluation dimension to evaluate the quality of emergency information on social media.

The comprehensive system's effectiveness was verified through the case study. Further, it was found that users' emotional characteristics (reflected in their information behavior) are inconsistent with their evaluation of websites' information quality regarding major emergencies. Integrating users' emotional characteristics into the information evaluation system can enhance its effectiveness following major emergencies.

First, an evaluation index system of emergency information quality on social media about major emergencies was offered. Unlike the commonly available index system for information quality evaluation, this proposed evaluation index system not only accounted for the characteristics of social media, such as massive disordered information, multiple information sources and rapid dissemination, but also for the characteristics of emergency events, such as variability and the absence of precursors. This proposed evaluation index system enhances the pertinence of the information quality evaluation and compensates for the shortcoming that the current research only focuses on evaluating social media information quality in a broad context, but pays insufficient attention to major emergencies. Second, cloud theory was introduced as a method to evaluate the emergency information quality found on social media. Existing research has primarily included the use of traditional statistical methods, which cannot transform numerical values into qualitative concepts effectively. Various indeterminate factors inevitably affect the quality of emergency information on social media platforms, and the traditional methods cannot eliminate this uncertainty in the evaluation process. The method to assess emergency information quality based on cloud theory can effectively compensate for the gaps in the research and improve the accuracy of information quality assessment. Third, the inspection and the dynamic adjustment of assessment results are absent in the research on information quality assessment, and the research has relied principally on the information users' evaluation and has paid insufficient attention to their attitudes and behaviors toward information. Therefore, the authors incorporated users' emotional characteristics into the evaluation of emergency information quality on social media and used them to test the evaluation results so that the results of the information quality assessment not only include the users' explicit attitudes but also their implicit attitudes. This enhances the effectiveness of the information quality assessment system. Finally, through this case study, it was found that an inconsistency exists between user evaluation and user emotional characteristics after major emergencies. The reasons for this phenomenon were explained, and the necessity of integrating user emotional characteristics into information quality assessment was demonstrated. Based on this, the users' emotional characteristics were used as a separate evaluation dimension for assessing the quality of emergency information on social media. Compared with assessing the quality of general information, integrating the user's emotional characteristics into the evaluation index system can lead the evaluation results to include not only the users' cognitive evaluation but also their emotional experience, further enhancing their adaptability.

Quality Assurance and Improvement Program (QAIP) is defined as “an ongoing and periodic assessment of the entire spectrum of audit and consulting work performed by the internal audit (IA) activity”. QAIP is an important component of internal auditors’ commitment to improve internal audit (IA) quality. The pressure towards improvement is urgent for central banks, in light of the vulnerabilities of their IA functions identified by the International Monetary Fund. The authors analyse the professional standards and the literature on IA and QAIP, aiming to propose general considerations to enhance IA quality and to develop and maintain a QAIP, with reference to central banks, also shedding light on the synergies among IA, QAIP and total quality management (TQM).

This paper reviews the most relevant professional standards in light of the professional and academic literature regarding IA quality, QAIP and their relationship with TQM. The analysis of these sources represents an important step to identify general measures to improve IA quality and develop effective QAIP in central banks.

This analysis shows that it is important to understand the rationale behind the development of an IA function and its theoretical and practical foundation, especially for complex organisations such as central banks. In addition, the authors show that QAIP represents an important tool to exploit the synergies between TQM and IA. These synergies could result in higher levels of quality for the IA function and more effective implementation of TQM within the whole organisation. Lastly, the authors provide practical suggestions to support the implementation of an effective QAIP in central banks and to spread TQM philosophy within the organisation.

The authors contribute to the scant literature on IA quality and QAIP by focusing on central banks and shedding light on the relationship with TQM. Regardless of their importance, these topics have been largely neglected by the extant literature.

Despite technological advancements to enhance patient health, the risks of not discovering the correct interactions and trends in digital health are high. Hence, a careful policy is required for health coverage tailored to needs and capacity. Therefore, this study aims to explore the adoption of a cognitive computing decision support system (CCDSS) in the assessment of health-care policymaking and validates it by extending the unified theory of acceptance and use of technology model.

A survey was conducted to collect data from different stakeholders, referred to as the 4Ps, namely, patients, providers, payors and policymakers. Structural equation modelling and one-way ANOVA were used to analyse the data.

The result reveals that the behavioural insight of policymakers towards the assessment of health-care policymaking is based on automatic and reflective systems. Investments in CCDSS for policymaking assessment have the potential to produce rational outcomes. CCDSS, built with quality procedures, can validate whether breastfeeding-supporting policies are mother-friendly.

Health-care policies are used by lawmakers to safeguard and improve public health, but it has always been a challenge. With the adoption of CCDSS, the overall goal of health-care policymaking can achieve better quality standards and improve the design of policymaking.

This study drew attention to how CCDSS as a technology enabler can drive health-care policymaking assessment for each stage and how the technology enabler can help the 4Ps of health-care gain insight into the benefits and potential value of CCDSS by demonstrating the breastfeeding supporting policy.

Identifying and prioritizing supply chain risk is significant from any product’s quality and reliability perspective. Under an input-process-output workflow, conventional risk prioritization uses a risk priority number (RPN) aligned to the risk analysis. Imprecise information coupled with a lack of dealing with hesitancy margins enlarges the scope, leading to improper assessment of risks. This significantly affects monitoring quality and performance. Against the backdrop, a methodology that identifies and prioritizes the operational supply chain risk factors signifies better risk assessment.

The study proposes a multi-criteria model for risk prioritization involving multiple decision-makers (DMs). The methodology offers a robust, hybrid system based on the Intuitionistic Fuzzy (IF) Set merged with the “Technique for Order Performance by Similarity to Ideal Solution.” The nature of the model is robust. The same is shown by applying fuzzy concepts under multi-criteria decision-making (MCDM) to prioritize the identified business risks for better assessment.

The proposed IF Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) for risk prioritization model can improve the decisions within organizations that make up the chains, thus guaranteeing a “better quality in risk management.” Establishing an efficient representation of uncertain information related to traditional failure mode and effects analysis (FMEA) treatment involving multiple DMs means identifying potential risks in advance and providing better supply chain control.

In a company’s supply chain, blockchain allows data storage and transparent transmission of flows with traceability, privacy, security and transparency (Roy et al., 2022). They asserted that blockchain technology has great potential for traceability. Since risk assessment in supply chain operations can be treated as a traceability problem, further research is needed to use blockchain technologies. Lastly, issues like risk will be better assessed if predicted well; further research demands the suitability of applying predictive analysis on risk.

The study proposes a hybrid framework based on the generic risk assessment and MCDM methodologies under a fuzzy environment system. By this, the authors try to address the supply chain risk assessment and mitigation framework better than the conventional one. To the best of their knowledge, no study is found in existing literature attempting to explore the efficacy of the proposed hybrid approach over the traditional RPN system in prime sectors like steel (with production planning data). The validation experiment indicates the effectiveness of the results obtained from the proposed IF TOPSIS Approach to Risk Prioritization methodology is more practical and resembles the actual scenario compared to those obtained using the traditional RPN system (Kim et al., 2018; Kumar et al., 2018).

This study provides mathematical models to simulate the supply chain risk assessment, thus helping the manufacturer rank the risk level. In the end, the authors apply this model in a big-sized organization to validate its accuracy. The authors validate the proposed approach to an integrated steel plant impacting the production planning process. The model’s outcome substantially adds value to the current risk assessment and prioritization, significantly affecting better risk management quality.

Under a gradual long-term plan of the Saudi Stock Market (TADWUAL) from 2016, Saudi Arabia decided to work with International Financial Reporting Standards (IFRS) board to fully adopt its accounting standards. Saudi Arabia has undergone several reforms in governance and standards of internal controls are changing rapidly. This study aims to assess whether IFRS adoption has any moderator role in the relationship between disclosure quality and firm-specific characteristics in the Saudi Stock Market.

This study assesses whether IFRS adoption has any moderator role in the relationship between disclosure quality and firm-specific characteristics in the Saudi Stock Market. The key research hypotheses postulate that compared to IFRS status, after adoption, several independent variables influence the disclosure level. The analysis covers a local sample of 184 Saudi listed firms over the period 2016 to 2020. Using an in-depth content analysis technique, the voluntary disclosure and number of annual report pages are measured manually and year by year to capture levels and unique characteristics. The authors apply cross-sectional regression, first difference method, Pooled OLS and feasible general least square estimations. The mean of disclosure level increases from 33.03% in 2016 to 56.14% in 2020.

The results reveal that the vast majority of firm-specific characteristics were significant in pre-IFRS adoption period. First difference analysis shows a significant impact of firm size and non-executive composition on the disclosure level. The authors confirm that IFRS adoption plays a critical role in the quality of firms’ financial reports and supports to create a conducive economic environment in Saudi Arabia.

First, the implementation of IFRS adoption should impact the Saudi accounting information and disclosure quality in Saudi context markedly. Second, firm-specific characteristics align with corporate governance are the main determinants of accounting information and transparency; therefore, focusing on this angle enables regulators and policymakers to mitigate uncertainty and asymmetric information. Third, the findings of this research state that there is a negative relationship between disclosure quality and board meetings. This encourages policymakers to reconsider the number of board meetings in firms that was not as high as in the developed markets. Notwithstanding all previous implications, it is recommended that future research undertake a various quasi-experimental design such as a difference-in-difference approach to estimate the causal effect of corporate governance mechanisms on IFRS 7 mandatory disclosure requirements on in Saudi Arabia context.

There is a lack of studies on this realm and such as these studies will enrich the understanding of aspects of IFRS adoption and contribute to the prior empirical literature. Importantly, the extend of this sample into other Gulf Cooperation Council countries and exhibition the difference effect can be very useful to enrich the knowledge of IFRS adoption aspects in corporate disclosure and accounting information quality.

Saudi Arabia has undergone several reforms in governance, and their standards of internal controls are changing rapidly. This has been attributed to the importance of providing guidelines, practices and regulations for listed companies. One of the major turning points of financial reporting quality in Saudi listed firms was adoption of IFRSs. This adoption deems to be necessity in ensuring the highest level of transparency and information reliability. Based on the findings of this research, the present investigations set up a platform and furnish many implications for policymakers, companies’ board of directors, financial analysts and other related authorities. The results should provide policymakers with greater insight of the relationship between disclosure quality and corporate-specific characteristics throughout the IFRS adoption periods. Thus, the results derived from this study can be effective and useful for the IFRS adoption committee in the Saudi Organization for Certified Public Accountants (SOCPA). According to the best of the authors’ knowledge and based on official secondary information sourced from the SOCPA website, there are several standards that are subject to difficulties in measurement and are modified from time to time, such as: IFRS1, IFRS8, IFRS12, IFRS16 and IFRS18.

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.

This study aims to address the growing importance of online health information (OHI) and the associated uncertainty. Although previous research has explored factors influencing the credibility of OHI, results have been inconsistent. Therefore, this study aims to identify the essential factors that influence the perceived credibility of OHI by conducting a meta-analysis of articles published from 2010 to 2022. The study also aims to examine the moderating effects of demographic characteristics, study design and the platforms where health information is located.

Based on the Prominence-Interpretation Theory (PIT), a meta-analysis of 25 empirical studies was conducted to explore 12 factors related to information content and source, social interaction, individual and media affordance. Moderators such as age, education level, gender of participants, sample size, platforms and research design were also examined.

Results suggest that all factors, except social support, have significant effects on the credibility of OHI. Among them, argument quality had the strongest correlation with credibility and individual factors were also found to be relevant. Moderating effects indicate that social support was significantly moderated by age and education level. Different sample sizes may lead to variations in the role of social endorsement, while personal involvement was moderated by sample size, platform and study design.

This study enriches the application of PIT in the health domain and provides guidance for scholars to expand the scope of research on factors influencing OHI credibility.

This article investigates the quality of stakeholder engagement (SE) process disclosure in the context of non-financial reporting (NFR) introduced by Directive 2014/95/EU (NFRD). SE implies the involvement of the subjects interested in the organization's activity, according to the principle of inclusiveness and the key concepts of the stakeholder theory (ST).

The authors conducted a content analysis on 75 non-financial statements (NFSs) published by companies listed on the Italian Stock Exchange in 2018 and 2021 to evaluate the evolutionary profiles of SE quality through the years.

The average level of SE is not significantly high. The research showed an overall poor quality of disclosure concerning stakeholders' key expectations and issues to be addressed and answered. Furthermore, a certain variability emerged in the quality of the disclosure between the various reports, and no significant improvements in SE quality were noted from 2018 to 2021.

The conclusions provide a replicable method for the analysis of SE quality in NFSs and the development of new standpoints in the ongoing debate on the implications of mandatory legislative frameworks for NFR. Content analyses intrinsically present margins of subjectivity. The sample was limited to a subset of NFS from Italy; hence, the results could be country specific.

This work suggests some possible ways of improvement of SE practices by companies.

Original assessment model based on eight variables identified from the academic literature and the most common international sustainability reporting standards. These variables were stakeholder identification, stakeholder selection process, degree of involvement, SE approach, dialogue channels, SE results, different points of view and integration of the SE process.