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This study investigates the extent to which structural equivalence between acquiring and target firms is associated with post-merger and acquisition (M&A) performance—a relationship that is proposed to be moderated by industry-level vertical relatedness between acquiring and target firms.

Applying social network analysis and regression, this study analyzes a buyer–supplier relationship network dataset of 279 M&A deals completed between 2010 and 2017 to test the hypotheses. Structural equivalence is measured as the proportion of common customers and suppliers between an acquiring firm and a target firm.

Supporting a view about the importance of supply chains in explaining M&As outcomes, the results suggest that the structural equivalence in the supplier network is positively associated with post-M&A firm performance. The results also show that the effect of the structural equivalence in the customer network is moderated by vertical relatedness between two merging firms (i.e. structural equivalence contributes to post-M&A performance when vertical industry relatedness is high).

This study contributes to the M&A and supply network literature by investigating the performance implications of structural equivalence in supplier and customer networks, demonstrating the importance of taking a supply chain view when explaining M&As outcomes. Specifically, the authors suggest considering structural equivalence as a new type of relatedness between merging firms (i.e. relatedness in network resources in explaining post-M&A performance). It also indicates how industry-level vertical resource relatedness, which is about relatedness in internal resources between the two firms, could interact with firm-level network resource relatedness, which is about relatedness in external supply chain resources between the two firms, in affecting post-M&A performance.

This study aims to analyze information on vessel traffic between the two Koreas with a probability distribution for each route/vessel type. The study will then conduct an estimate for maritime transport patterns of inter-Korean trade in the future. To analyze the flow of inter-Korean coastal shipping, this study conducted visualization analysis of shipping status between North and South Korea by year, ship type, and port using navigation data of three years from Port Logistics Information System (Port-MIS) sources during 2006 to 2008, which saw the most active exchanges between the two governments. Also, this study analyzes shipping status between the two governments as a probability distribution for each port and provides the prospects for future maritime transport for inter-Korean trade by means of Bayesian Networks and simulation. The results of the analysis are as follows: i) when North-South routes are reopened, the import volume for sand from North Korea will be increased; ii) investment in the modernization of ports in North Korea is required so that shipping companies can generate profit through economies of scale; iii) the number of the operating vessels including container ships between the two governments is expected to increase like when the tensions and conflict on the Korean Peninsula was release, especially between Busan port in South Korea and Nampo port in North Korea; and iv) among container ships, transshipment containers imported and exported through Busan Port will be shipped to North Korea by feeder transportation.

This study aims to develop an assessment methodology using a Bayesian network (BN) to predict the failure probability of oil tanker shipping firms.

This paper proposes a bankruptcy prediction model by applying the hybrid of logistic regression and Bayesian probabilistic networks.

The proposed model shows its potential of contributing to a powerful tool to predict financial bankruptcy of shipping operators, and provides important insights to the maritime community as to what performance measures should be taken to ensure the shipping companies’ financial soundness under dynamic environments.

The model and its associated variables can be expanded to include more factors for an in-depth analysis in future when the detailed information at firm level becomes available.

The results of this study can be implemented to oil tanker shipping firms as a prediction tool for bankruptcy rate.

Incorporating quantitative statistical measurement, the application of BN in financial risk management provides advantages to develop a powerful early warning system in shipping, which has unique characteristics such as capital intensive and mobile assets, possibly leading to catastrophic consequences.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank.

The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators.

The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent.

The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.

El riesgo de incumplimiento de préstamos o la evaluación del riesgo de crédito es importante para las instituciones financieras que otorgan préstamos a empresas e individuos. Existe el riesgo de que el pago de préstamos no se cumpla. Para entender los niveles de riesgo de los usuarios de crédito (corporaciones e individuos), los proveedores de crédito (banqueros) normalmente recogen gran cantidad de información sobre los prestatarios. Las técnicas analíticas predictivas estadísticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los préstamos. En este artículo abordamos la cuestión de la predicción por defecto de los préstamos a corto plazo para un banco comercial tunecino.

Utilizamos una base de datos de 924 archivos de créditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo bayesiano de clasificadores se llevó a cabo y los resultados muestran que la tasa de clasificación buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo.

Los resultados de la prueba de validación muestran que la buena tasa de clasificación es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de área bajo curva (AUC, por sus siglas en inglés) es del orden del 69%.

El documento destaca el hecho de que el Banco Central tunecino obligó a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de crédito de los prestatarios.

Curva ROC, Evaluación de riesgos, Riesgo de incumplimiento, Sector bancario, Algoritmo clasificador bayesiano.

Artículo de investigación

Efficient delivery of integrated healthcare requires solid alliances and collaboration with stakeholders on a regular basis. Due to coronavirus disease 2019 (COVID-19), it has become necessary to explore new ways of delivering integrated healthcare, and virtual clinics have offered one solution and are likely to continue due to the uncertainty with COVID-19. This study aims to explore clinicians’ experiences of how efficient virtual elective knee clinics (VEKC) are in an orthopaedic setting in comparison to traditional face-to-face clinics.

The study utilised a mixed-methods study to obtain qualitative and quantitative data. This involved an anonymous online survey in addition to in-depth qualitative interviews conducted with a purposive sample of multidisciplinary colleagues who work with the VEKC in an acute hospital.

Three overarching themes and nine sub-themes emerged in the qualitative analysis. Overall, clinicians in both the quantitative and qualitative aspects of the study highlighted several ways that virtual clinics are efficient from both the patient and health service perspective. However, participants also highlighted barriers in relation to virtual clinics not being suitable for certain cohorts of patients and pathologies.

This is the first study in Ireland to provide valuable insights into the experiences of multidisciplinary clinicians using VEKC and their efficiency compared to traditional face-to-face clinics.

Several types of vaccines were manufactured by different companies to control and stop the spread of COVID-19. This study aimed to identify the postvaccination side effects of the three different vaccines (Pfizer, AstraZeneca and Sinopharm) among the Iraqi population in Baghdad, Iraq.

A prospective cross-sectional study was conducted in Baghdad, Iraq from May 2021 to March 2022. An online-based questionnaire was used to collect the data through social media, i.e. WhatsApp, Messenger and Google Classroom. A total of 737 vaccinated participants using a snowball sampling methodology were used in this study.

Among the study population, 328 (44.50%) were males and 409 (55.50%) were females. The highest age group that participated was 18–30 years (79.10%) followed by 31–40 years (12.10%), 41–50 years (4.20%), 51–60 years (2.40%) and 60 = years (2.20%). However, 58.8% of the participants received Pfizer-BioNTech, 23.7% received the AstraZeneca-Oxford vaccine and 17.5% received Sinopharm. Out of the total participants, 56.60% showed postvaccination side-effects such as fever, headache, fatigue and dizziness, while 33% showed no side-effects and 10.40% were not sure. Pfizer-BioNTech and AstraZeneca-Oxford vaccines were the most vaccines prevalent of side-effects.

The majority of the side reactions associated with the AstraZeneca and Pfizer vaccines were manageable and self-limiting, including fever, fatigue, headache, joint pain and dizziness, compared to the Sinopharm vaccines, which reported lower postside effects.

Uncertainty, a state of unknowing linked to threats and opportunities, is a key characteristic of megaprojects, making it challenging for government officials and politicians to decide on their initiation. For them, implementation by the private sector adds an extra layer of complexity and uncertainty to megaproject planning. In this context, only a few studies have focussed on governing and the mobilization of uncertainty arguments in communication between government actors and private developers either in favour of or against megaprojects. The purpose of this article is to shed light on how private megaproject proposals progress towards political acceptance or rejection in public decision-making.

This process of public decision-making on private megaproject proposals is examined in the case of the Helsinki–Tallinn undersea rail tunnel. In line with the interpretive research tradition, the authors’ study draws on a qualitative methodology underpinned by social constructionism. The research process can be characterized as abductive.

The authors’ findings suggest that while public decision-making on megaprojects is a conflictual and dynamic process, some types of uncertainty are relatively more important in affecting the perceived feasibility of the projects in the eyes of public sector decision-makers.

This study contributes to the debate on uncertainty management in megaprojects, proposing a new type of uncertainty – uncertainty about privateness – which has not been explicitly visible thus far.

Financial health of a corporation is a great concern for every investor level and decision-makers. For many years, financial solvency prediction is a significant issue throughout academia, precisely in finance. This requirement leads this study to check whether machine learning can be implemented in financial solvency prediction.

This study analyzed 244 Dhaka stock exchange public-listed companies over the 2015–2019 period, and two subsets of data are also developed as training and testing datasets. For machine learning model building, samples are classified as secure, healthy and insolvent by the Altman Z-score. R statistical software is used to make predictive models of five classifiers and all model performances are measured with different performance metrics such as logarithmic loss (logLoss), area under the curve (AUC), precision recall AUC (prAUC), accuracy, kappa, sensitivity and specificity.

This study found that the artificial neural network classifier has 88% accuracy and sensitivity rate; also, AUC for this model is 96%. However, the ensemble classifier outperforms all other models by considering logLoss and other metrics.

The major result of this study can be implicated to the financial institution for credit scoring, credit rating and loan classification, etc. And other companies can implement machine learning models to their enterprise resource planning software to trace their financial solvency.

Finally, a predictive application is developed through training a model with 1,200 observations and making it available for all rational and novice investors (Abdullah, 2020).

This study found that, with the best of author expertise, the author did not find any studies regarding machine learning research of financial solvency that examines a comparable number of a dataset, with all these models in Bangladesh.

Digital resources such as smart applications reviews and online feedback information are important sources to seek customers’ feedback and input. This paper aims to help government entities gain insights on the needs and expectations of their customers. Towards this end, we propose an aspect-based sentiment analysis hybrid approach that integrates domain lexicons and rules to analyse the entities smart apps reviews. The proposed model aims to extract the important aspects from the reviews and classify the corresponding sentiments. This approach adopts language processing techniques, rules, and lexicons to address several sentiment analysis challenges, and produce summarized results. According to the reported results, the aspect extraction accuracy improves significantly when the implicit aspects are considered. Also, the integrated classification model outperforms the lexicon-based baseline and the other rules combinations by 5% in terms of Accuracy on average. Also, when using the same dataset, the proposed approach outperforms machine learning approaches that uses support vector machine (SVM). However, using these lexicons and rules as input features to the SVM model has achieved higher accuracy than other SVM models.