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The paper investigates the effect of ownership structure on the financial distress of firms listed in sub-Saharan Africa.

Using secondary data from 106 non-financial firms listed in 9 selected SSA countries from 2016 to 2021, the research using paired t-tests and conditional logistic regression model analysed a sample of 174 distressed observations matched with 174 non-distressed observations.

T-tests determined significant differences between distressed and non-distressed groups concerning institutional, foreign, and local ownership. Conditional logistic results established that institutional, foreign, and state ownership significantly reduce distress. However, managerial ownership does not influence financial distress while a significant positive relationship is observed between local ownership and financial distress.

This is the first study to investigate the influence of ownership structure, including local ownership, on financial distress in SSA, employing a unique methodology of matched design and conditional logistic regression analysis. Furthermore, the paper presents cross-country evidence from emerging frontier markets, highlighting the importance of governance frameworks in firms’ stability.

This bibliometric review examines the recent literature on sensor-based fall prevention for older adults. It analyzes publication trends, key researchers and institutions, major research themes, as well as gaps and opportunities in this field.

A comprehensive search was conducted in Scopus and Web of Science (WoS) databases for publications from 1990 to 2024. Bibliometric indicators including publication output, citation analysis and co-occurrence of keywords were used to map the research landscape. Network visualizations were employed to identify key thematic clusters.

The research on sensor-based fall prevention has grown rapidly, peaking in 2019. The USA, Australia and Canada lead this work, with universities and hospitals collaborating globally. Key themes include fall epidemiology, wearable sensors and AI for fall detection. Opportunities exist to better implement these sensor systems through large trials, user-centered design, hybrid sensors and advanced analytics.

While comprehensive, the analysis focused primarily on publications indexed in Scopus and WoS, which may not capture all relevant literature. Future studies could expand the search to include other databases and conduct deeper analyses of highly influential studies.

The review provides an evidence-informed roadmap to accelerate the translation of sensor innovations into scalable and sustainable fall prevention practices for vulnerable older adult populations.

This is the first comprehensive bibliometric analysis to map the research landscape of sensor-based fall prevention, identifying key trends, themes and opportunities to advance this critical domain addressing a major global public health challenge.

Recent technological advancements, often linked to Industry 4.0, require organizations to be more agile and innovative. Blockchain technology (BT) holds immense potential in driving organizations to achieve efficiency and transparency in supply chains. However, there exist some insurmountable challenges associated with the adoption of BT in organizational supply chains (SC). This paper attempts to categorically identify and systematize the most influential challenges in the implementation of BT in SC.

This study resorts to an extensive literature review and consultations with experts in the field of supply chain management (SCM), information technology and academia to identify, categorize and prioritize the major challenges using VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and Combined Compromise Solution method (CoCoSo).

The top three classes of challenges revealed in this study are privacy challenges (PC), infrastructure challenges (IC) and transparency challenges (TC). Maintaining a balance between data openness and secrecy and rectification of incorrect/erroneous input are the top two challenges in the PC category, integration of BT with sustainable practices and ensuring legitimacy are the top two challenges in the IC category, and proper and correct information sharing in organizations was the top most challenge in the TC category.

Future scholars and industry professionals will be guided by the importance of the challenges identified in this study to develop an economical and logical approach for integrating BT to increase the efficiency and outcome of supply chains across several industrial sectors.

In the past few decades, performance measuring systems have become important managerial tools for healthcare organizations. Healthcare performance metrics are a useful tool in understanding how healthcare organizations achieve their goals while satisfying the needs of their patients and conforming to national and international standards. Various efforts have been made to assess healthcare performance. Most of these measures are focused on a single perspective or developed by a single source to meet management and strategic objectives on time.

We develop a review of the literature to shed light on the measures used to assess performance in the healthcare sector at various points in time, as well as to establish a thorough understanding of healthcare performance measurement.

Developing real-time digital traceability of metrics and an integrative perspective that increases the actionability of information acquired is an attractive potential made possible by the introduction of new technologies and the digitization of data.

We conclude that a proper measurement system should be one to combine patient, physician, non-medical staff and system perspective, which will further facilitate the assessment of healthcare performance and the comparative function.

Questionnaire studies of knowledge, attitude and practice (KAP) are effective research in the field of health, which have many shortcomings. The purpose of this research is to propose an automatic questionnaire-free method based on deep learning techniques to address the shortcomings of common methods. Next, the aim of this research is to use the proposed method with public comments on Twitter to get the gaps in KAP of people regarding COVID-19.

In this paper, two models are proposed to achieve the mentioned purposes, the first one for attitude and the other for people’s knowledge and practice. First, the authors collect some tweets from Twitter and label them. After that, the authors preprocess the collected textual data. Then, the text representation vector for each tweet is extracted using BERT-BiGRU or XLNet-GRU. Finally, for the knowledge and practice problem, a multi-label classifier with 16 classes representing health guidelines is proposed. Also, for the attitude problem, a multi-class classifier with three classes (positive, negative and neutral) is proposed.

Labeling quality has a direct relationship with the performance of the final model, the authors calculated the inter-rater reliability using the Krippendorf alpha coefficient, which shows the reliability of the assessment in both problems. In the problem of knowledge and practice, 87% and in the problem of people’s attitude, 95% agreement was reached. The high agreement obtained indicates the reliability of the dataset and warrants the assessment. The proposed models in both problems were evaluated with some metrics, which shows that both proposed models perform better than the common methods. Our analyses for KAP are more efficient than questionnaire methods. Our method has solved many shortcomings of questionnaires, the most important of which is increasing the speed of evaluation, increasing the studied population and receiving reliable opinions to get accurate results.

Our research is based on social network datasets. This data cannot provide the possibility to discover the public information of users definitively. Addressing this limitation can have a lot of complexity and little certainty, so in this research, the authors presented our final analysis independent of the public information of users.

Combining recurrent neural networks with methods based on the attention mechanism improves the performance of the model and solves the need for large training data. Also, using these methods is effective in the process of improving the implementation of KAP research and eliminating its shortcomings. These results can be used in other text processing tasks and cause their improvement. The results of the analysis on the attitude, practice and knowledge of people regarding the health guidelines lead to the effective planning and implementation of health decisions and interventions and required training by health institutions. The results of this research show the effective relationship between attitude, practice and knowledge. People are better at following health guidelines than being aware of COVID-19. Despite many tensions during the epidemic, most people still discuss the issue with a positive attitude.

To the best of our knowledge, so far, no text processing-based method has been proposed to perform KAP research. Also, our method benefits from the most valuable data of today’s era (i.e. social networks), which is the expression of people’s experiences, facts and free opinions. Therefore, our final analysis provides more realistic results.

Dubai Quality Award (DQA) is one of the quality awards that has been implemented in the United Arab Emirates (UAE) for the purpose of improving organisational performance. The objective of this study is to determine whether receiving the DQA has positive impacts on Human Resource Development Practices (HRDP) and organisational performance (OP) of the award-winning companies.

Secondary data relating to DQA scores were collected from the Department of Economic Development archive of UAE and primary data relating to HRDP and OP were collected from the individual DQA recipients. Data elicited were subjected to statistical analyses by using the structural equation modelling (SEM) approach. This was to identify the nature of relationships existing between DQA criteria and organisational performance outcomes through the mediating effect of HRDP.

The results indicated that winning of DQA significantly enhances organisational performance and that HRDP is a statistically significant mediator between DQA and OP. Based on the findings of this study, a model has been developed by linking the DQA criteria, HRDP and organisational performance.

Despite few of its limitations, this study is first of a kind in UAE that investigated the relationships among DQA criteria, HRDP and OP. The findings are expected to help the UAE companies to adjust their management policies and practices for enhancing their organisational performance. The findings can also be applicable to companies in other countries, especially Gulf countries that have similar awards such as DQA.

Ọsẹ dúdú production and sale constitute a major indigenous business among the Yoruba people. Scholars have noted that the business is capable of boosting the socio-economic status of black soap entrepreneurs and of countries. However, ọsẹ dúdú enterprise has some significant threats and problems that are yet to be researched. This chapter examined the challenges of osẹ dúdú entrepreneurs in Southwest Nigeria. Twenty-six interviews were conducted among indigenous black soap producers and sellers in Ogun, Oyo and Lagos States. Data were analysed in themes. Weather, financial, spiritual, copyright and succession challenges, as well as issues such as a large number of sellers, debt, lack of support, pricing and brand competition, were found to be problems faced by black soap entrepreneurs. This chapter concluded that certain controllable and uncontrollable factors were not only capable of limiting the development of osẹ dúdú business but also have adverse implications for the achievement of the sustainable development goals through the indigenous resource. This chapter suggests that osẹ dúdú business actors such as mechanical engineers, local fabricators, financial institutions, and governmental and non-governmental agencies collaborate with black soap entrepreneurs to ameliorate the challenges of the latter. It is only through this alliance that black soap entrepreneurs can contribute to indigenous business development and the achievement of sustainable development goals in Africa.

This paper explores the factors that shape the complexity of company annual reports in the USA. Using a general-to-specific modeling approach, this study examines the determinants of annual reports' tone complexity.

Negative relationships were found between agency problems and tone; agency costs and readability of annual reports; profitability and tone; and ownership structure and tone complexity.

These relationships helped to confirm several of this study’s hypotheses, whereas positive associations were found between investment growth opportunities and tone complexity, which contradicts one of our initial hypotheses. Findings reveal that the more complex the language in an annual report is, the more difficult it is to strategically make a judgment or decision about the reported financial situation.

Analyzing these variables allows security analysts and investors to obtain important information, not available in the financial statements, which would enhance their understanding of the firm and improve their recommendations and investment decision-making process.

The objective of this research is to determine the influence of solvency and liquidity on the profitability [return on assets (ROA)] of Tunisian banks from Q2-2020 to Q3-2022 by considering asset quality as a moderating variable.

This study uses data on liquidity, solvency, ROA and asset quality for 12 banks. It also considers bank size, gross domestic product (GDP) growth and inflation as control variables. The methodology is based on panel data with generalized least squares (GLS) estimation to assess the moderate influence of the asset quality on solvency, liquidity and ROA. Also, the generalized method of moments (GMM) estimation is used as a robustness test.

The results of the GLS model estimation indicated a negatively significant moderating correlation between the liquidity and the solvency. The data from the GMM model indicate that the liquidity variable predicted by the liquidity has a positively significant influence on a bank's ROA as well as for the solvency variable, which is predicted by the capital capacity. Therefore, we conclude that these two variables had a positively significant impact on the ROA.

The studies have many implications for banks and their management in addition to the industry regulators. The results of this study will enable political decision-makers to determine the banks' profits based on their liquidity and solvency.

This analysis provides financial explanations and recommendations for stakeholders in Tunisian banks. Furthermore, these banks must also be able to maintain their liquidity and solvency to ensure their profits in times of COVID-19.

This study aimed to analyse user experiences and perceptions of eRupee banking applications in India, focussing on understanding the key factors driving user satisfaction and dissatisfaction.

A comprehensive text-mining approach was employed to analyse 5,176 user reviews collected from the Google Play Store. Sentiment analysis and latent Dirichlet allocation (LDA) were used to classify reviews and uncover prevailing themes.

The analysis revealed that positive reviews highlighted the themes of usefulness, convenience, satisfaction, app attributes, and ease of use. Negative reviews emphasise issues related to lack of trust, faulty updates, unreliability, security concerns, and inadequate customer support. The Logistic Regression model demonstrated superior performance in predicting user sentiments, achieving an AUC of 0.7926 and an accuracy rate of 77.90%.

This study was limited to reviews from a single-platform source. Future research could incorporate data from multiple online sources and employ qualitative methods to gain deeper insight. Additionally, longitudinal studies and cross-cultural analyses are recommended to capture evolving user sentiments and global perspectives.

The findings provide actionable insights for bank managers, app developers and policymakers to enhance eRupee applications by addressing identified issues and leveraging positive aspects to improve overall user experience and satisfaction.

This study makes a novel contribution to the literature on digital currency and advanced text-mining techniques using machine-learning models to analyse user feedback in the context of an emerging economy. The proposed conceptual model and practical recommendations serve as the foundation for future research and practical development in digital financial services.