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This paper aims to propose a methodology for managerial decision-making based on scenario planning and a multi-criteria approach.

The methodology consists of two stages, one referring to scenario planning and the other to multi-criteria decision-making. The methodology was applied to a company in the Brazilian agribusiness sector, aiming to help managers face the current situation of the COVID-19 pandemic.

The proposal addresses a set of simple methods for developing a scenario analysis based on different approaches. Although the methodology may allow the future addition of new, perhaps more robust strategies, the purpose of the analysis is not only to tell the decision maker which strategy should be adopted, but also to provide greater knowledge about the problem and possible scenarios.

The contribution of this research is to propose a structured and easily applicable methodology that can help managers in the future planning of their companies, especially when faced with complex decisions and high level of uncertainty.

This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.

Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.

The results showed that random forests and bagging outperform other methods in terms of predictive power.

This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.

Cardiac arrest is a severe heart anomaly that results in billions of annual casualties. Smoking is a specific hazard factor for cardiovascular pathology, including coronary heart disease, but data on smoking and heart death not earlier reviewed. The Heart Rate Variability (HRV) parameters used to predict cardiac arrest in smokers using machine learning technique in this paper. Machine learning is a method of computing experience based on automatic learning and enhances performances to increase prognosis. This study intends to compare the performance of logistical regression, decision tree, and random forest model to predict cardiac arrest in smokers. In this paper, a machine learning technique implemented on the dataset received from the data science research group MITU Skillogies Pune, India. To know the patient has a chance of cardiac arrest or not, developed three predictive models as 19 input feature of HRV indices and two output classes. These model evaluated based on their accuracy, precision, sensitivity, specificity, F1 score, and Area under the curve (AUC). The model of logistic regression has achieved an accuracy of 88.50%, precision of 83.11%, the sensitivity of 91.79%, the specificity of 86.03%, F1 score of 0.87, and AUC of 0.88. The decision tree model has arrived with an accuracy of 92.59%, precision of 97.29%, the sensitivity of 90.11%, the specificity of 97.38%, F1 score of 0.93, and AUC of 0.94. The model of the random forest has achieved an accuracy of 93.61%, precision of 94.59%, the sensitivity of 92.11%, the specificity of 95.03%, F1 score of 0.93 and AUC of 0.95. The random forest model achieved the best accuracy classification, followed by the decision tree, and logistic regression shows the lowest classification accuracy.

The purpose of this paper is to observe how to invest in upper-middle income countries via an innovation perspective following global innovation index (GII) by multicriteria decision aid (MCDA) approach, once MCDA was designed to support subjective decisions.

Pearson’s correlation was the milestone for understanding innovation indicators at upper-middle income countries profiles. In a MCDA first step, the analytical hierarchy process (AHP) was applied to obtain the criteria weight. In this step, the judgments or evaluations inputted in AHP were collected from a sample composed by five experts in GII. After getting the criteria weights compose to GII, Borda and Preference Ranking Organization Method for Enrichment Evaluations (PROMÉTHÉE) methods were applied to obtain an MCDA-based GII. The inputs for this second step were: the weights come from AHP output; and the countries performance came from GII data.

As a result, it was found out the upper-middle countries’ rank to invest and groups with countries acting like “hubs” or “bridges” for economic sectors in near countries; when they are grouped according to their maximum and minimum scores profiles, observing not only a particular region but also similar profiles at diverse world areas.

Pearson-AHP-PROMÉTHÉE works as a supportive decision tool for several and complex investment perspectives from criteria and alternatives analysis regarding innovation indicators for upper-middle income countries. This combination also demonstrates grouping possibilities, aligning profiles and not only ranking countries for investment and eliminating others but also grouping countries with similar profiles via innovation indicators MCDA combined application.

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 primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Although Public Research Institutions (PRIs) are large technology producers, they lack automated information tools that follow technical and scientific criteria for assessing and valuing patents. The assessment and valuation processes are stages of technology transfer (TT) that make it possible to obtain productive arrangements and guide the efforts of those involved in the development, maintenance and negotiation. This study aims to analyze the hybrid model of assessment and valuation of technologies by Soares (2018), applying the ‘Valorativo' software. In addition to patent value and indicator scores, the methods allow an understanding of the technology portfolio and its management.

This research is quali-quantitative, following an approach of applied nature and descriptive objectives. The research has bibliographical, documental and case study features based on the software development methodologies described in the study and the theoretical framework.

The Valorativo software assisted in the analysis of ten patents on PRIs. With the data collection and patent analysis, PAT1 scored highest among engineering patents, PAT3 scored highest among pharmaceutical patents and PAT10 scored highest among biotechnology patents. Five of the assessed patents resulted in a surplus of net present value (NPV), final net present value (NPVF) and royalties; revenue expectations outpaced investments.

The authors based the developed software on Soares’s (2018) methodology, with additional calculations and graphs. The Web software and the spreadsheet with Visual Basic for Application (VBA) were developed to deal with the patents assessment and valuation, helping in the analysis of their Legal Value, Technological Value and Market Conditions in the assessment process, and the Discounted Cash Flow and NPV in the valuation process.

The software helps with patent analysis and can generate indicators for traders, technology holders and researchers. Thus, it was necessary to understand and develop a theoretical-applied framework to outline and replicate the methodology clearly and easily.

The purpose of this study is to fill the gap and apply a fault tree analysis (FTA) in detention lists of Black Sea Region published port state reports from 2005 to 2016. The study analyzes valid records of 2,653 detained ships with 6,374 deficiencies based on a strategic management approach. This paper sets up FTA technique to assess the detention probability of a random ship which calls the Black Sea Region with the help of detention lists published within subject years.

This paper is not published elsewhere, and it is based on an original work, which figures out detention probability of a regular ship at Black Sea Region port state control from published lists of Black Sea Memorandum of Understanding (MoU). By utilizing these detention lists, a generic fault tree diagram is drawn. Those probabilities could be used strategically with the most seen deficiencies in the region which all could guide the users, rule makers and the controllers of the maritime system.

FTA has conducted based on the data which was collected from website of BS MoU detention lists that published from 2005 to 2016. Those lists have been published on monthly basis from 2011 to 2016 and on quarterly basis from 2005 to 2010. Proper detention records have been included into the research, whereas some missing records were excluded. Subject lists have been harmonized and rearranged according to Black Sea MoU Detention Codes which was published on October 2017 at Black Sea MoU’s website. According to BS MoU Annual Reports, 58,620 ships were inspected from 2005 to 2016 as seen in Table 1. Those ships were inspected by each member country’s PSOs in the light and guidance of predefined selection criteria of the region. Detention frequency of inspected ships detected as 0.103116 which explains any ship that called any port in the Black Sea Region could be 10% detained after inspected by PSO. Also, each intermediate event-calculated frequency enlightens the probabilities of nonconformities of ships. Although those deficiencies show structural safety and security nonconformities, those probabilities also prove us that management side of the ships are not enough to manage and apply a safety culture. By the light of that, ship owners/managers could see the general nonconformities according to regional records and could manage their fleet and each ship as per those necessities.

In the light of the above analysis, the future research on this subject could be studied on other regions which might enable a benchmark opportunity to users. Also, insurance underwriters have their own reports and publications that could clarify different points of view for merchant mariners and regulators. In this research, FTA is used as a main method to figure out the root causes of the detentions. For future researches, different qualitative and quantitative methods could be used under the direction of subjects.

Detention frequency of inspected ships detected as 0.103116 which explains any ship that called any port in the Black Sea Region could be 10% detained after inspected by PSO. Also, each intermediate event-calculated frequency enlightens the probabilities of nonconformities of ships. Although those deficiencies show structural safety and security nonconformities, those probabilities also prove us that management side of the ships are not enough to manage and apply safety culture. By the light of that, ship owners/managers could see the general nonconformities according to regional records and could manage their fleet and each ship as per those necessities.

With the nature of carriage, shipping business carry out its essential economic attendance in world trade system via inclusion in national and international transportation. As a catalyst in international trade, shipping itself enables time, place and economic benefits to users (Bosneagu, Coca and Sorescu, 2015). Social and institutional pressures generate shipping industry as one of the most regulated global industries which creates high complexity. Industry evolved to multi-directional structure ranges from international conventions (IMO and ILO) to “supra-national interferences” (EU directives), to regional guidance (MoUs) to national laws (flag states). Ship operators endeavor to adopt/fit its industry environment where rules are obvious. With adaptation of industrial environment, ship operators are able to create an important core competency.

This study enlightens the most recorded deficiencies and analyzed them with the help of fault three method. These calculated frequencies/probabilities show the most seen nonconformities and the root causes of detentions in the Black Sea Region in which those results will be benefited strategically that enables a holistic point of view that guide the owners/managers, charterers/sellers/shippers, classification societies, marine insurance underwriters, ship investors, third parties, rule makers and the controllers of the system to apply safety culture.

Purpose – The purpose of this research was to get the initial information about the phenotype diversity of avocado plants and as an information source of Acehnese avocado germplasm.

Methodology – This research was conducted at Bebesen sub-district Aceh Tengah District, from March to October 2017. Exploration was conducted using the descriptive method with purposive sampling. Plants observed in accordance with predetermined criteria namely plants that have been several times fruitful and preferred by consumers.

Originality – The research shown that the avocado plants in the Bebesen sub-district have a high degree of diversity. The diversity can be seen from canopy width, stem circumference, plant height, stem surface, tree shape, number of branches, branch shape, leaf length, leaf width, leaf area, and leaf shape. The number of superior avocado plants that were sampled was 15 accessions. The similarity level of superior avocado accession in the Bebesen sub-district ranged from 0.34 to 1.00.