Volumen 7 (2016): Heft 2 (September 2016)

The special issue of Business Systems Research (SI of BSR) is co-published by the Slovenian Society INFORMATIKA – Section for Operational Research (SSI – SOR) and contains developments and new techniques in operations research (OR) models and their practical uses in various fields of business and economics. Seven papers that investigate developments and new techniques in operations research (OR) models and their practical uses in various fields of business and economics have been selected for this SI of BSR.

Background: Intramax is a hierarchical aggregation procedure for dealing with the multi-level specification problem and with the association issue of data set reduction, but it was used as a functional regionalization procedure many times in the past.

Objectives: In this paper, we analyse the simultaneous use of three different constraints in the original Intramax procedure, i.e. the contiguity constraint, the higher-inner-flows constraint, and the lower-variation-of-inner-flows constraint.

Methods/Approach: The inclusion of constraints in the Intramax procedure was analysed by a programme code developed in Mathematica 10.3 by the processing time, by intra-regional shares of total flows, by self-containment indexes, by numbers of singleton and isolated regions, by the number of aggregation steps where a combination of constraints was applied, by the number of searching steps until the combination of constraints was satisfied, and by surveying the results geographically.

Results: The use of the contiguity constraint is important only at the beginning of the aggregation procedure; the higher-inner-flows constraint gives singleton regions, and the lower-variation constraint forces the biggest employment centre as an isolated region up to a relatively high level of aggregation.

Conclusions: The original Intramax procedure (without the inclusion of any constraint) gives the most balanced and operative hierarchical sets of functional regions without any singletons or isolated regions.

Background: Alongside the theoretical progress made in understanding the factors that influence firm growth, many methodological challenges are yet to be overcome. Authors point to the notion of interpretability of growth prediction models as an important prerequisite for further advancement of the field as well as enhancement of models’ practical values.

Objectives: The objective of this study is to demonstrate the application of factor analysis for the purpose of increasing overall interpretability of the logistic regression model. The comprehensive nature of the growth phenomenon implies propensity of input data to be mutually correlated. In such situations, growth prediction models can demonstrate adequate predictability and accuracy, but still lack the clarity and theoretical soundness in their structure.

Methods/Approach: The paper juxtaposes two prediction models: the first one is built using solely the logistic regression procedure, while the second one includes factor analysis prior to development of a logistic regression model.

Results: Factor analysis enables researchers to mitigate inconsistencies and misalignments with a theoretical background in growth prediction models.

Conclusions: Incorporating factor analysis as a step preceding the building of a regression model allows researchers to lessen model interpretability issues and create a model that is easier to understand, explain and apply in real-life business situations.

Background: Agriculture is a production system in which the economic principles of organisation act in mutual dependence with its ecological boundaries.

Objectives: Building on this premise, the paper evaluates performance of a chosen agricultural production system (dairy production in Slovenia) from two complementary perspectives, the socio-economic and the biophysical.

Methods/Approach: The latter is presented by means of emergy analysis, which is a system-based approach that measures the aggregate work of biosphere needed for the provision of goods or services in the units of solar energy joules. The novelty aspect of this paper is the introduction of emergy indicators into the standard socioeconomic optimisation model of the chosen agricultural production system. The optimisation model based on linear mathematical programming is designed to empirically investigate different alternatives to the sector’s reorganisation.

Results: The results of the optimisation models suggest considerable restructuring of the sector and, consequently, large discrepancies in the sector’s performance.

Conclusions: The results suggest that further expansion of organic production systems as a result of a stronger environmental focus in farm management would improve the sector from both, the socio-economic and the emergy perspective. Moreover, even pursuing certain socio-economic targets may improve the sector’s biophysical performance and lower pressure on the local environment.

Background: Duality in the microeconomic theory enables us to represent consumers’ preferences and production technology with various dual functions satisfying certain regularity conditions.

Objectives: Since the basis for the application of duality in the microeconomic theory is the price taking behaviour, this paper takes the challenge of applying principles of duality to a monopolistic case where a single producer has an influence on the price which it charges for its product.

Methods/Approach: The standard approach of deriving the profit function for the monopolist from the production function and the defined pseudoproduction function is accompanied by an alternative approach in which the starting point is the pseudocost function. Starting from the derived profit function, the pseudoproduction function and the pseudocost functions are recovered and a version of Hotelling’s lemma is given.

Results: The structure of the profit maximization problem in a monopolistic case was made similar to the structure of the profit maximization problem in the perfectly competitive case and it is shown that all starting functions can be recovered back from derived functions. A version of Hotelling’s lemma is illustrated, which brings us indirectly from the profit function to the supply function.

Conclusions: By introducing the pseudoproduction function in the profit maximization model of a monopolist, the structure of the problem becomes similar to the perfectly competitive case and duality results can be applied. The profit function is derived from the pseudoproduction and the pseudocost function, and all starting functions are recovered back from the derived profit function.

Background: The paper focusses on the efficiency evaluation of the EU-28 NUTS 2 regions production process according to the concept of the Regional Competitiveness Index 2013.

Objectives: Production units are divided into four groups using the factors of regional competitiveness. Production technology also enables reduction of the undesirable outputs (a negative impact on health and long-term unemployment). Based on the analysis of distance of the production units from the efficiency frontiers, a directional output distance function assuming a constant return to scale is used. This approach thus respects the heterogeneity among the groups of regions.

Methods/Approach: The nonparametric meta-frontier Data Envelopment Analysis approach was used in two steps. Firstly, the efficiency evaluation within each group of regions is provided and in the second step the meta-frontier is set down. For the measurement of the gap between the group-frontier and the meta-frontier, the technology gap ratios are provided. The paper also analyses environmental inefficiencies.

Results: The obtained results indicate that a significant improvement of meta-technology ratio holds within the European context.

Conclusions: The combination of empirical findings, with respect to technology gaps and environmental technology gaps, supports the evidence that traditional differences of technological frontiers formation are more significant in comparison to group frontiers constitution.

Background: Investors on financial markets are interested in finding trading strategies which could enable them to beat the market. They always look for best possibilities to achieve above-average returns and manage risks successfully. MGARCH methodology (Multivariate Generalized Autoregressive Conditional Heteroskedasticity) makes it possible to model changing risks and return dynamics on financial markets on a daily basis. The results could be used in order to enhance portfolio formation and restructuring over time.

Objectives: This study utilizes MGARCH methodology on Croatian financial markets in order to enhance portfolio selection on a daily basis. Methods/Approach: MGARCH methodology is applied to the stock market index CROBEX, the bond market index CROBIS and the kuna/euro exchange rate in order to model the co-movements of returns and risks on a daily basis. The estimation results are then used to form successful portfolios.

Results: Results indicate that using MGARCH methodology (the CCC and the DCC model) as guidance when forming and rebalancing a portfolio contributes to less portfolio volatility and greater cumulated returns compared to strategies which do not take this methodology into account.

Conclusions: It is advisable to use MGARCH methodology when forming and rebalancing portfolios in terms of portfolio selection.

Background: In the cluster sampling approach many parameters have influence on lowering the survey costs and one of the most important is the intracluster homogeneity.

Objectives: The goal of the paper is to find the most optimal value of intracluster homogeneity in case when two or more questions or variables have a key role in the research.

Methods/Approach: Five key variables have been selected from a business survey conducted in Croatia and results for the two-stage cluster sampling design approach were simulated. The calculated intracluster homogeneity values were compared among all the five observed questions and survey costs and precision levels were inspected.

Results: In the new cluster sampling design, for the fixed precision level, the lowest survey costs would be achieved by using the intracluster homogeneity value which is the closest to the average intracluster homogeneity value among all the key questions. Similar results were obtained when survey costs were held fixed.

Conclusions: If there is more than one key question in the survey, then the best solution would be to use an average intracluster homogeneity value. However, one should notice that in that case minimum survey costs would not be reached, but the precision levels would increase at all key questions.