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The contribution of the banking industry to the financial crisis of 2007/8 has raised public concerns about the financial soundness of banks around the world with many countries still suffering the backlogs of this crisis. The continuous emergence of such crises at both national and international levels increases governments', bank regulators' and financial market participants' need for reliable tools to assess the financial soundness of banks. In this context, this study investigates the financial soundness of the Kazakh banking sector, which is ranked by the World Bank as the first in the world in terms of the percentage of nonperforming loans (NPL) to total gross loans in 2012.

Using data about all Kazakh banks over the period January 01, 2008 to January 01, 2014, the study identifies a number of accounting indicators that influence the financial soundness of banks using principal component analysis (PCA). Then, it uses the outcomes of the PCA in a cluster analysis and groups the Kazakh banks into sound, risky and unsound banks at two points in time: January 01, 2008 and January 01, 2014. This methodology was further tested against a ranking system of banks and proved to be more reliable in detecting risky banks.

Fifteen financial ratios were initially selected as accounting indicators for the assessment of bank financial soundness. Using PCA, twelve indicators were isolated, which explain five principal components of capital adequacy, return on assets, profitability, asset quality, liquidity and leverage. Then using the “k-means” method, the results suggest a structure of the Kazakh banking sector on January 01, 2008 that includes two groups of banks: sound and risky banks. On January 01, 2014, this structure of the banking system has changed to include three groups of banks: sound, risky and unsound banks. Thus, in 2014 a new group of banks has emerged, i.e. financially unsound banks.

The proposed cluster-based methodology has proven to be a reliable tool to detect the financial soundness of Kazakh banks, which makes us advocate its employability for bank monitoring and supervision purposes.

This study is the first to employ a cluster-based methodology to assess the financial soundness of a banking sector. This methodology can be used at a micro-level to determine the structure of a banking sector. Also, it can be used to monitor any changes in the structure of a banking sector and provide early warning signals about the financial health of banks.

This paper seeks to present an integrated principal component analysis (PCA) data envelopment analysis (DEA) framework for assessment and ranking of manufacturing systems based on equipment performance indicators.

The integrated framework discussed in this paper is based on PCA and DEA. The validity of the integrated model is further verified and validated by numerical taxonomy (NT) methods.

The results of the integrated PCA DEA framework show the ranking of sectors and weak and strong points of each sector with regard to equipment and machinery. Moreover, a non‐parametric correlation method, namely, Spearman correlation experiment shows high level of correlation among the findings of PCA, DEA and NT. Furthermore, it identifies which indicators have major impacts on the performance of manufacturing sectors.

To achieve the objectives of this study, a comprehensive study was conducted to locate all economic and technical indicators which influence equipment performance. These indicators are related to equipment productivity, efficiency, effectiveness and profitability. Standard factors such as down time, time to repair, mean time between failure, operating time, value added and production value were considered as shaping factors. The manufacturing sectors are selected according to the format of International Standard for Industrial Classification.

The modeling approach of this paper could be used for ranking and analysis of other sectors in particular or countries in general.

Understanding diversity amongst potential beneficiaries of land redistribution is of critical importance for both design and planning of successful land reform interventions. This study seeks to add to the existing literature on farming types, with specific emphasis on understanding diversity within a sub-group of commercially oriented or emerging smallholders.

Using a multivariate statistical analysis – principal component and cluster analyses applied to a sample of 442 commercially-oriented smallholders – five distinct clusters of emerging farmers are identified, using variables related to farmers' characteristics, income and expenditure and farm production indicators and willingness to participate in land redistribution. The five clusters are discussed in light of a predefined selection criteria that is based on the current policies and scholarly thinking.

The results suggest that there are distinct differences in farming types, and each identified cluster of farmers requires tailored support for the effective implementation of land reform. The identified homogenous sub-groups of smallholders, allows us to understand which farmers could be a better target for a successful land redistribution policy.

Most of the existing typology studies in South Africa tend to focus on general smallholders and in the Eastern Cape province; this study extends the literature by focussing on specific prime beneficiaries of land reform in three provinces. This study uses a more detailed dataset than the Statistics general and agricultural household surveys.

Integrating sustainability strategies with business processes is the most challenging task for industry professionals due to the lack of a proper understanding of sustainability concepts. At the same time, a lack of proper guidance restricts them from pursuing such activities. As far as the aspects of implementation are concerned, it is very tough to analyse and pick up key points to start with. The purpose of this paper is to utilize a text mining approach to analyse qualitative data and identify the critical issues for implementing sustainability in the manufacturing sector by focussing on turning processes based on the survey responses of researchers and industry professionals.

An integrated method employing principal component analysis (PCA) and the k-means clustering algorithm has been applied to extract useful information from a set of various suggestions provided by both the groups surveyed. The textual data has also been visualized using word clouds and, thus, it has been compared with the results of the text mining approach.

The results of the study indicate the importance of the role of government organizations and the need for a skilled workforce, which are crucial for enhancing aspects of sustainability in the manufacturing sector, as supported by both researchers and industry professionals. Besides this, researchers have highlighted the need to focus more on environmentally related issues, whereas industry professionals have raised performance-related issues.

The findings of the study present the important concerns of both the groups towards sustainability initiatives and, thus, will help to enhance the understanding of the underlying possibilities of negotiating jointly to enhance the performance of machining processes.

The novelty of this paper lies in its identification of important initiatives that are having a direct impact on the sustainable aspects of the machining process, based on the views of researchers and industry professionals.

The aim of this paper is to better understand European consumers' behaviour in relation to Short Food Supply Chains (SFSCs), so as to provide insights to support their development as part of a sustainable food system. Specifically, it aims to analyse consumer purchase patterns, motivations and perceived barriers and to identify patterns of behaviour amongst different consumer groups.

An online consumer survey was conducted in 12 European countries (n = 2,419). Quantitative data analysis, including principal component analysis (PCA) and cluster analysis, was undertaken using SPSS.

Four consumer clusters are named according to their behavioural stage in terms of SFSC engagement: Unaware Unengaged, Aware Unengaged, Motivationally Engaged and Executively Engaged. Unaware Unengaged and Aware Unengaged are in the non-engagement phase of behaviour. Motivationally Engaged are motivationally activated to engage in the behaviour but fail to do so consistently. Executively Engaged is the fully engaged cluster, being motivated to act and purchasing local food on a frequent basis. The results show an interesting interplay between motivations and barriers, i.e. higher scores for motivations and lower scores for barriers do not necessarily translate into higher purchase frequency.

The research gleans insights into the contextual factors that may inhibit SFSC purchases in different consumer segments. It offers practical implications for policymakers and others seeking to develop SFSCs as part of a sustainable food system.

The purpose of this paper is to develop a maintenance decision support system (DSS) framework using in-service lubricant data for fault diagnosis. The DSS reveals embedded patterns in the data (knowledge discovery) and automatically quantifies the influence of lubricant parameters on the unhealthy state of the machine using alternative classifiers. The classifiers are compared for robustness from which decision-makers select an appropriate classifier given a specific lubricant data set.

The DSS embeds a framework integrating cluster and principal component analysis, for feature extraction, and eight classifiers among them extreme gradient boosting (XGB), random forest (RF), decision trees (DT) and logistic regression (LR). A qualitative and quantitative criterion is developed in conjunction with practitioners for comparing the classifier models.

The results show the importance of embedded knowledge, explored via a knowledge discovery approach. Moreover, the efficacy of the embedded knowledge on maintenance DSS is emphasized. Importantly, the proposed framework is demonstrated as plausible for decision support due to its high accuracy and consideration of practitioners needs.

The proposed framework will potentially assist maintenance managers in accurately exploiting lubricant data for maintenance DSS, while offering insights with reduced time and errors.

Advances in lubricant-based intelligent approach for fault diagnosis is seldom utilized in practice, however, may be incorporated in the information management systems offering high predictive accuracy. The classification models' comparison approach, will inevitably assist the industry in selecting amongst divergent models' for DSS.

Talented people with interpersonal skills and competencies are pivotal for creating knowledge, innovation and organizational effectiveness, contributing to local development. In this regard, the quality of life is a critical factor in attracting and retaining talented people in any region. This study aims to analyze talents’ perception of the quality of life in an urban innovation ecosystem. This study considers talents to be the students from Science, Technology, Engineering and Mathematics programs.

This study analyzes 263 students from three of the country’s most important universities located in the city of Porto Alegre in southern Brazil. This study examines the data using principal component analysis and cluster techniques.

The results indicate five clusters. The “Love for the city” and the “Mixed” ones portray high levels of a sense of belonging to the city, but differ, for example, in their perception on city infrastructure. Conversely, both the “Worried about education” and the “Worried about commercial services” ones express low levels of a sense of belonging in the city. The “Security” cluster portrays the highest level on security issues in the city. The cluster analyses provide detailed information on the factors valued by talents in urban innovation ecosystems.

To date, to the best of the authors’ knowledge, this study is the first that uses cluster techniques to measure talents’ perception of the quality of life in an urban innovation ecosystem. The findings contribute to mapping talents’ perception and building profiles which may support the development of policies and programs to attract and retain qualified people in innovation ecosystems.

The purpose of this paper is to discover profiles of organic food consumers in Lebanon by performing a market segmentation based on lifestyle and attitude variables and thus be able to propose appropriate marketing strategies for each market segment.

A survey, based on the use of closed-ended questionnaire, was addressed to 320 organic food consumers in the capital Beirut, in February and March 2014. Descriptive analysis, principal component analysis and cluster analysis (k-means method) were performed upon collected data.

Four clusters were obtained and labelled based on psychographic characteristics and willingness to pay for the most purchased organic products. “Localist” and “Health conscious” clusters were the largest proportion of the selected sample, thus these were the most critical to be addressed by specific marketing strategies, emphasising the combination of local and organic food and the healthy properties of organic products. “Rational” and “Irregular” cluster were relatively small groups, addressed by pricing and promotional strategies.

This is the first study attempting to segment organic food consumers into different categories in a developing country as Lebanon.

Improving the adoption rates of proven innovative practices in bean farming and their impacts on livelihoods requires persistent promotion of practices, complemented by rigorous socioeconomic analysis that recognises the diversity of smallholder farmers. The purpose of this paper is to typify farm households in Angonia district of Mozambique, based on their socioeconomic characteristics prompting the adoption of proven innovative practices in bean production, management, and marketing.

The authors use a multivariate statistical analysis approach that combines principal component analysis, and cluster analysis to clearly identify five distinctive farm household types with respect to the adoption of proven innovative practices in smallholder bean farming using socio-economic factors.

The study findings show that various socioeconomic factors define clusters and can be associated with the adoption and use of innovative practices in smallholder bean farming. The five farm types identified are: female landowners with small farm sizes (29.52 per cent); educated farmers with access to credit (6.63 per cent); relatively rich male land owners with large farm sizes and low education (8.73 per cent); youthful, inexperienced and poor male farmers (6.33 per cent); and experienced female farmers with high labour endowments (8.43 per cent). The respective farm types seemed to have different patterns in the adoption of proven innovative practices in bean farming.

The authors recommend that policy makers promote strategies meant to raise adoption of innovative practices in bean production, management and marketing in Mozambique that takes into account household diversity. The farm types identified by this study can be a good starting point for guiding such future efforts.

Deposit insurance is a key element in modern banking, as it guarantees the financial safety of deposits at depository financial institutions. It is necessary to have at least a dual fair premium rate system based on creditworthiness of financial institutions, as considering singular premium system for all banks will have moral hazard. This paper aims to develop theoretical and empirical model for calculating dual fair premium rates.

The definition of a fair premium rate in this paper is a rate that covers the operational expenditures of the deposit insuring organization, provides it with sufficient funds to enable it to pay a certain percentage share of deposit amounts to depositors in case of bank default and provides it with sufficient funds as precautionary reserves. To identify and classify healthier and more stable banks, the authors use credit rating methods that use two major dimensional reduction techniques. For forecasting nonperforming loans (NPLs), the authors develop a model that can capture both macro shocks and idiosyncratic shocks to financial institutions in a vector error correction model.

The response of NPLs/loans to macro shocks and idiosyncratic innovations shows that using a model with macro variables only is insufficient, as it is possible that under favorable economic conditions, some banks show negative performance due to bank level reasons such as mismanagement or vice versa. The final results show that deposit insurance premium rate needs to be vary based on banks’ creditworthiness.

The results provide interesting insight for financial authorities to set fair deposit insurance premium rate. A high premium rate reduces the capital adequacy of individual financial institutions, which endangers the stability of the financial system; a low premium rate will reduce the security of the financial system.