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Changes in food retailing (globalization, concentration) have negative impacts on smaller, “traditional” food retail businesses. Their market share decreasing year by year. The purpose of this study is to examine and compare the financial performances of these businesses under the given circumstances and current economic environment in a Hungarian and a Romanian county.

The study is based on two complete databases, including all companies that behoove retail food activity (considering the NACE cod) in the counties of Hajdu-Bihar (Hungary) and Cluj (Romania). The database analyzed contains the financial statements for five consecutive years for 212 and 690 businesses. Databases were examined by the most typical financial indicators using the multivariate and univariate analysis of variance and the k-medoid cluster analysis methods.

The results of the analysis have shown that there are differences in the number of retail food companies in the case of two counties, both in number and in financial performance. Companies in Hajdú-Bihar county perform better in terms of financial ratios than those in Cluj county. The groups created by k-medoids cluster analysis are relatively well distinguished in the case of Hajdú-Bihar county, while the picture is much more mixed in the case of Kolozs county. However, it is also important to note that the companies analyzed should generally perform better to survive.

Among the limitations of the study, it is important to note that the findings are relevant only to the two counties examined. Another limiting factor is that quite several companies had to be excluded from the analysis due to missing data or outliers.

The study presents for the corporate decision-makers the current performance of the companies of the sector examined in the two counties. The results of the study highlight the business areas of concern in management. The findings show that they need to change this performance to strengthen their market position. We believe that it is not enough to complain about the expansion of the supermarket chains, but they should take appropriate actions to improve their situation. Based on the results of the study, it can be concluded that there is a need to improve the financial efficiency of retail food companies in both counties to survive in the long run. This improvement is essential because retailers can play an important role in smaller settlements and narrower residential environments.

Comparative analysis of retail food companies in similar counties in these two neighboring countries has not been conducted using complex financial analysis. The study revealed the common and/or individual characteristics of these companies.

Fixing problems in an organization often involves developing managers in order to increase leader effectiveness. This paper aims to discuss the aforementioned issue.

Data collection includes multiple surveys and small group interviews. Analysis uses rigorous coding methods to construct a model of critical organizational values and behaviors essential for leadership effectiveness. The authors bring “theory to practice” by applying complexity leadership concepts in the authors’ intervention strategy.

Findings are categorized into three parts: identifying critical culture value gaps, applying complexity concepts to a scenario-based training intervention, and identifying intervention outcomes. Outcomes include transformed work environment led by leaders who respect others, share decision-making and enable employees to be interdependent.

This explanatory case study contributes to research by applying complexity leadership theory to create a practical consulting intervention.

This work provides a template and process for managers using complexity leadership to inform their client interventions.

This case study identifies value shortfalls in a manufacturing plant, documents a scenario-based training intervention which develops managers to build organizational trust. Results include reducing turnover, improving job satisfaction and increasing production.

The dropout rates of open e-learning platforms are often cited as high as 97%, with many users discontinuing their use after initial acceptance. This study aims to explore this anomaly through the lens of affordances theory, revealing design–reality gaps between users' diverse goals and the possibilities for action provided by an open IT artefact.

A six-month case study was undertaken to investigate the design implications of user-perceived affordances in an EU sustainability project which developed an open e-learning platform for citizens to improve their household energy efficiency. Thematic analysis was used to reveal the challenges of user continuance behaviour based on how an open IT artefact supports users in achieving individual goals (e.g. reducing energy consumption in the home) and collective goals (lessening the carbon footprint of society).

Based on the findings, the authors inductively reveal seven affordances related to open e-learning platforms: informing, assessment, synthesis, emphasis, clarity, learning pathway and goal-planning. The findings centre on users' perception of these affordances, and the extent to which the open IT artefact catered to the goals and constraints of diverse user groups. Open IT platform development is further discussed from an iterative and collaborative perspective in order to explore different possibilities for action.

The study contributes towards research on open IT artefact design by presenting key learnings on how the designers of e-learning platforms can bridge design–reality gaps through exploring affordance personalisation for diverse user groups. This can inform the design of open IT artefacts to help ensure that system features match the expectations and contextual constraints of users through clear action-oriented possibilities.

The purpose of this paper is to review the current literature in the field of tourism demand forecasting.

Published papers in the high quality journals are studied and categorized based their used forecasting method.

There is no forecasting method which can develop the best forecasts for all of the problems. Combined forecasting methods are providing better forecasts in comparison to the traditional forecasting methods.

This paper reviews the available literature from 2007 to 2017. There is not such a review available in the literature.

This paper aims to test purchasing power parity (PPP) hypothesis for Greece, Italy, Ireland, Portugal and Spain, which are known as the GIIPS countries.

The authors conduct a comprehensive analysis by using unit root approaches without and with structural breaks and non-linearity.

The PPP is valid for the GIIPS countries. Considering structural breaks in non-linear framework plays a crucial role.

There is no empirical study testing PPP hypothesis by focusing on the GIIPS countries. This study further takes into account for structural breaks and non-linearity in the real exchange rates of these countries.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

This paper aims to examine whether changing the clustering of countries within a United States Combatant Command (COCOM) area of responsibility promotes improved forecasting of conflict.

In this paper statistical learning methods are used to create new country clusters that are then used in a comparative analysis of model-based conflict prediction.

In this study a reorganization of the countries assigned to specific areas of responsibility are shown to provide improvements in the ability of models to predict conflict.

The study is based on actual historical data and is purely data driven.

The study demonstrates the utility of the analytical methodology but carries not implementation recommendations.

This is the first study to use the statistical methods employed to not only investigate the re-clustering of countries but more importantly the impact of that change on analytical predictions.

This paper proposes a heuristic, data-driven approach to the rapid performance evaluation of periodic maintenance on complex production plants. Through grouping, maintenance interval (MI)-based evaluation and performance assessment, potential nonvalue-adding maintenance elements can be identified in the current maintenance structure. The framework reduces management complexity and supports the decision-making process for further maintenance improvement.

The evaluation framework follows a prescriptive research approach. The framework is structured in three steps, which are further illustrated in the case study. The case study utilizes real-life data to verify the feasibility and effectiveness of the proposed framework.

Through a case study conducted on 9,538 pieces of equipment from eight offshore oil and gas production platforms, the results show considerable potential for maintenance performance improvement, including up to a 23% reduction in periodic maintenance hours.

The problem of performance evaluation under limited data availability has barely been addressed in the literature on the plant level. The proposed framework aims to provide a quantitative approach to reducing the structural complexity of the periodic maintenance evaluation process and can help maintenance professionals prioritize the focus on maintenance improvement among current strategies.

The proposed framework is especially suitable for initial performance assessment in systems with a complex structure, limited maintenance records and imperfect data, as it reduces management complexity and supports the decision-making process for further maintenance improvement. A similar application has not been identified in the literature.