Assessing the efficiency of Lithuanian transport sector by applying the methods of multimoora and data envelopment analysis

This study focuses on evaluating Lithuanian transport sector throughout 1995–2009 by applying multi–criteria decision making method MULTIMOORA (Multi–Objective Optimization plus the Full Multiplicative Form) and data envelopment analysis (DEA). MULTIMOORA provided ranks that enabled to perform time series analysis, whereas DEA made possible to identify both technical and scale inefficiencies. Due to limited data availability, we analyzed the transport sector as a whole, i. e. it was not decomposed into that of land, air, railway or water. Although every production factor, including labour, capital and land is required for developing the transport sector, due to limited data availability, it is not possible to tackle them all when performing analysis. Consequently, one input, namely energy consumption in transport, was considered in the conducted analysis. On the other hand, two forms of transport – passenger and freight transport – were distinguished, and each of them was measured using composite indicators of passenger and tonne kilometres respectively. These two indices were considered as the outputs of transport sector activity. The final ranks provided by MULTIMOORA indicate that the transport sector was operating most effectively during 2004–2008, whereas it exhibited relative inefficiency throughout 1996–1998. The application of DEA suggests that the efficiency downturn of 1996–1998 might have been caused by technical inefficiency, whereas that of 2008–2009 was driven by scale inefficiency. Indeed, the technical modernization of the transport sector as well as the resolution of resource allocation problems might have lead to an increase in technical efficiency. Meanwhile, economic downturn prevents the transport system from working at full capacity; hence, scale efficiency is still observed

An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods

The aim of this study was to offer a novel procedure for integrated assessment and comparison of Lithuanian economic sectors on the basis of financial ratios and fuzzy MCDM methods. The complex of interrelated issues regarding integrated assessment of economic sectors is discussed in the paper. The object of research is financial indicators of different Lithuanian economic sectors. The proposed procedure for multi-criteria comparison of economic sectors encompasses: 1) the indicator system, 2) application of fuzzy MCDM methods, and 3) inter-sectoral comparison based on ranks provided by fuzzy MCDM methods. The research covers period of 2007–2010, starting at the very beginning of the economic recession and, hopefully, ending with the upcoming recovery. The application of the three MCDM methods was successful. The results suggested the best performing sector being that of forestry and logging. Furthermore, enterprises operating in trade sector, hospitality sector, mining and quarrying sector, information sector, or manufacturing sector can be considered as working more efficiently than an average Lithuanian enterprise. At the other end of spectrum, construction, real estate, and transportation sectors were those severely damaged by the economic crisis: they were ranked below the average alternative. Meanwhile, relatively low positions of utilities sectors may be caused by their specifics. Finally, the transport sector can be considered as the typical victim of economic downturn

Turizmo vadyba: tarptautinio turizmo administravimo įvadas

Mykolo Romerio universiteto Politikos ir vadybos fakulteto Strateginio valdymo katedros dėstytojų parengtame mokomajame leidinyje apžvelgti tarptautinio turizmo administravimo pagrindai: pasaulio turizmo raida; turizmo samprata, formos ir rūšys; jo funkcijos ir kultūra; pateikiama turisto samprata; aptarti turizmo ištekliai; turizmo produktai ir paslaugos; turizmo regionai; turizmo administravimas; turizmo praktika

Lietuvos inovacijų infrastruktūra: struktūrinis funkcinis požiūris.

Abundance and diversity of organisations of innovation infrastructure complicates their study and development as well as improvement of innovation support. The problem of the research is the following: aspects of the methodological analysis of innovation infrastructure institutions. The subject of the research is the Lithuanian institutions of innovation infrastructure. The aim of the study was to suggest methodological decisions for the institutional (organisational) analysis. Three tasks were defined: (1) to discuss evolution of infrastructure conception; (2) to group institutions of innovation infrastructure according to management levels; (3) to systematise support institutions according to their role. The following methods were applied during the research: scientific literature analysis, expert evaluation, analysis of online sources. The methodological aspects of the Lithuanian innovation infrastructure institutions analysis carried out on the structural basis for functional analysis are discussed. From the structural point of view, the following can be distinguished: institutions of innovation policy, innovation management and innovation support institutions. Innovation infrastructure institutions are classified according to their role: encouraging innovations on the state level, mediation, direct contact with enterprises. This article discusses the organisations providing services of innovation infrastructure. The results of the research are appropriate for researches and the development of supply and demand of services, as well as management of innovation infrastructure.Straipsnyje aptariami Lietuvos inovacijų infrastruktūros institucijų analizės metodiniai aspektai struktūrinės funkcinės analizės pagrindu. Struktūriniu požiūriu išskirtos: inovacijų politikos, inovacijų valdymo ir inovacijų paramos institucijos. Inovacijų infrastruktūros institucijos suklasifikuotos pagal atliekamą vaidmenį: inovacijų skatinimas valstybės lygmeniu, tarpininkavimas, tiesioginis ryšys su įmonėmis. Straipsnyje aptartos inovacijų infrastruktūros organizacijų teikiamos paslaugos. Gautus rezultatus tikslinga taikyti inovacijų infrastruktūros valdymo, paslaugų pasiūlos bei paklausos tyrimuose ir tobulinime

VIŠECILJNA OPTIMIZACIJA BLAGOSTANJA U ZEMLJAMA ČLANICAMA EU-ROPSKE UNIJE

Well-being is of crucial importance for both individual and society as a whole. It is therefore important to quantify performance and progress made by certain states, regions, communities, social groups, and individuals in improving their well–being. The aim of study was to offer a new framework for multi–criteria assessment as well as international comparison of objective well–being. Well–being is a multi–dimensional phenomenon; hence the appropriate indicator system should be capable to identify the most important underlying processes influencing well–being. For our research we have established the indicator system of twelve indicators identifying various dimensions of well–being. Therefore we propose MULTIMOORA, a model which can be used for approaching the objective of societal well–being. It is applied for international comparison of the well-being in the EU Member States. Consequently, it was revealed that Ireland, the Netherlands, Denmark, Austria, France, Cy-prus, Finland, Germany, and Belgium have achieved the highest level of well–being as of 2009. At the other end of spectrum, Czech Republic, Lithuania, Slovakia, Bulgaria, Poland, Hungary, Estonia, Lat-via, and Romania can be considered as those peculiar with relatively lowest well–being.Blagostanje je od ključnog značaja kako za pojedinca tako i za društvo u cjelini. Stoga je važno kvantificirati performanse i napredak određenih država, regija, zajednica, društvenih grupa i pojedinaca kako bi se unaprijedilo njihovo blagostanje. Cilj istraživanja je ponuditi novi okvir za višeciljnu procjenu kao i međunarodnu usporedbu objektivnog blagostanja. Blagostanje je višedimenzionalna pojava; stoga bi prikladni sustav indikatora trebao biti u mogućnosti identificirati najvažnije temeljne procese koji utječu na blagostanje. Za potrebe našeg istraživanja ustanovili smo indikatorski sustav od dvanaest indikatora koji identificiraju razne dimenzije blagostanja. Stoga predlažemo MULTIMOORA, model koji se može koristiti za približavanje cilju društvenog blagostan-ja. Primjenjuje se u svrhu međunarodne usporedbe blagostanja u zemljama članicama EU. Tako se otkrilo da su Irska, Nizozemska, Danska, Austrija, Francuska, Cipar, Finska, Njemačka i Belgija do-segle najviši stupanj blagostanja od 2009. Na drugom kraju spektra se nalaze Češka, Litva, Slovačka, Bugarska, Poljska, Mađarska, Estonija, Latvija i Rumunjska u kojima je blagostanje najniže

European Union member states preparing for Europe 2020. An application of the MULTIMOORA method

It is the intention of the European Union to create a growing and sustainable European economy by 2020, a much more moderate target than the 2010 target of becoming the most competitive and dynamic knowledge-based economy in the world. This intention has to be supported by an adequate Optimization and Decision Support System. Therefore, MULTIMOORA is proposed. MULTIMOORA is a quantitative method, which compares multiple and optimum objectives, expressed in different units, as much as possible on a non-subjective basis. In opposition to similar methods MULTIMOORA does not need normalization, being based on dimensionless measures. Importance of an objective can eventually be given by the stakeholders concerned. MULTIMOORA is composed of three approaches: Ratio System, Reference Point and Multiplicative Form Methods, all of the same importance and each controlling each other. Twenty two objectives, 10 originating from statistics and 12 from statistics and forecasts, important for the future, characterize the 27 EU-Countries economies as a preparation for 2020. Which of these countries are the best prepared for 2020? A Dominance Theory, summarizing the three obtained ordinal numbers per country, ranks the 27 countries for that purpose

Tikimybinis gamybos technologijos modelis ir dalinės gamybos funkcijos: taikymas Lietuvos žemės ūkio sektoriuje

The paper presents the non-parametric benchmarking technique, viz. Free Disposal Hull (FDH), along with its probabilistic extensions. Indeed, the frontier methods are rather sensitive to outliers. The probabilistic methodology, therefore, is useful when handling the practical problems ofbenchmarking. The paper aimed at analysing the patterns of efficiency ofLithuanian family farms with respect to the uncertain data. The latter aim was achieved by the virtue of the probabilistic production functions. The sensitivity of the efficiency scores estimated for the Lithuanian family farms was analysed by manipulating the numbers of randomly drawn benchmark observation estimations and thus constructing respective order-m frontiers. The livestock farms appeared to be the most efficient, or even super-efficient, independent of the model orientation or the order of the frontier. The application of the order-alpha frontier also confirmed these results

Daugiakriterinis ūkininkavimo santykinio efektyvumo vertinimas Europos Sąjungos valstybėse narėse

Assessment of economic sector efficiency is of high importance when making strategic decisions at any management level. This study, hence, is aimed at proposing a new framework for multi-criteria assessment and comparison of farming efficiency. In order to achieve this aim, statistical analysis, the multi-criteria decision-making method Multimoora, and data envelopment analysis were employed. Farming efficiency was evaluated across the EU Member States on the basis of data from the Farm Accountancy Data Network. The proposed indicator system covers the three ratios, which virtually describe the other six input or output indicators. The EU Member States were ranked according to the indicator system by the Multimoora method. The application of DEA enabled to identify the main factors of inefficiency. Attention was focused on the three Baltic States and Poland. The analysis suggests that farming efficiency in these states is below the EU average. More specifically, land productivity should be increased in the Baltic States. Moreover, the increased crop output would enable to achieve the efficiency frontier. The future challenges for the agricultural development of the Baltic States are discussed in the paper

Indeksinio išskaidymo analizės modelis strateginių pokyčių identifikavimui: javų derlius Lietuvos apskrityse

In this study we analysed harvest of the two broad groups of grain crops, namely winter and spring cereals. The aim of the research was to develop and apply an index decomposition analysis model for assessment of changes in crop harvest. Consequently, we defined four factors quantifying effects of amount of cropped area, crop spatial distribution inter counties, species mix within counties, and yield. This study focuses on changes in harvest of grain crops, namely winter and spring cereals, in Lithuania. These two broad categories were analysed at the county level throughout the period of 2000–2010. The area effect was the driving force causing increase in cereal harvest. The structure effect (re-location of crop areas among different counties of Lithuania) resulted in increase in total cereal harvest. The alteration of proportions of the area under different cereal species in certain county lead to positive species mix effect. Hence, these decisions can be considered as rational ones. Nevertheless, unfavourable climatic conditions lead to decrease in yields, i. e. negative yield effect was observed. Further modifications of the proposed model, hence, might be useful tools for decision aiding