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The purpose of this paper is to elaborate pros and cons of two coding methods: the rapid network assessment (RNA) and the manual content analysis (MCA). In particular, it focuses on the applicability of a new rapid data extraction and utilization method, which can contribute to the timely coordination of disaster and emergency response operations.

Utilizing the data set of textual information on the Superstorm Sandy response in 2012, retrieved from the LexisNexis Academic news archive, the two coding methods, MCA and RNA, are subjected to social network analysis.

The analysis results indicate a significant level of similarity between the data collected using these two methods. The findings indicate that the RNA method could be effectively used to extract megabytes of electronic data, characterize the emerging disaster response network and suggest timely policy implications for managers and practitioners during actual emergency response operations and coordination processes.

Considering the growing needs for the timely assessment of real-time disaster response systems and the emerging doubts regarding the effectiveness of the RNA method, this study contributes to uncovering the potential of the RNA method to extract relevant data from the megabytes of digitally available information. Also this research illustrates the applicability of MCA for assessing real-time disaster response networks by comparing network analysis results from data sets built by both the RNA and the MCA.

Heart diseases have become one of the most causes of death among Egyptians. With 500 deaths per 100,000 occurring annually in Egypt, it has been noticed that medical data faces a high-dimensional problem that leads to a decrease in the classification accuracy of heart data. So the purpose of this study is to improve the classification accuracy of heart disease data for helping doctors efficiently diagnose heart disease by using a hybrid classification technique.

This paper used a new approach based on the integration between dimensionality reduction techniques as multiple correspondence analysis (MCA) and principal component analysis (PCA) with fuzzy c means (FCM) then with both of multilayer perceptron (MLP) and radial basis function networks (RBFN) which separate patients into different categories based on their diagnosis results in this paper, a comparative study of the performance performed including six structures such as MLP, RBFN, MLP via FCM–MCA, MLP via FCM–PCA, RBFN via FCM–MCA and RBFN via FCM–PCA to reach to the best classifier.

The results show that the MLP via FCM–MCA classifier structure has the highest ratio of classification accuracy and has the best performance superior to other methods; and that Smoking was the most factor causing heart disease.

This paper shows the importance of integrating statistical methods in increasing the classification accuracy of heart disease data.

This paper aims to investigate emerging trends in data analytics and knowledge management (KM) job market by using the knowledge, skills and abilities (KSA) framework. The findings from the study provide insights into curriculum development and academic program design.

This study traced and retrieved job ads on LinkedIn to understand how data analytics and KM interplay in terms of job functions, knowledge, skills and abilities required for jobs, as well as career progression. Conducting content analysis using text analytics and multiple correspondence analysis, this paper extends the framework of KSA proposed by Cegielski and Jones‐Farmer to the field of data analytics and KM.

Using content analysis, the study analyzes the requisite KSA that connect analytics to KM from the job demand perspective. While Kruskal–Wallis tests assist in examining the relationships between different types of KSA and company’s characteristics, multiple correspondence analysis (MCA) aids in reducing dimensions and representing the KSA data points in two-dimensional space to identify potential associations between levels of categorical variables. The results from the Kruskal–Wallis tests indicate a significant relationship between job experience levels and KSA. The MCA diagrams illustrate key distinctions between hard and soft skills in data across different experience levels.

The practical implications of the study are two-fold. First, the extended KSA framework can guide KM professionals with their career planning toward data analytics. Second, the findings can inform academic institutions with regard to broadening and refining their data analytics or KM curricula.

This paper is one of the first studies to investigate the connection between data analytics and KM from the job demand perspective. It contributes to the ongoing discussion and provides insights into curriculum development and academic program design.

Social inequalities in mental health can be captured by occupational situation and social class stratification. This study analyzes the adequacy of a classification of work and employment conditions and an adaptation of the Goldthorpe social class scheme in relation to mental health in Barcelona, Spain.

Multiple correspondence analysis (MCA) and hierarchical cluster analysis (CA) on working and employment conditions were used to empirically construct distinctive working groups. Through 2 logistic regression models, we contrasted the association between mental health and (1) the cluster of employment and working conditions (with 4 categories: insiders, instrumental, precarious and peripheral workers), and (2) a standard Spanish version of the Goldthorpe social class scheme. The performance of the 2 models was assessed with Akaike and Bayesian information criteria. The analyses were carried out using the Barcelona Health Survey (2016) including the labor force population from 22 to 64 years of age.

Wide inequalities were found in mental health with both class schemes. The empirical class scheme was more effective than the Goldthorpe social class scheme in explaining mental health inequalities. In particular, precarious and peripheral workers in the MCA-CA analysis, together with unemployed workers, emerged as distinctive social groups apparently masked within the lower social class in the standard scheme. When using the standard scheme, the authors recommend widening the scope at the bottom of the social class categories while shrinking it at the top as well as considering unemployed persons as a separate category to better represent mental health inequalities.

The working poor appear to report at least as much poor mental health as unemployed persons. Policies aimed at more inclusive work should consider job quality improvements to improve the mental well-being of the labor force.

Our study examines the utility of social classes to explain mental health inequalities by comparing an empirically based social class to the Spanish adaptation of the Goldthorpe classification.

Decision making at local planning level is usually concerned with evaluation of alternatives and selection of a preferred action. This can be improved with the use of multicriteria (MCA) methods which provide a systematic process for trading off effects of various alternative, synthesizing, individual contributions. This paper illustrates three MCA methods for evaluating complex planning projects where multiple criteria are taken into account. These methods are applied to an example of urban regeneration. Subjective issues, such as those related to the perception of quality of life, are taken into account alongside more quantitative data. The results obtained using the three methods are considered when applied to three alternative design solutions.

The purpose of this paper is to propose a multi-methodological approach for assessing the sustainability of urban projects. The proposed framework has the objective of addressing the different dimensions involved in the analysis (economic, environmental, social, technological, cultural, etc.), following the subsequent phases for the definition of the projects (from a very general masterplan to more detailed preliminary projects).

The multi-methodological approach is organized according to subsequent steps, involving the application of different evaluation methods, namely SWOT analysis, multicriteria analysis, discounted cash flow analysis and sensitivity analysis. With the aim of better illustrating the proposed approach, the method is applied on a real case concerning the project of a new boulevard in the city of Skopje.

The proposed approach resulted to be successful in structuring the complex planning context and in increasing and improving the information for the project, thus acting as a learning process.

The paper is based on the experimentation of an hybrid approach for addressing planning process. The multi-methodological framework can be useful in supporting decision making process in the domain of urban projects. In this sense, the proposed framework is able to communicate the planners’, designers’ and decision maker’s perspectives, to enhance transparency in the decision process and thus to increase acceptance.

People living in rural and urban areas experience different socioeconomic conditions, which should affect their consumer behaviour. The purpose of this study is to examine the effects of these socio-economic differences on the consumption of milk, yoghurt, and cheese.

Face-to-face interviews are conducted with 191 households in the city of Van (Eastern Turkey). The collected data are evaluated using SPSS – Multiple Correspondence Analysis.

Results of the evaluations show that there are socio-economical differences between urban and rural populations in terms of age, education, occupation, and income, which affects their consumption behaviour. The rural consumers generally consume raw (unpasteurized, unpackaged) milk. These consumers tend to have low income and be illiterate. The urban consumers, on the other hand, consume pasteurized milk and have higher incomes compared with the rural consumers and have a mid-level to university education. In the rural areas, consumers usually make their own yoghurt but those who purchase their yoghurt pay attention to its packaging the most. In the rural areas, consumers who make their own cheese have mid-level income and primary school education.

The paper analyses the current effects of socio-economic differences in rural and urban areas on the consumption behaviour of milk, yoghurt, and cheese using the Multiple Correspondence Analysis (MCA). MCA analysis has rarely been used in this area.

The purpose of this paper is to identify offender typologies based on aspects of the offenders’ psychopathology and their associations with crime scene behaviours using data derived from the National Confidential Enquiry into Suicide and Safety in Mental Health concerning homicides in England and Wales committed by offenders in contact with mental health services in the year preceding the offence (n=759).

The authors used multiple correspondence analysis to investigate the interrelationships between the variables and hierarchical agglomerative clustering to identify offender typologies. Variables describing: the offenders’ mental health histories; the offenders’ mental state at the time of offence; characteristics useful for police investigations; and patterns of crime scene behaviours were included.

Results showed differences in the offenders’ histories in relation to their crime scene behaviours. Further, analyses revealed three homicide typologies: externalising, psychosis and depression.

These typologies may assist the police during homicide investigations by: furthering their understanding of the crime or likely suspect; offering insights into crime patterns; provide advice as to what an offender’s offence behaviour might signify about his/her mental health background. Findings suggest information concerning offender psychopathology may be useful for offender profiling purposes in cases of homicide offenders with schizophrenia, depression and comorbid diagnosis of personality disorder and alcohol/drug dependence.

Empirical studies with an emphasis on offender profiling have almost exclusively focussed on the inference of offender demographic characteristics. This study provides a first step in the exploration of offender psychopathology and its integration to the multivariate analysis of offence information for the purposes of investigative profiling of homicide by identifying the dominant patterns of mental illness within homicidal behaviour.

Although Islamic banks offer superior financial services than other interest-based conventional banks, they could not expand their share and dominate the markets in several Islamic countries. This problem could be attributed to some causes not addressed. The current study proposes Islamic financial literacy as an important factor that could help aggress this problem. Due to a wide variety of Islamic financial services and the lack of understanding of these services, the banks' small business customers are indifferent between Islamic and interested-based conventional services to finance their business.

This study uses the exploratory technique of multiple correspondence analysis to detect any potential role of Islamic financial literacy in customers' preference for Islamic banks over conventional ones. The potential effect was tested with other essential factors, such as the customers' age, gender, and educational level. This analysis was conducted on a data set from 2061 banks' small businesses customers using the mall-intercept survey method.

The study shows a low level of Islamic financial literacy among Yemeni banks' small business owners' customers. Furthermore, despite integrating some critical factors that could influence the actual bank selection process among Yemini banks' customers, the authors found a decisive potential role of Islamic financial literacy as one of the key determinants of bank selection preferences.

This is the first paper to explore the potential role of Islamic financial literacy on the actual selection between Islamic Banks and their counterparts: the conventional banks in Yemen. The research results could build a more comprehensive theoretical model on Islamic banks' customer behavior.

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.