Differentiate Patterns of Individually Perceived Quality of Life in Big Cities, Towns and Rural Areas

Generally, it is agreed that QoL differs in diverse local places. Explaining these differences remains a complex and challenging problem. Scientific research has concluded that the impacts of socio-economic circumstances in local places (measured by social indicators) on subjectively perceived individual QoL are not direct, but complex, versatile and diverse. Objective differ-ences of socio-economic factors in local places suggest that personal characteristics as well as other objective and subjective QoL factors, specific to individuals, could form some common patterns, able to disclose the differences in individually perceived QoL in different socio-economic conditions and diverse local places, e.g. cities, towns, or rural areas. The research aims at investigating differences in the patterns of individual subjectively perceived QoL in diverse local places. The differences of the considered local places are reasoned by the indicators of objective socio-economic conditions in cities, towns and rural areas. The tested presumption is that common patterns of individual subjectively perceived QoL differ, respecting objective socio-economic factors in local places. The research concludes with the discovery and discussion of statistical regression models to predict individual subjectively perceived QoL in towns, big cities and rural areas. Residents' attitude to social environment, their abilities to deal with important problems in life, household total income and age are used as predictors to explain the differences of individual subjectively perceived QoL. The case of Lithuania is being investigated, using European Social Survey (round 6, 2013) data

Cause-effect relationships between objective and subjective measures of quality of life in Lithuania municipalities

The Quality of Life (QoL) in the local place concept anticipates that overall individually perceived QoL is being constituted both by objective local living conditions as well as subjective individual experience and evaluation of these local place's external QoL factors. However, the mathematical-statistical research based scientific evidences about some certain context related direction and strength of relationships between objective local place related conditions (as a cause side) and subjective QoL perception (as an effect side) are lacking. By taking into account specific characteristics, i.e. the extent and nature of the data available to measure the objective and subjective QoL, the article explores methodological possibilities to model statistically probable cause-effect relationships established and empirically observed in Lithuania municipalities’. Such statistical probability based cause-effect models would be extremely valuable in building and justifying empirical observation based and any single municipality local place context relevant QoL improvement strategies

Competitiveness of Lithuanian manufacturing industry

Nations and their industries (by the activities of certain enterprises) compete globally to sustain and increase their standard of living. International competitiveness is however a multifaceted concept, which can lead to differences in measurements and definitions. The conference paper is aimed at the analyses of competitiveness of Lithuania's manufacturing industry among European Union member state countries using several methodologies and corresponding measures. The research is based on the quantitative statistical data analyses. Methodological approaches used to analyse competitiveness of industries are summarized, some characteristics and limitations of measuring competitiveness by the quantitative data analyses are discussed. Theoretical considerations of competitiveness and related conceptual issues are however not considered in the paper

Regiono vystymo modelis skirtingų veikėjų interesų aspektu

Cooperation, collaboration and networking of social-economic actors positively affect the development processes of regions. Shared actors' interests facilitate common actions leading to collaboration and cooperation. Interests of actors are important factors driving social-economic development of regions. The aim of the article is to theoretically consider the factors that influence stakeholders' possibilities to relate their actions lead by theirs own interests to the goals and strategies of social-economic region development. The model of social-economic structure of region favourable for stakeholder interest based regional development is dealt with in this article

Quality of life peculiarities in Lithuania regions

Gyvenimo kokybė – tai tema, nagrinėjanti teorinius ir empirinius klausimus apie tam tikrų žmogaus poreikių patenkinimą, kurie apibūdinami tam tikrais iš anksto numatytais kontekstais arba sritimis. Vietinis potencialas remti, palaikyti ir pildyti adekvačius žmogaus poreikius kartu su atitinkama individo faktinio šių poreikių patenkinimo patirtimi (išmatuota subjektyviai) suformuoja su tam tikra vieta susijusios gyvenimo kokybės reiškinį. Straipsnio tikslas yra empiriškai atskleisti gyvenimo kokybės ypatybes Lietuvos administracinėse apskrityse. Empiriniai matavimai, atlikti naudojant maždaug 10 Lietuvos apskričių duomenis apimančius 2008–2010 metus, leidžia daryti tokias išvadas: Lietuvos apskritys yra labiausiai lygios pagal subjektyviai išmatuotą socialinę ir emocinę gyvenimo kokybę (t.y. gyvenamosios vietos reputaciją ir aplinkos draugiškumą pagal triukšmą); didžiausi skirtumai pastebėti matuojant gyvenimo kokybę pagal objektyvią ir subjektyvią materialiąją dimensiją, o taip pat objektyvioje fizinėje ir produktyvioje ir objektyvioje socialinėje ir emocinėje gyvenimo kokybės dimensijose. Apskritys, kuriose nustatyta aukštesnė objektyvi materiali gyvenimo kokybė (išmatuota grynųjų pajamų kintamąja), t.y. didesnės pagal gyventojų skaičių, yra apibūdinamos kaip turinčios žemesnę gyvenimo kokybę pagal subjektyvią materialią gyvenimo kokybę, ir objektyvioje socialinėje ir emocinėje srityje matuojant registruotų kriminalinių nusikaltimų skaičių.Quality of life (QoL) subject is dealing with theoretical and empirical questions about fulfilment of certain definite human needs, which are termed in some predefined contexts or areas. Locally built potential (described in objective terms) to support, sustain and fulfil adequate human needs along with respective individual experience about actual fulfilment of these needs (measured subjectively) forms the phenomenon of local place related quality of life. The article presents results of measurements of QoL in Lithuania regions (i.e. administrative counties). Set of social indicators, data on which is provided by Lithuania Statistics Office, is being used. Selection of particular indicators to be used is reasoned and argument by following conceptual QoL framework that incorporates five core QoL domains (i.e. material, social, emotional, physical and productive QoL). Objective and subjective sides of core quality of life domains are measured. Subjective evaluation of residents’ ability to make their end meet is seen as influenced by objective locally existing potential and its subjective perception. The article reveals specific QoL differences in Lithuania counties. These differences are areas to be addressed by socio-economic development strategy decision-makers

Industrial preconditions for smart specialization of Lithuania regions

The main focus of this article is on concept of smart specialisation. The analysis justifies the industrial structure of the state and its regions as one of the most important preconditions for smart specialization while choosing priority sectors to specialise. The importance of analyses of the industrial structure has been argued. The article aims at highlighting characteristics of Lithuania regions’ industrial structure as important preconditions for smart specialization. During the regional level analysis of selected industrial sectors (food, clothing, furniture, and rubber and plastic products) and their interfaces with directions of Lithuanian smart specialization strategy has been observed that, the same industry localized in a different regions have different characteristics (measured in terms of productivity, efficiency, value-added, a critical mass of companies, etc.). So the article concludes that the smart specialization strategy and its implementation in different regions would lead to different results by different priority sectors. This must be taken into an account in the preparation and implementation of smart specialization strategies at regional and state levels. The analysis of regional industrial structure allows to assess the challenges facing the industry sector operating in specific regions, thus creating preconditions for the solution of problems through smart specialization strategy for supporting innovation and scientific research. Based on the research of regional industrial structure the insights highlighting Lithuanian smart specialization directions are provided.Sumanios specializacijos darbotvarkėje numatoma, kad, siekiant pažangos ir ekonominio konkurencingumo, regionų ekonominės veiklos struktūros transformacija, teikiant politinę paramą ir skatinant investicijas, turi būti orientuojama į prioritetines veiklos sritis, grindžiamas inovacijomis, teikiančias konkurencinius pranašumus ir išskirtinį ekonomikos augimo potencialą. Šių prioritetinių veiklos sričių išskyrimo metodai ir kriterijai gali būti mokslinės diskusijos objektas. Straipsnyje aptariama šalies ir jos regionų ekonominė specializacija – industrinė struktūra kaip viena iš svarbių sumanios specializacijos prielaidų, kuri taip pat gali būti naudojama konkrečių ekonominių veiklų prioritetiškumui pagrįsti, sumanios specializacijos kryptims parinkti ir šių veiklos sričių vystymo strateginiams sprendimams pagrįsti

Setting the Grounds for the Transition from Business Analytics to Artificial Intelligence in Solving Supply Chain Risk

As supply chains (SCs) become more complex globally, businesses are looking for efficient business analytics (BA), business intelligence (BI), and artificial intelligence (AI) tools for managing supply-chain risk. The tools and methodologies proposed by the supply-chain risk management (SCRM) literature are mostly based on experts’ judgments, their knowledge, and past data. The expert evaluation-based approach could be partly or fully replaced by AI solutions, increasing objectivity, impartiality, and impersonality, reducing sources of human mistakes, biases, and inefficiencies in SCRM. However, the transition from BA to AI in SCRM is not a self-contained process; though attractive as a vision, it is not straightforward as a management or implementation process. The purpose of this research is to explore and define the conceptual grounds for transitioning from BA to AI in SCRM. The conceptual SCRM structure, its AI suitability, and implementation terms are defined theoretically based on a literature review. A single, in-depth business case study is employed to explore the theoretically defined terms of AI-based SCRM implementation. The proposed conceptual AI-suitable SCRM structure is defined by five principal building blocks: risk events, risk-event indicators, data-processing rules and algorithms, analytical techniques, and risk event probability forecasts. The study concludes that the business environment meets AI-based SCRM-implementation terms of data existence and access. Since data on risk events and negative outcomes are limited for machine learning, experts’ experience and knowledge might be utilised to build initial rules and data-processing algorithms for AI