multiple criteria evaluation of entrepreneurship

MULTIPLE CRITERIA EVALUATION OF ENTREPRENEURSHIP DEVELOPMENT IN NEWLY EU COUNTRIES

Algis Zvirblis, Antanas Buracas International Business School at Vilnius University Saulėtekio al. 22, LT-10225 Vilnius, Lithuania.

2012


Contents

About authors

3

Abbreviations Keywords, JEL classification

3

List of schemes

4

List of tables

5

Preface Chapter 1. Identification of research and evaluation problems

6

4

Chapter 2. Analysis of business advancement indicators: country competitiveness approach

8 14

20 Chapter 3. Multiple criteria evaluation technique 24 Chapter 4. Evaluation of SMEs competitive abilities in newly EU countries 24 4.1. Conceptual Provisions 25 4.2. Technique of quantitative assessment 27 4.3. Background multicriteria evaluation models and pillars 4.4. The case evaluation: Estimating Lithuania’s entrepreneurship competitive 28 advantage 32 4.5. Simplified evaluation of competitive ability determinants 36 Chapter 5. Assessing the SMEs macro surrounding favorability 36 5.1. Main principles and models of quantitative evaluation of business macro surrounding 41 5.2. Assessing the Lithuania’s business macro environment: case of new construction companies 5.3. Assessing socioeconomic factors influencing Lithuania‘s productive 45 sector enterprises Chapter 6. Complex evaluation of SMEs financial potential 52 6.1. Principles of complex evaluation 52 6.2. Basic evaluation models for integrated financial potential groups 55

Chapter 7. Estimation of the development level of goods markets in Baltic States

58

7.1. Model of consolidated evaluation 7.2. Typical primary indicators specifying the market components and results of the consolidated evaluation: Lithuania‘s case Chapter 8. Reasoning of the entrepreneurship development decisions. Conclusions and recommendations References Annotation

58

2

59 62 66 70 74

ALGIS ZVIRBLIS, ANTANAS BURACAS

ABOUT AUTHORS

Algis Zvirblis Tel. +370 5 2441829 E-mail: [email protected] Professor in Economics &Management, International Business School at Vilnius University Author and co-author of more than 50 research papers, 2 monographs. Visiting lecturer in Sweden. Research interests: forecasting models in economics and business finance, marketing control efficiency theory, quantitative evaluation methodology of social processes, including national entrepreneurship development.

Antanas Buracas Tel. +370 684 68242

E-mail: [email protected] Professor in Intellectual Economics & Banking, International Business School at Vilnius University and Lithuanian University of Educational Sciences. Author of Reference Dictionary of Banking and Commerce (1997-2010, 5 vol.) a/o scientific books and articles in metaeconomics, regional multisectoral forecasting, social infrastructure, economic terminology. Ed.-in-chief, the scientific journal Intellectual economics; vice-chairman of editing board, Universal Lithuanian Encyclopedia (21/25 vol.).

Abbreviations AHP Analytical Hierarchy Process ARAS Additive Ratio Assessment COPRAS COmplex PRoportional ASsessment DSS Decision Support System MADM Multiple Attribute Decision Making MCDM Multiple Criteria Decision Making MODM Multiple Objective Decision Making MOORA Multi-Objective Optimization by Ratio Analysis MULTIMOORA Multi-Objective Optimization by Ratio Analysis plus Full Multiplicative Form PEST Political, Economic, Social, and Technological analysis PESTEL Political, Socio-cultural, Technological, Economic, Environmental, and Legal factor analysis SAW Simple Additive Weighting SCA Sustainable competitive advantage SWOT Strengths, Weaknesses/Limitations, Opportunities, and Threats TOPSIS Technique for Order Preference by Similarity to Ideal Solution VIKOR Compromised classification ranking and solution obtained with the initial (given) weights WEF World Economic Forum 3


Keywords: entrepreneurship competitive advantages, business macro surrounding, primary determinants, determinant pillars (groups), multiple criteria evaluation, quantitative assessment, advancement index, SAW and COPRAS methods.

JEL classification: C39, C82; L26; L52, O16.

List of schemes 1.

Comparison of Baltic States by competitiveness indicators and their changes

17

2.

Comparison of Baltic States and EU-27 by levels of innovation objectives

18

3.

Scheme of entrepreneurship level estimation algorithm

27

4.

Estimation of macro surrounding components for country’s entrepreneurship

41

5.

Algorithm for evaluation of socioeconomic factors

46

6.

Procedures of evaluation of SMEs financial potential

53

7.

Scheme of multiple criteria validation of entrepreneurship strategic development attitudes and prediction of the programmed advancement level

65

4


List of tables 1.

Baltic &selected Scandinavian States in 2007/2008 and 2010/2011 by selected primary macroeconomic indicators (Comparative analysis)

14

2.

Comparative ranking of selected newly EU Countries by primary competitiveness indicators in 2010/2011

16

3.

Innovation objectives as a percentage of innovative enterprises, 2008 (Baltic States & EU)

17

4.

Underlying pillars of typical primary indicators (not ranked)

29

5.

Reliability parameters for determinative primary indicators, by pillars

30

6.

Assessment of Lithuania’s entrepreneurship level index

31

7.

Primary determinants and estimation of total index for Lithuania by SAW method

33

8.

Primary factor identification for environment and assessment of their values and significance of influence, and concordance coefficients W

43

9.

The evaluation of components according to specific combinations of primary factors

44

10. The evaluation of marketing environment level index according to comparative variants (compositions of components)

45

11. Scenarios of separate indicator (indices) groups and general scenarios of Lithuanian socioeconomic environment

48

12. Qualitative and quantitative assessment for identified indicator groups of socioeconomic environment

50

13. Level index of socioeconomic environment according to general scenario variants

51

14. Totality of basic & partial criteria, and primary financial & other indicators and sub-

55

criteria 15. Main reliability parameters by expert assessment (W and χ2 values)

60

16. Lithuania’s good market, competitive advantage indicators, and determination of component level index by SAW method

60

17. Lithuania’s good market dominance, tariff barriers, financial indicators, and determination of component level index by SAW method

61

5


Preface

The study presents theoretical framework and empirical viewing of complex (qualitative and quantitative) evaluation of the country’s entrepreneurship development, of SMEs finance management potential as well as of business macro surrounding favorability and the functioning level of the country‘s markets based on multiple criteria evaluation methodology. The results of performed comparative analysis of the global economic competitiveness indicators in Baltic & Nordic countries (with account of WEF data) was oriented to the perspectives of business control systems in newly EU countries. The essential assessing stages of the national entrepreneurship competitive advantages are the examination also quantifiable assessment of identified primary indicators, the establishment of indicator pillars indexes (using Simple Additive Weighting (SAW) method) and determination of entrepreneurship competitive advantages index (using Complex Proportional Assessment method) on basis of the background evaluation models (proposed by authors). Such idiosyncratic indicator pillars were selected: competitive advantage for goods and services, transformation for their markets and SMEs working effectiveness indicators. The different significance parameters of the determinants influencing entrepreneurship advantages and the country’s economic competitiveness are taken into account. Evaluation results using Lithuania’s data as a typical case are presented. The quantitative evaluation principles and basic evaluation models (oriented on constructed determinant pillars) for functioning level of goods markets as well as for SMEs finance management potential are developed. The formalization of business surrounding (interrelations of macro factors and components) is the basis for complex (qualitative and quantitative) assessment and establishment (using multiple criteria technique) of general favorability index. SAW method and adequate assessment models may be applied for determining the component indexes (with regard of significance of each macro factor); on this basis, the general surrounding index has been established by additive proportional assessment method. Assessing the macro surrounding (according the forecasted changes scenarios) in Lithuania‘s case was performed. The viability of the presented quantitative evaluation methodology is in possibility to apply it for determining main decisions of country entrepreneurship development strategy.

6


7


Chapter 1. Identification of Research and Evaluation Problems The increase in competitive advantage is the strategic priority of the economic development in the newly EU member countries. The transformation processes in general are an important part of the economic development of a country with a small open economy, also of business macro-environment as well as enhancing entrepreneurship development oriented to the advanced growth. The significance of the investigation and estimation of the indigenous entrepreneurship development level as well as predicting future trends in different developing countries - EU members may be emphasized in few aspects. The expanding of the state economic competitiveness is one of its strategic tasks when designing and evaluating an acceptability of the national entrepreneurship development strategy. The favorable factors of business macro surrounding, on the one side, substantially influences the growth of national economy. On the other side, the research of these factors is important for the business entities so as reveal premises for avoiding the threat of going environmental dynamism and new competitive abilities of certain business. The entrepreneurship development has also to be analyzed in the context of a country’s integrated competitive advantage, in particular, on basis of the country’s competitiveness evaluations according to the World Economic Forum (WEF), which determines so-called competitiveness pillars (The Global…, 2009/10, 2010/11). They include many significant primary and integral advantage and disadvantage indicators determining the level of state macroeconomic and entrepreneurship development. It is insufficient to propose the comparison of these indicators; therefore a part of them reflects the entrepreneurship advantage and/or disadvantage. However the WEF methodology provides the using predetermined weights of the primary indicators (PI) and not allows to adequate differentiation of significances of the any PI by assessment of the economic competitiveness pillars as well as of the Global Competitiveness Index. The investigation and assessment of the macroeconomic advancement of newly EU countries is important to incorporate into the macroeconomic strategy-making when validating the strategic decisions of economic development and its ex-post situation; it is also relevant for the business entities. The evaluation results may be used for the preparation and monitoring of implementation of the national economic development program. The expanding of the state economic competitiveness is one of its strategic tasks when designing and evaluating acceptability of the national entrepreneurship development strategy. The favorable factors of business macro surrounding, on the one side, substantially influenced the growth of national economy. On the other side, the research of these factors is important for the business entities so as reveal premises for avoiding the threat of growing environmental dynamism and / or of new possibilities to use the competitive abilities of certain business. The scientific publications concerning the problems under review and focused on the analysis of substantive macroeconomic problems, emphasize the expanded economic models, theorize on the economic growth effects, related mathematical techniques, when analyzing the financial and marketing aspects of the system, sustainable development concept of regions, etc. The studies examine simple monetary and fiscal policy, level of government debt under certain conditions (it is not possible to infer how strongly the monetary and fiscal instruments should be used, without explicit reference to the level of government debt), and their changes impact to economic development. In this case, the interconnections between the competitiveness level and financial stability of the sectorial enterprises are important (Allen, Gale, 2004). The significant indicators characterizing the development of the financial a/o infrastructure sectors were established, which have the strong correlation with the GDP per 8


capita in the countries (foremost Baltic States) within formed EU financial system (comparable). It is shown that indicators of the global environment and infrastructure surrounding - are also significant in the structure of economic and financial system. The investigations of the institutional factors on the national economic growth were also done for some selected transitional economies (Yusuf, Ngomori, 2002; Gries, Naude, 2010). The enhancement of sustainable macroeconomic development in the newly EU Member States first of all must be oriented to the effective employing of national resources pursuing the growth of national economic competitiveness and at the same time - to coordination of its economic development strategy with the EU policy both common and regional (Ma, 2004; Siggel, 2006; Beck, Laeven, 2006; Brauers et al., 2007; Grundey, 2008; Rutkauskas, 2008; Naude, 2010). It means that entrepreneurship development strategy also must take into account the expected new competitive advantage-oriented changes and their effective determinants. Theoretical as well as empirical research works examine factors having an impact on sustainable economic development in newly EU countries; highlight the importance of knowledge factors for long-term economic growth (Grundey, 2008; Gries, Naude, 2010). Those papers also assert that sustained investments in education, information and communication technologies, innovations as well as in a favorable economic and institutional environment will lead to increases in the use and creation of knowledge in economic production, and consequently result in sustained growth of economic competitiveness. In many publications the importance of systemic conceptual approach to the sustainable development of knowledge economy and its complex evaluation was argumented, the importance of knowledge potential development for long-term economic growth highlighted (see: Buracas, 2007; Melnikas 2008). The authors detailed the knowledge economy framework asserting that sustained investments in education, innovation, information and communication technologies, and a conductive institutional environment which lead to increases in the knowledge impact on the economic production, as consequently result in sustained economic growth. Some publications detailed the influence of human resource management on competitivity, formation of individual competencies on strategic development, some of them on problem aspects of innovation efficiency evaluations (Geoff et al., 2009). This methodical approach was critically discussed, especially the validity of indices used to measure national economic performance and competitiveness. As a result, a review of related researches has shown that the complex assessment of the country’s knowledge-based economy determinants is not detailed enough analytically. Research works in detail investigate various theoretical aspects of the development of business (entrepreneurship) as well as SMEs in order to employ its advantages. The researchers accented mostly the entrepreneurship development at the state level in view of SME activities’ impact on the country’s economy. SMEs working effectiveness (with account of activating integrative processes and dynamic changes of entrepreneurship development) is important since they create the significant part of the GDP in the newly EU states. The investigations of the corruption impact and other institutional factors on the national economic growth were also performed for some selected transitional economies. The separate significant environmental indicators are analyzed mostly revealing the impact on business enterprises working effectiveness (Avlonitis, Salavou, 2007; Adekola et al., 2008; McGee et al., 2009). The published research investigated how the SMEs were integrated into the holding structure, the processes of related diversification and internationalization, also the integration of activities as well as the separation of closely linked activities that improve the entrepreneurial efficacy (Lechner, Leyronas, 2009; McGee et al., 2009). Themes entrepreneurship and structural economic transformation as well as institution building and 9


growth in transition economies, the impact of clusterization on the development of SMEs in order to employ its advantages are discussed (Beck, Laeven, 2006; Gries, Naude, 2010). Therefore, it is important to identify and evaluate the influence of clusterization level on the competitiveness in the modern service-based economies. It must be emphasized that clusters, depending on the phase of their growth and development, exercise the increasing influence over business organizations, as well as their competitive abilities, the links between the level, or degree, of clusterization and sector competitiveness (Navickas, Malakauskaite, 2009). The competitive advantages of organizations as strategic management factors are determined by many things including dynamic changes in business surroundings and various patterns of relationship between competitive advantage and entrepreneurship development. Some papers are oriented to a study of the marketing capabilities role in innovation-based competitive strategy, also to the establishment and accumulation of dominant advantages, appliance of their totality as well as to a demand-based perspective of sustainable competitive advantage (Ma, 2000b; Adner, Zemsky, 2006; Porter, 2008). Other publications deal with aspects of entrepreneurship and SMEs development in the context of the key factors affecting countries in the specific region (Fairbairn, 2006). They can be interconnected with competitiveness of the goods and services what is one of the most important of SMEs marketing functions and the significant stage in enterprise marketing research, kinetic and positional competitive advantage and synergy effects (Ma, 1999, 2000b; Fleisher, 2003; Smith, 2003; Kotler, 2003; Simmons et al., 2009; Kotler, Armstrong, 2009). The published research works investigated how the SMEs integrated into the holding structure, the processes of related diversification and internationalization, also the integration of activities as well as the separation of closely linked activities who improve the entrepreneurial efficacy (Lechner, Leyronas, 2009; McGee et al., 2009). The various theoretical aspects of innovation implementation in SMEs were also analyzed in the empirical studies more attention given to the efficiency of social capital employment, also risk management as a factors determining competitivity (Avlonitis, Salavou, 2007). The corporate social responsibility (CSR) in business (entrepreneurship) strategy is revealed as priority what is important when measuring the created social value. The studies deal with influence of goods and services quality, new products diversification level, production and export of high-tech goods, innovations in production, marketing strategy, intellectual and social potential, their implementation in enterprises respectively improve the entrepreneurial efficacy (Zvirblis, 2003; Avlonitis, Salavou, 2007; Philip, 2007; Krisciunas, Greblikaite, 2007; Buracas, 2007; Adekola et al., 2008; Parada Daza, 2009; McGee et al., 2009; Iturrioz et al., 2009; Drejeris, Zinkeviciute, 2009). Authors noticed that knowledge economy principles are influencing the changes in the entrepreneurship, in particular, its modernization, innovativeness, social responsibility, taking SMEs in Lithuania as a case. However, much entrepreneurship studies are fragmentary and focused narrowly on the essential aspects of entrepreneurship, according to our opinion and opinion of some other authors (Anderson, Starnawska, 2008). It is insufficient to focus the empirical research on the problem of complex investigation and the assessment of entrepreneurship development, revealing the priority aspects of both the state institutions and the business entities also the associative structures. Authors of many papers discussed the applying of composite economic development indices, i.e. aggregate measures. In terms of complex measurement technique, the multidimensional composite (mainly quantitative) indices are generally additive ones with equally weighted components consisting of variables selected in an ad hoc manner. The composite indices are relatively flexible, because changes in selection, scaling, weighting and aggregation can be effected readily. However the comparative application of these indices of development over space and time remains problematic. It is important to evaluate more adequately the differences in the newly EU countries, to apply estimated rather than predetermined weights of primary indicators, and the more adequate differentiation of 10


significances levels for the PI and their pillars. The same opinion is expressed by other researchers (Bowen, Moesen, 2009; Bruneckiene, Paltanaviciene, 2012). We support the similar position of T. Man, et al. (2008), P. Misztal (2009) concerning a variety of multidimensional composite (mainly quantitative) indices of economic development representing aggregate measures of complex development. In terms of method and technique (complex measurement construct), composite indices, furthermore, are generally additive ones with equally weighted influence. The variety of essential primary indicators (enhancing or minimizing the competitive advantages) describing country’s competitiveness determines the required quantitative evaluation methods. It will be observed that an assessment may comprise the scenarios interpreting the government macroeconomic policy trends, strategic perspectives of national economic also business development and must be adequately formed for newly EU countries. Moreover, the provided quantitative examination methodology foresee the different influence of primary factors (compatible with qualitative – SWOT - analysis also scenario method) as useful methodical tool is concerned by the adaptation theoretical background. Thus, a review of related researches has shown that relevant studies applied the complex assessment of the country’s economic development determinants and a system of integral measurement of the sustainability of macroeconomic development. By holistic approach to the influence of totality macroeconomic factors and their influence hierarchy on macroeconomic as well as entrepreneurship development in countries - newly EU member countries, the adequate reasoned models must be developed, in particular, with account of specificity of quantitative evaluation methods to be used. When motivating the understanding both of competitive advantage and strategic marketing, it is relevant to define their applicability for theory building and testing in the process of strategic management with account of the value priorities (Vasiliauskas, 2007). In recent years, considerable debates on the role of marketing in competitive strategy were continuing. It is suggested that marketing managers observe the relative importance of these value levels in order to apply True Marketing in a strategic sense. Besides, there are only few researches dedicated to the complex evaluation of those essential advantages of entrepreneurship especially in the newly EU countries and to their assessment revealing the priority aspects of the state institutions, business entities, also associative structures. R. Dolphin (2004) attempted to integrate the concept of sustainable competitive advantage (SCA) as implementing the value-creating and resource-based management strategy not simultaneously duplicating its benefits. This author have elaborated some specific skills and topics in strategy research resources and, as a result, proposed a theoretical model of some relationships within the macro factors affecting the multi-item measure of SCA construct in the network environment and attempted to operationalize it. The research contributes to strategic marketing theory and practice by developing, refining and validating the measures of entrepreneurship, marketing capabilities, organizational innovation and SCA constructs. Some researchers argue that important strategic marketing developments were in this direction of SCA (Gao, 2010). In recent years, the considerable debates on the role of marketing and management as well as risk management in competitive strategy were continuing (Rutkauskas, Ginevicius, 2011). Their effective strategy must increase the efficiency of business value added creation, its downstream and upstream sources and, coherently, determines a wide spectrum of the factors to be analyzed and adequate methodological potential. In a competitive market for takeover bids, by J. Madura and T. Ngo, their premium serves as an effective proxy for the expected synergy which is primarily related to the premiums paid in other recent takeovers in the same industry. The some variation in expected synergies among takeovers can be explained by the premiums derived from recent takeovers in the same industry than by all bidder- and target-specific characteristics combined: the bidder valuation effects are inversely related to the premium paid for targets, implying that abnormally high premiums may reflect 11


the overpayment rather than abnormally high synergies (Madura, Ngo (2008). Other aspects of business development are also important, it is why various models of SMEs production quality management and their interaction with knowledge management models are analyzed by some authors, and both the criteria system of quality evaluation and possibilities of the multiple criteria method application is proposed and discussed. The complex investigations of macro environment are necessary to validate the decisions when implementing the concept of sustainable business development and making the strategic decisions environment-friendly (environmental management). Among them is the research (and evaluation) of marketing political (and/or legal), economic, social and technological environment increasingly important due to the specific dynamic changes of environment components, by determining new opportunities and threats. It helps to reduce negative effect of environment changes and often to use these changes (together with the revealed new opportunities) to acquire (or retain) competitive advantage of a company (Ma, 2000 b; Fleisher, 2003; Kotler, Keller, 2006). It is also important to underline, as revealed by Smith (2003), that the research of marketing environment in general help a companies to improve the effectiveness of the value added development. The marketing researches fall to the category of so-called downstream sources of the value added. This fact, in turn, determines a huge variety of analyzed indicators, thus it correspondingly requires a sophisticated theoretical and methodological potential. Certainly, the qualitative analysis of marketing environment components (both micro and macro) as well as economic and social environment is the most frequent between the marketing research and evaluation methods. It can be considered rather as a certain initial stage of quantitative evaluation. The following qualitative methods must be mentioned in the review of the analysis methods: PEST (Political, Economic, Social, and Technological analysis), PESTEL analysis (systematically including Political, Socio-cultural, Technological, Economic, Environmental, and Legal factors), also environment dynamics and scenario analysis. The qualitative analysis is also related to the SWOT analysis, which reveals the company’s opportunities and threats interconnected with significant external factors. Firstly, it manifests itself from the strategic perspective (either in its expansion or narrowing). It also improves the opportunities to strive at compatibility of strategic marketing management decisions (both at their formation and implementation stages) with socioeconomic indicators and its changes. When analyzing the environment of productive companies, it is important to distinguish trends, to determine the macroeconomic and institutional indicators influencing export strategy of a company as well as the forecasted market’s potential. Below is highly stressed how promising the quantitative evaluation is in general; therefore, the objectives of its application in evaluation of macro environment components are also relevant. After all, only this evaluation (applying quantitative methods and creating algorithms for the evaluation process) may be incorporated into the general system of evaluation of strategic marketing management decisions (Zinkeviciute, 2007). The acquisition and preservation of the competitive advantages as a problem of strategic management depend of macro factors and dynamic environmental changes. The investigation of entrepreneurship competitive advantages and their evaluation is actual both for enhance of state economic competitiveness as well as for making strategic entrepreneurship marketing decisions. These questions are analyzed insufficiently in the special scientific publications under review, however, it is not enough of studies dedicated to the complex evaluation of the problem, and the adequate quantitative evaluation methodology is still not applied in this field. The assessment of competitiveness as an exceptional phenomenon, remains between the most pressing problems of management and marketing research - their theorists are still debating the multi-aspect terms of content, the essential characteristics of the measurement (in particular, sees the cross-identification) techniques, as well as for competitive advantage in 12


the acquisition of the underlying conceptual framework. The fundamental of the country (region, sector, business unit - cluster) competitivity factors (primarily - economic) affecting the formation of patterns was published by M. E. Porter (2008) but is not enough of studies for such competitive examination at the enterprise level. Nevertheless, the determinants of economic competitiveness have probabilistic aspect (by increasing the influence of a country or region's overall competitiveness) however deterministic approach also allows, in principle, to address some tasks of the relative competitiveness assessment (Aiginger, 2006; Chikan, 2008; Rutkauskas, 2008). The authors provided a theoretical framework and empirical viewing, first-of-all, for the solving of the problem to be defined on basis of the general evaluation criteria and determined by a totality of essential determinants to be structured by specific attributes, adapted for newly EU countries and oriented to the perspectives of control systems (by applying the reasoned multiple criteria evaluation methods on the basis of the designed models). Object of this research is the entrepreneurship advancement in the newly EU countries, also business macro surrounding. Task of research is to develop the main principles (theoretical and measurement framework) of complex evaluation of the country’s entrepreneurship development, to describe the determinant pillars of country’s entrepreneurship competitive advantages and to design the basic quantitative evaluation models for their complex measuring and evaluation. Primary indicators must be structured by specific attributes, adapted for newly EU countries and formalized for the quantitative evaluation oriented to the perspectives of multiple criteria decision making systems (MCDM). Research methods used in the publication below: a systemic review of scientific publications, multi-aspect analysis of macroeconomic indices, multiple criteria evaluation by COPRAS and SAW methods. The originality of research results consists in the constructing of basic valuation models for complex assessment of the entrepreneurship competitive advantages as well as for business macro surrounding components by applying the multiple criteria evaluation methods, their application in the case studies. It is applicable for countries - newly EU member countries in various possible conditions and solutions. The viability of the presented evaluation system is determined by the fact that this quantitative evaluation technique may be applied for determining the acceptance of main parameters of country entrepreneurship development strategy.

13


Chapter 2. Analysis of Business Advancement Indicators: Country Competitiveness Approach Comparison of the selected macroeconomic, economic competitiveness as well as business advancement indicators in the newly EU countries was performed with a task to reveal the essential primary indicators. When analyzing the country macroeconomic development determinants, the indicators of global country’s competitiveness index (according to WEF practice) must be taken into account (The Global.., /2008; 2011) by simplified way. 12 competitiveness pillars were composed on basis of typical primary indicators and evaluated by experts (scores in WEF evaluating system: 1- the worst; 7 points – the best possible). The pillars integrate also the indicators having influence on country‘s economic competitiveness, actually institutional, macroeconomic stability, financial markets development, business sophistication, etc. indicators. The results of comparative analysis for 2007/2008 and 2010/2011 and ranking the Baltic States according to main selected indicators of global competitiveness index (comparable with selected Scandinavian countries) are presented in Table 1. Table 1. Baltic & Selected Scandinavian States in 2007/2008 and 2010/2011 by Selected Primary Macro Economic Indicators (Comparative Analysis) Selected primary economic competitiveness indicators*

Gross domestic product per capita Inflation** Government budget balance** Burden of government regulation***

Lithuania

Latvia

Estonia

Finland Denmark

Score

Score

Score

Score

Score

Score

Score

Score

2008

2011

2008

2011

2008

2011

2011

2011

72/5.8

86/4.2

116/10.1 75/3.3 81/6.6

18/0.1

45/1.6

41/1.3

18/-0.1

16/0

10/4.3

26/3.8

75/-1.2 48/3.5

…

124/-8.9 115/ 22.7

76/3.1

125/-8.9 29/2.8 29/-1.7 88/3.1 10/4.5

7/4.4

Government debt**

21/17.3 46/29.3

12/9.7 63/36.1 5/2.9 5/7.2

93/52.6

90/51.8

National savings rate **

96/15.8 93/15.9 108/13.6 25/30.2 79/19.9 47/24.1 82/18.1

60/21.7

…

64/52.7

Extent and effect of taxation

63/3.6

126/2.7

Total tax rate****

81/48.3 75/42.7

27/32.6

Pay and productivity***

23/4.8

18/4.7

Prevalence of trade barriers***

50/4.8

Interest rate spread** Ease of access to loans

Country credit rating**

…

80/45.1

…

56/57.1

7/90.6

8/90.4

69/3.5 117/2.9 13/5.2 18/4.3

114/3

130/2.6

44/33 85/49.2 98/49.4

93/47.7

27/29.2

30/4.7

42/4.3

8/5.2

69/3.9

27/4.5

64/4.7

20/5.5

29/5.2

15/5.7 14/5.6

8/5.9

31/5.1

4/1.5

13/1.9

61/4.8 101/8.2

8/2.1 51/4,6

5/0,9

36/3.2

54/3.6

112/2.2

63/3.4

125/2

16/4.6

5/4.5

28/3.5

76/5

99/4.5

33/5.7

63/4.9

35/5.7 48/5.1

23/5.6

44/5.2

25/5.8 43/5.1

12/6

28/5.6

8/5

50/3

Prevalence of foreign ownership*** Availability of financial services/financial market sophistication Soundness of banks***

66/4.3

74/4.5

55/4.5

86/4.3

41/6.1

87/4.8

62/5.7

127/3.9 25/6.4 72/5.2

11/6.3

85/4.8

Capacity for innovation***

52/3.3

48/3.3

71/3

57/3.1 40/3.6 34/3.6

5/5.6

9/4.9

FDI and technology transfer ***

83/4.7

105/2.9

73/4.8

103/2.9 25/5.3 92/3.1

9/5,1

20/4.6

14

ALGIS ZVIRBLIS, ANTANAS BURACAS Production process 64/3.7 51/4 65/3.6 72/3.5 39/4.3 41/4.3 12/5.7 5/6.1 sophistication*** *Composed by authors using WEF data included into included into the global competitiveness index pillars: 3rd, 6th, 7th, 8th, 9th, 10th, 11th and 12th (The Global…, 2007/2008; 2010/2011). Numerator – rank between 134 states, denominator – score by WEF experts. Score presented by WEF was not sufficient for differentiation, and rank is additional parameter. **Score as % of GDP in 2009. Country credit rating based on expert assessment of the probability of sovereign debt default on a 0–100 (lowest probability) scale.2007 & 2010. *** Burden of government regulation: 1 = burdensome, 7 = not burdensome. Pay and productivity -to what extent is pay in your country related to productivity? [1 = not related to worker productivity; 7 = strongly related to worker productivity]. Prevalence of foreign ownership - how prevalent is foreign ownership of companies in your country? [1 = very rare; 7 = highly prevalent]. Prevalence of trade barriers to what extent does tariff and non-tariff barriers limit the ability of imported goods to compete in the domestic market? [1 = strongly limit; 7 = do not limit]. Soundness of banks [1 = insolvent and may require a government bailout; 7 = generally healthy with sound balance sheets. Do exporting companies have a narrow or broad presence in the value chain? [1 = narrow, primarily involved in individual steps of the value chain (e.g., resource extraction or production); 7 = broad, present across the entire value chain (i.e., do not only produce but also perform product design, marketing sales, logistics, and after-sales services)]. How sophisticated are production processes? [1 = not at all—labor-intensive methods or previous generations of process technology prevail; 7 = highly—the world’s best and most efficient process technology prevails]. To what extent do companies use sophisticated marketing tools and techniques? [1 = very little; 7 = extensively]. How do companies obtain technology? [1 = exclusively from licensing or imitating foreign companies; 7 = by conducting formal research and pioneering their own new products and processes]. All 2009–10 weighted average. **** Total tax rate - combination of profit tax (% of profits), labor tax and contribution (% of profits), and other taxes (% of profits).

The comparison of WEF competitiveness indicators for Baltics and two smaller neighbor Scandinavian countries in 2010-2011 shown some substantial differences both between their groups and some countries revealing global situation changes, one of them – similarity of competitiveness indicators in Estonia to selected Scandinavian countries (naturally, except specific backwardness in FDI and technology transfer, production process sophistication, also state of cluster development). It differs for those groups of indicators measured by state ranks adequately about 70- 30 places. The substantial distance between Lithuania and Latvia, on the one side, Estonia, Denmark and Finland, on the other side, are those in government budget balancing, and, adequately, evaluations of government regulation burden (the comparative ranks respectively differs about 100 and even more places). At the same time, the state debt level is substantially higher for Denmark and Finland – the difference amount about 30-40 places by rank on behalf of Latvia and Lithuania. Estonia is in especially favorable situation in the world (at 5th place) with its government debt level at 7.2 proc. GDP. The extent and effect of taxation also differ Estonia from all other comparative states as having much more benevolent their influence on business competitiveness: its distance from the rank of other countries under review amounts up to 100 and more places. Estonia overruns other Baltic neighbors by its better rank also by pay and productivity, prevalence of trade barriers, ease of access to loans as well as by financial market sophistication. This fact, however, witnesses the roots of growing social differentiation esp. in the Baltic States between profits and wages. As a total, all Baltic States are on worse level by nature of competitive advantage (the distance from Denmark and Finland ranks amounted 40-50 places), influencing economic competitiveness. Lithuania overtakes other Baltic States by creating value chain breath but lag substantially behind Denmark and Finland. For many of indicators, the situation was even worsening under last financial crisis impact: p. ex., unfavorable changes in taxation policy dislodged Lithuania from 63rd rank place in the world in 2008 to 126th, and Latvia – adequately from 69th to 117th. According to FDI and technology transfer, Lithuania slept from 83rd to 105th place, Latvia – from 73rd to 103rd, and Estonia - from 25th to even 92nd (Table 1) However such important indicator as capacity for innovation ameliorated for all Baltic States. 15


The revealing of the whole of entrepreneurship competitive advantages requested to examine in detail the constructing of so-called pillars of global country’s competitiveness index according to the WEF. In the article below, first of all the comparison of selected economic competitiveness indicators determining the pillars 6, 8, 9, 10, 11, 12 and reflecting the entrepreneurship advantage or disadvantage was performed for some newly EU countries with different economic development level (Table 2). Table 2 Comparative Ranking of Selected Newly EU Countries By Primary Competitiveness Indicators in 2010/2011 Indicators

Lithuania

Estonia

Latvia

Poland

Bulgaria

Czech Republic 24/4.1

Capacity for 48/3.3 34/3.6 57/3.1 50/3.3 79/2.8 innovation Extent of market 97/3.3 38/4.2 70/3.7 39/4.2 87/3.4 15/5 dominance & sophistication Creating of value 34/4.2 58/3.7 82/3.3 39/4 90/3.2 29/4.3 chain & breath Firm level 56/5 42/5.3 89/4.5 83/4.6 127/4 36/5.4 technology absorption Production 51/4 41/4.3 72/3.5 48/4.1 89/3.2 34/4.6 process sophistication Intensity of 78/4.7 31/5.4 92/4.6 35/5.4 94/4.5 12/5.7 local competition State of cluster 105/2.9 92/3.1 103/2.9 108/2.9 112/2.8 41/4 development Pay and 18/4.7 8/5.2 42/4.3 54/4.2 58/4.1 22/4.6 productivity *Selected by the authors from: WEF, The Global…, 2010/2011, tables 6.01, 6.02, 7.06, 9.02, 11.03, 11.05, 11.07, 12.01 a/o. Numerator – rank between 134 states, denominator – score (weighed average) by WEF experts. Weighted average is indexed from 1(lower evaluation) to 7 (highest evaluation).

The specific differences may be seen in Table 2 when comparing the main competitive indicators for Baltic and Central and East Europe (CEE) countries; their ranking is mostly different between Czech Republic and Bulgaria. The certain distance between the same indicators for selected countries amounts from 91 ranking places for firm level technology absorption, 82 – for extent of market dominance and sophistication, also for intensity of local competition, 67 – for state of cluster development, 61- for creating of value chain and breath and 55 places - for capacity for innovation and production process sophistication. Czech Republic overruns all sample states esp. by capacity for innovation, extent of market dominance and production process sophistication also firm level technology absorption. Estonia overruns all sample states by pay and productivity. The main differences between comparable indicators of all Baltic States are clearly interconnected with differences in their macroeconomic situation especially last three years of continuing financial crisis in 20082010. There are some substantial differences of competitiveness and taxation indicators: for government debt adequately Lithuania - 46, Latvia 63 and Estonia - 5 places; similar better rank position of Estonia in taxation (see also Fig. 1).

16


Edited by authors on basis of: The Global…, 2010/2011, tables 6.01, 6.02, 7.06, 9.02, 11.03, 11.05, 11.07, and 12.01.

Fig.1. Comparison of Baltic States by Competitiveness Indicators and Their Changes

As imperfections of the WEF ranking, we can mention the ignorance of some important determinants of entrepreneurship advantage. The WEF experts do not present joint comparative evaluation of the states according to the totality of the substantial indicators of the economic competitiveness, also production and services diversification level and extent of marketing sophistication. It is important to evaluate more adequately (not uniformly) the differences in the newly EU countries, especially in the determinants of intellectual potential development such as impact of new technologies and innovative decisions on surplus value, efficiency of comparative investments into knowledge technologies (Table 3). Table 3 Innovation Objectives as a Percentage of Innovative Enterprises, 2008 (Comparison of Baltic States & EU) Countries

Increase of Replace of range of outdated new technologies products & services

Enter Increase new markets markets share

Improve quality of goods & services

Improve flexibility for producing goods & services

Increase capacity of producing goods & services

Reduce labor costs per unit output

EU 27 52,2 34,5 39,6 42,4 56,6 33,9 31,7 28,1 Estonia 36,5 35,8 24,1 32,3 50,8 31,1 33,9 21,3 Lithuania 30,3 26,4 26,5 32,8 42,8 26,6 27,7 28,3 Latvia 12,2 9,3 11,3 8,9 12,6 7,4 10,3 6,9 Source: Science, technology and innovation in Europe, 2011, p. 90. http://epp.eurostat.ec.europa.eu/portal/page/portal/product_details/publication?p_product_code=KS-31-11-118.

In this context the cooperation of innovative enterprises with other enterprises, universities or public research institutes is important in the EU-27. It was high in Estonia (48.6 %), but the lowest in Romania (13.8 %), Italy (16.2 %), Bulgaria and Latvia (both 16.6 %, Science, technology and innovation in Europe, p.81). 17


Graphically the connections between levels of main innovation objectives of EU-27 and Baltic States can be shown on cob-web chart (see Fig.2). It shows more substantial differences between both groups in levels of such objectives as increase of range of new products & services and entering to new markets. At the same time the significances of increasing capacity of producing goods & services and improving quality of goods & services are near identic (except Latvia which substantially diminished its innovative activity under financial crises influence at the time of data selection).

Edited by authors on basis of: The Global…, 2010/2011, tables 6.01, 6.02, 7.06, 9.02, 11.03, 11.05, 11.07, and 12.01.

Fig. 2. Comparison of Baltic States and EU-27 by Levels of Innovation Objectives

The authors attempted to apply estimated rather than predetermined weight values of competitiveness indicators, therefore the more adequate differentiation of their significances levels and to generalize critically the presented expert estimations (according to their ranking and weighed averages). At the same time, this permitted to reveal the marketing sophistication (incl. production / services diversification), etc. determinants describing SMEs competitive advantages and, what is most important, to formulate the approach to their complex evaluation based on their totality as an indivisible system.

18


19


Chapter 3. Multiple Criteria Evaluation Technique The system of multiple criteria analysis as perspective quantitative methods may be classified into following groups – ranking, classification, evaluation and optimization. They may be applied when assessment conditions are deterministic or undetermined. The multiple criteria methods are widespread esp. in the determinative solutions when the set of alternatives is defined. Such methods can be distinguished by available and applied information: Based on qualitative data, for ex., AHP (Analytical Hierarchy Process); - Based on qualitative data and not transferring into quantitative expressions, p. ex., verbal analysis; - Based on qualitative data, p. ex., utility functions, data envelopment analysis, discriminant functions, etc.; - Based on qualitative data according to criteria of different level, determination of priorities, optimization. Special attention is given below to AHP and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) mostly applied for determining the underlying alternatives (Parkan, Wu, 2000; Zapounidis, Doumpos, 2002a; Dombi, Zsiros, 2005; Ginevicius et al., 2008). The perspective multiple criteria methods of the quantitative evaluation are suggested to be reviewed in the first place as best applicable to the tasks solved below and by character of those tasks. In particular, it is preferred to apply SAW, COPRAS and compromised classification (VIKOR) methods as most widespread (Hwang, Yoon, 1981; Zhang, Yang, 2001; Zapounidis, Doumpos, 2002b; Turskis, 2008; Ginevicius, Podvezko, 2009). The application of the multiple criteria evaluation methods requests to formulate the adequate valuation criteria system (Podvezko, 2007). The evaluation of alternatives (their variants) by TOPSIS is based on proposal that priority is the nearest to ideal variant or most distant to worst of possible variants. For applying this method, any restrictions for significances of valuation criteria have not applied so as their sum not must be equal to 1. The multiple criteria method VIKOR is based on selecting from a set of alternatives in the presence of conflicting criteria by linear normalization and measurement of distance to best hypothetic alternative. The extended VIKOR method’s ranking is obtained through comparison of interval numbers and by doing the comparisons between intervals. The SAW method is especially applicable for the compound evaluation of substantially different primary criteria (both having quantitative and qualitative parameters to be measured) and determining the integral measure (the last one can be used also as subcriterial measure on different level). The choice is determined by the moment that this method is suitable in case when all factors are independent in the system and when their interaction with the integral measure is not important (as observed in the case study). By using the SAW method, the significance of every factor including various qualitative indicators is measured (they also may be differentiated according to the influence on compound measure), however, the system must finally involve only these factors (criteria) that meet the essential level of significance (Ginevičius, Podvezko, 2009). The sum of significance coefficients of all factors (criteria) in the group must be equal to 1 (or 100%) and that permit us to differentiate them by significance (however, the system of unvaried significance criteria can be also applied) and to use the adapted software. The significances of essential factor impact can be determined by calculations based on objective information or by expert way.

20


The basic model for the complex evaluation of certain determinant level (by applying multiple criteria SAW method) may be formulated by following way: i n

i n

i 1

i 1

E ( I )   ai Ei ;  ai  1,

(1)

where E(I) – determinant level index; Ei – factors influencing determinant; a i − coefficients of significance of direct influence of factors on integral measure E (I). By applying this basic model for entrepreneurship situation, the complex of essential factors must be adequately formulated. The reliability of expert evaluations is achieved by applying adequate technique with account of concordance coefficient W and parameter χ2 of its significance determined mostly by expert way (Ginevicius, Podvezko, 2009; Burinskiene, Rudzkiene, 2009). In this study, a comprehensive assessment of entrepreneurship development will focus on expert determination of factor significance:

W

12S , r (m3  m)

(2)

2

where r – a number of experts; m – number of parameters to be valued, S – sum of quadratic means of significance values deviations from expert ranks. The compatibility of expert evaluations is acceptable if significance of W is 0.7 – 0.8 (Kendall, 1979). In its case:

 2  Wr (m  1) 

12S ; rm(m  1)

(3)

COPRAS method opens the possibility to join the different primary criteria (factors) and to determine the integrative measure; it may be applied analytically in case whereas both maximizing and minimizing criteria are included, when all criteria (factors) are interdependent within system as well as when the interaction of criteria (factors) in the system and its impact to the integrative measure is insignificant (Zhang, Yang, 2001; Zavadskas et al., 2007). Absolute and relative indices and criteria with different dimensions (either maximized or minimized) may be integrated by these methods and recalculated as normalized or comparisons, p. ex., using such formula:

R = ij

Rij n

m

i 1

j 1

 R

,

(4)

ij

 

where Rij −normalized significance of j index from i- group. The inversion of minimized indices (minjRij) usually is done such way that they achieve highest significance:

R   minR

j

ij

Rij

.

(5)

ij

21


UTilités Additives DIScriminantes (UTADIS) method is based on additive utility functions and permitting to classify more correctly some objects under valuation also may be applied (Gaganis et al., 2006). The prolonged perspective of the complex evaluation and application for strategic business development program validation suppose integrated application of mentioned Multiple Criteria Decision Making (MCDM) methods for alternative decisions a/o with account of multiple tasks and multiple criteria. Tasks solved by MCDM are divided into such categories: 1) Multiple Objective Decision Making (MODM) designated to the alternatives of infinite set of possible solutions; or 2) Multiple Attribute Decision Making (MADM) designated to the alternatives of finite set (of possible solutions). MADM models are formulated such a way that analysis task is formulated with account of identifying indices (Peldschus, 2007; Zavadskas et al., 2009). Methods of Multi-Objective Optimization by Ratio Analysis (MOORA) and MultiObjective Optimization by Ratio Analysis plus Full Multiplicative Form ( MULTIMOORA) not request to define of variable significance weights (coefficients) by expert way, i.e. is more objective. MOORA method consists of two parts: 1) relation system; 2) theory of starting point. The relation system permits to normalize data and uniform various systems of variable measurement so the external mechanism of normalization becomes unnecessary. MOORA method was adapted and developed by W. K. Brauers and E. K. Zavadskas as MULTIMOORA which is based on the analysis of multiple task optimization of relative dimensions, on complete product form and apply the relative positioning of the object being evaluated to determination (Brauers, Zavadskas, 2008). The later application of methods for optimization of decisions on basis of task function is rather problemic, and Pareto method based on limitation theory may be in certain cases.

22


23


Chapter 4. Evaluation of SMEs Competitive Abilities in Newly EU Countries 4.1. Conceptual Provisions The conceptual theoretical principles of the entrepreneurship development and the estimation models are determined by such general parameters as a dynamism, progressivity, efficiency of the activity, and potential. The new value-added creation and competitive magnitude of goods and services are proposed to be included into the investigation of the various entrepreneurial characteristics affecting the behavioral efficiency of SMEs. The competitive entrepreneurship is considered to be a totality of the components characterized by a great multitude of quantitative indexes and qualitative indicators as variables which must be included into the complex evaluation of the economic competitiveness. It is important to measure its influence on multiaspect balancing between the entrepreneurship efficiency and its social aspects. The estimation principles are determined for this purpose on the basis of modern management theories, also continuing examination methods. The main examination principles are based on an holistic approach to country‘s entrepreneurship competitive advantages as well as to entrepreneurship development level depending from many parameters and characteristics, and determined by multitude of determinants. This viewpoint is developed by authors in our previous studies; they are assigned to assessment of analogous processes (Zvirblis, Buracas, 2011a). Namely, the variety of primary determinants (enhancing or minimizing the competitiveness) describing these processes undoubtedly determines the required quantitative evaluation methods (Parkan, Wu, 2000; Dombi, Zsiros, 2005; Ginevicius, Podvezko, 2009). Moreover, the provided quantitative examination methodology (by applying the reasoned multiple criteria evaluation methods on the basis of the adapted models) in the forecasting the different influence of primary determinants (compatible with qualitative – SWOT - analysis also scenario method) is a useful methodical tool concerned by the theoretical background adaptation. The applicability of complex quantitative assessment methods, the conceptual principles for evaluation of socioeconomic impact on the enterprising and basic models of the complex quantitative evaluation were developed, comparing with previous publications. The essence of the principal approach to the complex evaluation of the country’s entrepreneurship competitive advantages lies in the formalization of the system of primary determinants’ multitude (they have both quantitative and qualitative expression) having the combined dimension. For describing the investigated system, it is necessary to estimate the direct and indirect influence of primary determinants. Therefore, an all-round (general matrix) expression of the total competitive advantages’ vector {E (M ) } can be presented as follows:

b11 b12 b b (M ) {E }   21 22  ... ...  bn1 bn 2

... b1n  {E1} ... b2 n  {E2 } , ... ...   ...    ... bnn  {En }

(6)

where b11 , b12 , ..., bnn are the weight parameters of the direct and indirect influence of primary competitive advantage determinants (vectors {E1} , {E 2 } , ..., {E n } ) on the total competitive advantages’ vector {E (M ) } ; n- number of primary determinants identified on the results of underlying study.

24


It is insufficient to focus the empirical research on the problem of complex investigation and the assessment of entrepreneurship competitive advantage, revealing the priority aspects of both the state institutions and the business entities also their associative structures underlying the development. The authors provided a theoretical framework and empirical viewing, first-of-all, for the solving of the problem to be defined on basis of the general evaluation criteria and determined by a totality of primary competitive advantage determinants to be adapted for newly EU countries. The application of this principal model requires selecting the evaluation method with account of the possibilities for evaluation of different significance of those determinants in general dimension. The consolidated estimation of entrepreneurship level index must also follow these general principles: primary indicators PI may be analogically grouped (5-7 indicators) for these purposes and the indices of every group must be determined. The variety of these components (groups, pillars) describing the essential PI (enhancing or minimizing the competitive priorities) also determines the required quantitative evaluation methods (Zhang, Yang, 2001; Ginevicius, Podvezko, 2009). An assessment may comprise the scenarios interpreting the government macroeconomic policy trends, also the variants of perspective national economic development. After all, only this evaluation (with applying quantitative methods and algorithms) may be incorporated into computerized system of public sector management which is just formed for the purposes of strategic decisions in newly EU countries. The groups (pillars) of PI determining the level of entrepreneurship may be composed with account of global competitiveness pillars (WEF) and integrating the institutional, goods’ market efficiency, business sophistication and innovation indicators. However, the analysis of the entrepreneurship development level in transitional economies suggested to include additionally many other important indicators according to their impact on resumptive measure of its expanding, at last for some newly countries - members of the EU. So, e. g., the important indicators not accounted by the WEF experts are as follows: the procedures and time for starting business, the activity of associated structures, the procedures of the controlling institutions and the sufficiency of competitive financial facilities. The reasonable idiosyncratic pillars of PI determining level of the entrepreneurship as a totality are also the competitive advantage indicators for goods and services, the transformation indicators for goods and services’ markets and SMEs working effectiveness indicators; they were selected by the expert way. Besides, it is possible to include the additional primary indicators for those pillars of PI what would be actual for different countries as well as to add some additional pillars. We expect that provided quantitative evaluation methodology (compatible with qualitative SWOT analysis, also with scenario method) will be a useful methodical tool. The importance of the research is in the using of different, not predetermined, weights of primary indicators and in the adequate differentiation of pillars’ significances. 4.2. Technique of Quantitative Assessment On the basis of conceptual solutions for the quantitative assessment of analogous integral development dimensions that are widely developed by the authors, as were indicated, it is purposeful to tackle a problem. First and foremost multiple criteria evaluation methods are suitable in essence by nature of raised tasks, actually SAW, COPRAS and TOPSIS methods (Hwang, Yoon, 1981; Zhang, Yang, 2001; Zapounidis, Doumpos, 2002a, 2002b; Ginevicius et al., 2008). The choice of SAW method is grounded by the certitude that this method is suitable in case whereas both maximizing and minimizing criteria (with various qualitative indicators) are included (for example, the bankruptcy parameter of business enterprises, must be transferred into normalized values), and, of course, if only maximizing criteria are used. Besides, the significance parameters of primary criteria are taken into account; also they may 25


be differentiated according to potency of the influence to generalized measure. The SAW method is applied (when evaluating entrepreneurship as a system) to estimate the PI pillars mentioned above (as some partial criteria) and to determine the generalized value (the entrepreneurship development level). The calculated indexes (in points) of indicator pillars and estimated entrepreneurship development level were evaluated within 100 point system. The essence of the suggested assessment technique is the quantifiable expert examination of all essential PI: 50 point corresponds to medium evaluation, higher levels – to good or very good (more than 70 points) evaluation, and lower levels – to week or bad (less than 30 points) evaluation. PIs, the impact of which enhances the competitive disadvantage, are evaluated below 40 points but do not have negative values. The indicator significance parameter (in the non-dimensional expression) values was determined by expert way. Expert examination procedure must be implemented applying the widely known concordance methods (including coefficient W, its significance parameter χ2 a/o) and W, S, χ2 formulas (Kendall, 1979). As a result of the identification, the PIs with determined significance levels in the outcome were listed below according to every pillar. In summary, the process of the consolidated estimation of the entrepreneurship development level using justified multiple criteria SAW method (on basis developed backgrounds models) included the following stages: quantifiable (in points) expert examination of identified PI (as primary criteria) significances and listing the determinative PI according to the underlying pillars on this basis; quantitative (multiple criteria) assessment of determinative indicator pillars (as a partial criteria in evaluation system) and determination of the pillar weights (according to their influence on generalized measure); estimation of generalized measure – the entrepreneurship level index (as an integrated criterion) on basis of the determined partial criteria and their weights. As is shown, the reliability of multiple criteria method application is limited by the results of expert evaluations of the primary indicators. So, the computer-generated multiple criteria estimation process (schematically shown in Fig. 3) reveals that the various significance parameters (weights) of the primary and partial criteria are taken into account by the calculation of integrated criterion. Its main features are as follows: the appliance of national entrepreneurship data, the algorithmisation of estimation procedures (on basis of special means), the presentation of resulting findings Every stage of estimation process is adequate to multiple criteria procedures: expert examination of PI (presented in detail in 3.1), determination of the pillar indexes (equations (9-11) below), and, later, estimation of entrepreneurship level index (according to the equation (12) as below). According to the comparison of various scenarios and entrepreneurship development parameters, the consecutive simulation is applied by iteration procedures. This algorithm (Fig. 3) is rather universe and it allows to choose the different (by stages mentioned above) conditions not only in the newly EU countries but also in other countries of different level of the development using the adequate data bases. An assessment process may integrate the scenarios interpreting the government macroeconomic policy trends, strategic perspectives of national economic, also entrepreneurship development; is expected that they were adequately formed in newly EU countries. This process is important when modeling the changes with account of the perspective of the national entrepreneurship competitive advantage. At the same time, it is important theoretical tool for revealing the reserves of enlarging the country’s entrepreneurship potential and evaluating its perspective development programs in most of newly EU countries. These results may be useful as well for the associated entrepreneurship structures.

26


The viability of the presented evaluation system is determined also by the fact that this quantitative evaluation technique may be applied even for the establishment of main parameters of business development strategy.

Accomplishment of assessing conditions Accumulation of country entrepreneurship investigations data

Adaptation of set of basic primary indicators

Establishment of primary indicator values (in 100 point system), their significances and their listing according to pillars

Iterative examination of PI

No

CP

Comparison procedure: W ≥ 0.7

Yes Estimation indexes of underlying indicator pillars

Foresight of the investigated variants. Programming of the algorithm and simulation procedures

Estimation of entrepreneurship level index. Simulation with account of forecasted changes

Supplementary calculations

No

CP

Comparison procedure in accordance with simulative iterations

Yes Summing–up results of consolidated estimation and simulation

Fig. 3. Scheme of Entrepreneurship Level Estimation Algorithm

4.3. Background multicriteria evaluation models and pillars The background models applicable for the countries of different economic development level were developed by the authors with orientation to the conceptual provisions approved above (Zvirblis, Buracas, 2011a). In general, the PI pillar level index Ti(I) (as partial criterion for estimation of the generalized measure - entrepreneurship level index) may be calculating by using the formula: m

Ti (I) 

 j 1

m

pij Rij ;

p

ij

1,

(7)

j 1

where p ij – significance parameter of j-th PI at i-th selected pillar, Rij – value (in points) of j-th listed determinative PI (m– number of listed PI at i-th group). The consolidated entrepreneurship level index La(I) may be estimated after determining the indexes (values) of all partial criteria and their weights as follow: 27

MULTIPLE CRITERIA EVALUATION OF ENTREPRENEURSHIP DEVELOPMENT IN NEWLY EU COUNTRIES n

La ( I ) 

m

  ki

i 1

j 1

i n

m

pij Rij ;



pij  1 ,

k i 1

j 1

i

 1,

(8)

where k i − weight (determined by expert way) of partial criterion Ti (I) according to their direct impact on the entrepreneurship level La(I); n- number of PI pillars. The total amount of PI (their m groups) and n pillars in particular is determined by the complexity of the evaluation according to the formulated tasks and conditions of the valuation. The alternative directions of enterprising development and the monitoring of their development programs are simulated with account of forecasted changes. Below the pillars mentioned before are detailed. The expanded set of typical PI is selected preliminary (on basis of accomplished analytical investigation and SWOT analysis) and arranged according to the previous approach. The indicators of the first pillar of competitive advantage for goods and services (as level of their competitiveness) are such as their quality, up-to-date (to high-tech criteria), also suitability to export, and capacity for innovations. The indicators of second pillar of transformation for goods and services markets indicators include PI, as transparency of competition, means of government promotion, level of legal regulation, level of markets infrastructure. The third pillar of SMEs working effectiveness indicators is focusing on export share, marketing sophistication, diversification parameter, and appliance of social and intellectual capital. The set of typical PI is presented in the Table 1, however only the identified PI would be included, those with sufficient significance, by establishing the partial criteria. Some of them, such as diversification level, export share, spread in outsourcing, may be measured quantifiably besides the qualitative evaluation, however their integrated measurement is preferred within unified point system. 4.4. The case evaluation: Estimating Lithuania’s Entrepreneurship competitive advantage The assessment of Lithuania’s entrepreneurship development level presented below permits the investigation (as well as using SWOT analysis and derivative quantifiable indices, corresponding to the assessed PI) of the typical PI (Table 4) determining the underlying pillars. So, the multiple criteria evaluation process (computer-generated process schematically shown in Fig. 3) includes the following background procedures: the expert determination values and significances weights of primary determinants, the estimation of pillar indexes according equations (9-11) and of general dimension (total index) according equation (12) and modeling the alternative variants with account to tasks of examination. According to the comparison of various scenarios and entrepreneurship development program parameters, the consecutive simulation is applied by iteration procedures. For the case of Lithuania and other newly EU countries, the background models (7 and 8) can be adopted for the measurement of indexes of established pillars mentioned before (with account of the determinative PI and their significance coefficients). The pillar indexes were calculated (Table 7) on the basis of equations below for 2009-2010 and nearest perspective. Adequate to Lithuania’s situation in 2009-2012, the elaboration measurement system provided with account of results of quantifiable expert examination (quantifiable, in 10 point system) of the identified PI and their significance coefficients by the competent professional expert group (7 experts: 3 –from business research and 4 - macroeconomics analytics). The significance of the identified PI in the preliminary investigation was evaluated with a task of determinative PI establishment by every pillar (as could be seen in Table 5, the number of determinative primary criteria by pillars n≤7) and the averaged significances for listed PI were established. Later the determinative PI for Lithuania entrepreneurship development was evaluated (in points) with account of both values in 2009-2010 (I) and in nearest future– 28


2012-2013 (II). The procedure of the rejection of best and worst evaluations for every indicator was applied, expecting to eliminate the possible inadequate influence of any extreme expert opinion to the final evaluation results. Table 4 Underlying Pillars of Typical Primary Indicators (Not Ranked)* The name of a pillar

The essential indicators of a pillar

1. Competitive advantage indicators for goods and services

1.1.Level of goods and services competitiveness 1.2.Production of high-tech goods 1.3.New value-added creation 1.4.Export of high-tech goods 1.5.Capacity for goods and services innovation 1.6.Innovations in production 1.7.Value chain breadth 1.8.Development of competitive derivative services 1.9.Sufficiency of competitive financial facilities 1.10.Other indicators (by the situation)

2. Transformation indicators for goods and services markets

2.1.Level of legal regulation 2.2.Means of government promotion 2.3.Transparency of the competition 2.4.Tariff barriers 2.5. Impact of bureaucracy spread 2.6.Level of markets infrastructure 2.7.Procedures and time necessary for starting business 2.8.Procedures of the controlling institutions 2.9.Spread of shadow economy 2.10.Spread of e-commerce 2.11.Other indicators (by the situation) 3.1.Diversification level 3.2.Marketing sophistication 3.3.Activity of associated structures 3.4.Corporate social responsibility 3.5.Export share 3.6.Cluster formation breath 3.7.Appliance of social and intellectual capital 3.8.Spread of lobbyism 3.9.Outsourcing spread 3.10.Business expenses resulting from racket 3.11.Legal rights of shareholders 3.12.Other indicators (by the situation)

3. SMEs working effectiveness indicators

The table was composed by the authors. *Additional pillars can be added in the following research stages depending of the certain tasks of the expert examination.

29


The consensus and the necessary reliability of evaluation (with account of calculations of the applied expert opinion compatibility) is usually achieved when the statistical values of main reliability parameters W=0.65-0.8 and concordance coefficient significance χ2 related with pre-selected significance level α determining the confidence interval. The χ2 test statistic is basically the sum of the squares of the differences between the observed and expected frequencies, with each squared difference divided by the corresponding expected frequency. In our case, the values of the concordance coefficient W amounted to 0.66 –0.74 (for PI values - 70 percent of W>0.7; for PI significance - 60 percent of W>0.7) so as not exceeded the marginal values in the tables (Kendall, 1979). The concordance coefficient significance parameter χ2 is acceptable (with account of the widely accepted marginal values) by the preselected level α= 0.05 and by α= 0.01 (in detail it is shown in Table 5). Table 5 Reliability Parameters for Determinative Primary Indicators, by Pillars

Pillar and number of primary indicators Pillar (F); n=6 Pillar(E); n=6 Pillar (S); n=7

Concordance coefficient W For primary For significance indicators coefficients 0.74 0.70 0.72 0.68 0.69 0.66

The values W significance χ2 and min [χ2] De facto [χ2] as α= 0.01 [χ2] as α= 0.05 24.50 > 15.086 11.071 23.80 > 15.086 11.071 27.72 > 16.812 12.592

The adopted equations for determining Lithuania‘s entrepreneurship competitive advantage index are presented further. To estimate the level index F(I) of competitive advantage indicators for the goods and services (as the first partial criterion), the equation (9) was applied: i p

i p

i 1

i 1

F ( I )   ai Fi ;  ai  1, p=6,

(9)

where ai − the significance coefficient of direct impact of primary indicators Fi (level of goods and services competitiveness, production of high-tech goods, new value-added creation, capacity for goods and services innovation, etc. ) on the level index F(I). To estimate the level index E(I) of the transformation indicators for goods and services markets (as the second partial criterion), the following equation (10) was applied: i n

i n

i 1

i 1

E ( I )   bi Ei ;  bi  1, n=6,

(10)

where bi − the significance coefficient of direct impact of primary indicators E i (means of government promotion, level of legal regulation, level of market infrastructure, impact of bureaucracy, transparency of the competition, etc.) on level index E(I). To estimate the level index S(I) of SMEs working effectiveness indicator (the third partial criterion), the equation (11) was applied: i m

i m

i 1

i 1

S ( I )   ci S i ;  ci  1 ,

m=7,

(11)

where c i − the significance coefficient of direct impact of primary indicators S i (innovations in production, export share, diversification parameter, marketing sophistication, activity of associated structures, appliance of social and intellectual capital, etc.) on level index S(I). 30


On the basis of common expression (8) the equation for establishment Lithuania’s entrepreneurship development level La(I) is detailed: 3

La ( I )  k1 F ( I )  k 2 E ( I )  k3 S ( I );

k

i

 1;

(12)

i 1

where k1, k2 and k3 − the weights of direct impact of partial criteria F(I), E ( I ), S ( I ) on level index La(I). Table 6

Results of Assessment of Lithuania’s Entrepreneurship Level Index Indicator pillars and determinative indicators

Conditional marking

Pillar of competitive advantage indicators

Assessment (in points) I II

k= 0.4

F

(of goods and services)

Averaged significances and weights

Level of goods and services competitiveness

F1

4.2

5.3

a= 0.22

Capacity for goods and services innovation

F2

4.5

5.1

a= 0.18

Production of high-tech goods

F3

3.3

4.5

a= 0.16

New value-added creation

F4

4.3

5.4

a= 0.16

Sufficiency of competitive financial facilities

F5

3.9

4.6

a= 0.15

Export of high-tech goods

F6

3.6

4.8

a= 0.13

F (I)

4.0

5.0

Level index Pillar of transformation indicators for goods and services markets Means of government promotion

E1

5.2

4.8

b= 0.24

Level of legal regulation

E2

4.5

5.2

b= 0.19

Level of market infrastructure

E3

4.1

4.6

b= 0.15

Impact of bureaucracy

E4

3.9

4.5

b= 0.15

Transparency of the competition

E5

4.1

4.5

b= 0.14

Procedures and time necessary for starting business Level index

E6

4.3

4.7

b= 0.13

E (I)

4.3

4.8

k= 0.3

E

Pillar of SMEs working effectiveness indicators

S

Innovations in production

S1

4.2

4.8

c=0.19

Export share

S2

4.8

5.0

c=0.17

Marketing sophistication

S3

4.8

5.3

c= 0.16

Diversification parameter

S4

4.2

5.0

c= 0.15

Appliance of social and intellectual capital

S5

3.8

4.3

c=0.13

Activity of associated structures

S6

5.1

5.6

c=0.10

Cluster formation breadth

S7

3.9

4.2

c=0.10

Level index

S(I)

4.4

4.9

Consolidated entrepreneurship level index

La(I)

4.2

4.9

31

k= 0.3


When applying the similar equation system for other newly EU countries, the peculiar determinative PI and their number must be taken into account on the basis of additional expert evaluations. The final results of calculations of partial criteria indexes, on the one side, Lithuania’s entrepreneurship development level values, on the other (Table 6), may be interpreted in the following way. The values of indexes for all pillars were at comparable medium levels: for the pillar of SMEs working effectiveness indicators – 4.4-4.9 point, the pillar of transformation indicators for goods and services markets – 4.3-4.8 points, and pillar of competitive advantage indicators for goods and services – 4.0–5.0 points. The problematic primary indicators are production and export of high-tech goods, appliance of social and intellectual capital, impact of bureaucracy. The amelioration of some low scored primary indicators (excluding means of government promotion) is expected in the future. At the same time, the activity of associated structures valuated as good, and the procedures and time for starting business and the sufficiency of competitive financial facilities – are at lower level. At last, the consolidated level of Lithuania’s entrepreneurship development can be evaluated respectively 4.2 (I) and 4.9 (II) points (irretentive evaluation) and that means its level lower than middle for newly EU countries. These results are some additive marks for the directed sustainable development of national entrepreneurship system by growing competitive advantage in the context of macroeconomic country’s development perspectives; they may be useful as well for the associated business structures interested in evaluation forecasting the surrounding factors. The amelioration of the some low scored primary indicators as production and export of high-tech goods, appliance of social and intellectual capital (excluding means of government promotion) is expected in the future. When simulating the effects of challenges, these results may be used for determining some indicators of the entrepreneurship development strategy and/or for ex-post multivariate analysis. The computer simulation is possible according to the process presented in Fig. 3 when evaluating the real changes monitored, for evaluation of the consequences of the financial crisis, also the alternative scenarios of the entrepreneurship development at national level. 4.5. Simplified evaluation of competitive ability determinants The evaluation methodology presented above supposes the simplified determination of country competitive ability determinants. To estimate the total index E(I) by SAW multiple criteria method in process of the complex examination of the entrepreneurship competitive advantage determinants in newly EU countries (as well as in Lithuania and other Baltic States), the basic model may be expressed in the following way (Zvirblis, Buracas, 2011b): i n

i n

i 1

i 1

E ( I )   bi Ei ;0.1  b  0.2;  bi  1;

(13)

where bi − the coefficient of direct impact significance of identified primary determinant E i (capacity for innovation, production process sophistication, cluster development level, extent of marketing sophistication, etc.) on total index E(I); n – number of identified primary determinants for index E(I). The complex examination was performed adequately to the Lithuania’s situation in 2010 and expected it evaluation for 2014. The suggested examination technique propose the expert evaluation (quantifiable, in 10 points system) procedure of primary determinants: so 5 point correspond to media evaluation of investigated determinant level, higher score – to good or very good (more than 7 point) evaluation, and lower score – as satisfactory or poor (less than 3 point) evaluation. The assignment of determinant significance parameters (in the non32


dimensional expression) must be also performed by expert way. It was implemented applying the special theoretically based methods, especially determining the concordance coefficient W and the Pearson’s chi-square test - the concordance coefficient significance parameter χ2. Delimitation of the b values in 0.1 – 0.2 of determinant significance coefficient was performed by applying the SAW method: only factors with adequate significance must be included (according to number of determinants - 0.1), and at the same time those significances do not must differ more than twice. At first stage, the primary competitive advantage determinants (according to 10 points score) and their significance coefficients (nondimensional) were estimated by the expert group. According to the expert method, the satisfactory accuracy of estimations of main factors was achieved by a research team consisting of 7 professional experts: 4 researchers in macroeconomics and 3 analysts in strategic management (by their specialization). The necessary reliability of expert examination is characterized by the main reliability parameters (they received after excepted the best evaluation in every position): the values of the concordance coefficient W amounted to 0.63 – 0.72 (for primary determinants values - 71 percent of W>0.7; for determinant significance coefficients - 45 percent of W>0.7); the significance parameter for concordance coefficient χ2 (Pearson’s chi-square test) is also acceptable at the pre-selected level α= 0.05 (d. f. =8; χ2 >14.067) and at the pre-selected level α= 0.01 (d. f. =8; χ2 >18.475) so as they are better than minimal permitted significances. At second stage, the total index of the Lithuania’s entrepreneurship competitive advantage were determined (according to the proposed equation (13), n=9). They are respectively 4.5 point (2010), i.e. at comparatively unfavorable evaluation, and 4.9 point (2012), i.e. moderately favorable evaluation (Table 7). Some primary indicators such as state of cluster development and creating of value chain and breadth scored poor (< 4.0 point). (< 4.0 point). With account of export growth, the clusterization strategy must be expanded in some business in Lithuania. Creating of value chain and breath based on the high-tech marketing and entrepreneurship model is also necessary. Product development cycles and flexibility (in environmental management) need to be changed and developed. The prospective way is related to the customer and supplier as well as distribution system value chain. The results of evaluation of the determinative PI and their significance as well as weights of partial criteria (by expert examination) are given in Table 7. Table 7 Primary Determinants and Estimation of Total Index for Lithuania by SAW Method

Primary competitive advantage determinants

Appointed marking

Extent of marketing sophistication (incl.

Assessment (in points) 2010 2012

Determinant significance coefficients

E1

4.7

5.0

b=0.14

Production process sophistication

E2

4.5

4.9

b=0.13

Pay and productivity

E3

4.4

4.8

b=0.11

Capacity for production/services export

E4

5.1

5.6

b= 0.11

Capacity for innovation

E5

4.9

5.5

b=0.11

Firm level technology absorption

E6

4.7

5.2

b=0.1

Creating of value chain and breath

E7

3.9

4.4

b=0.1

Corporate social responsibility

E8

4.2

4.6

b=0.1

State of cluster development

E9

3.6

3.9

b=0.1

E(I)

4.5

4.9

diversification)

Total index

33


It was observed that an assessment process may integrate the scenarios interpreting the government macroeconomic policy trends, strategic perspectives of national economic, also entrepreneurship development; expected that they were adequately formed in newly EU countries. This process is important when modeling the changes with account of the perspective of the national entrepreneurship competitive advantage. At the same time, it is important theoretical tool when revealing the reserves of enlarging the country’s entrepreneurship potential and evaluating its perspective development programs in most of newly EU countries. These results may be useful as well for the associated entrepreneurship structures. This process can be applied when modeling the changes with account of the perspective dynamics scenarios of the national entrepreneurship’s competitive advantage; at the same time, it is important theoretical tool when revealing the reserves of enlarging the country’s entrepreneurship potential and evaluating its perspective development programs in most of newly EU countries.

34


35


Chapter 5. Assessing the SMEs Macro Surrounding Favorability 5.1. Main Principles and Models of Quantitative Evaluation of Business Macro Surrounding Evaluation of business macro surrounding factors to gain increasing significance; first of all to validate strategic management decisions; it is increasingly important due to the certain dynamic changes of macro surrounding. It is also important to underline (as revealed by B. Smith (2003) and C. S. Fleisher (2003) that the research of business environment in general must have a goal to make the scope, contents, methods and finally the results of a research help a company to improve the effectiveness of the development of value added. Marketing (its research) falls to the category of the so-called downstream sources of value added. This fact, in turn, determines a very huge variety of criteria and analyzed factors, thus a corresponding required methodological potential. The principal attitude when detailing the aspects of general business environment are their influence to enterprising, and favorability to certain activities. Such approach (based on qualitative analysis) determines both the business possibilities and risks for some of its fields. Nevertheless the research and evaluations of the general business environment helps to achieve the compatibility of entreprising strategy to its surrounding and at the same time to increase the competitive advantage of a company (its value added). The general environment study (including international environment) is important not only when revealing the dynamic changes in environmental trends but also for essential attitude (important for maximal application of environment research and evaluation results) orienting the strategic solutions of companies marketing and active environmental management (Palsaitis, Zvirblis, 2010). It is necessary to formulate conceptual principles and general models for quantitative evaluation of macro factors. In the most general form, they would express the dependence of compound variables (describing both a corresponding set of macro factors, their changes and the direction of changes (Zvirblis, Buračas, 2009; Buracas, Zvirblis, Joksiene, 2012), which mark macro environment, its influence on the identified dynamic factors determining them. It must be said that influence of few significant macroeconomic indicators on the results of company’s activity (total revenue or net profit) can be evaluated using the regressive analysis. However, the regressive analysis is inappropriate for the complex evaluation of the impact of significant macroeconomic indices on the company’s marketing management strategy. Thus the methods of quantitative evaluation, a focus should be given to one of the most perspective quantitative methods, i.e. multicriteria evaluation which allows analyzing the suitability of the decisions for business subjects regarding the possibility of the wide specter of various factors. Moreover, the provided quantitative evaluation methodology is compatible with SWOT and qualitative analysis as a methodical tool and helps to reveal the opportunities of and threats to a company; and it is important to underline this fact. Firstly, it manifests itself from the strategic space perspective (either in its expansion or narrowing). It also improves the opportunities to strive at compatibility of strategic marketing management decisions (both at their formation and implementation stages) with macroeconomic indicators and its changes. Here it is really important to underline relations with social indicators. Undoubtedly, these are important elements of marketing research for any company, and it is one of the most important management and marketing functions of a company (as highlighted by McGee et al. (2009)). When analyzing economic environment of productive companies, it is important to distinguish trends, to determine the main (basic) groups of macroeconomic indicators. Business micro components as object of analysis, have established the importance, the location and 36


boundaries. Necessary to define the situation, its structure, components groups of specific characteristics and parameters (Smith, 2003). Even these specifications determine the necessary data structure at primary stage of research when competitors are identified. The prepared data structure must ensure the recognition and authentication of the object to be analyzed, as well as its concretization according to selected criteria of evaluation.. The reliable evaluation findings often caused the success of company's marketing strategy, and help to foresee so named strategic area appropriate to develop their activities (Fleisher, 2003). The formalized macroeconomic indicator groups (determined by respective sets of indicators) and economic environment as a composition of its indicator groups should be the basis for quantitative assessment. Thus development of respectively formalized (in the most general form) assessment models is among the essential conditions. The development of such models is determined both by the specifics of respective methods of quantitative assessment and by the manner of their adjustment to company’s business situations. Since the principles of versatility, particularity and reliability of assessment are important when validating and making strategic marketing management decisions, among them growth of export potential. (Žvirblis, Zinkeviciute, 2008). Basic models of each component for quantitative evaluation were developed based on these principle provisions. In the most general form, they would express the dependence of compound variables (describing corresponding set of indicator parameters, their changes and the direction of changes), which mark macro environment, its influence on the identified dynamic factors determining them. Thus further analytic research is necessary to solve the problem related to evaluation of macro factors, the theoretical basis must be oriented towards preparation of evaluation methods, and inter alia considering the principles of functioning of long-term computer aided business systems. The basic business macro surrounding (as a composition of components) models for complex quantitative evaluation are developed similarly. Values of each macro surrounding component and significance parameters of their influence on the complex variable (level index) are considered here (Zvirblis, Krutkiene, Vitkunas, 2009). These principle models (general matrix expressions) can look as follows. To evaluate political environment:

 a11 a12 a a 22 PP1 , P2 , ..., Pn ,    21  ... ...  a n1 a n 2

... a1n   P1  ... a 2 n   P2  , ... ...   ...    ... a nn   Pn 

(14)

where a11 , a12 , ..., a nn are the significance parameters of the influence of factors determining political environment P1 , P2 , ..., Pn on the compound variable P . For evaluation of a group E of economic factors:

b11 b12 ... b1n   E1  b b ... b2 n   E 2  E E1 , E 2 , ..., E n ,    21 22 , (15)  ... ... ... ...   ...     bn1 bn 2 ... bnn   E n  where b11 , b12 , ..., bnn are the significance parameters of the influence of factors determining economic environment E1 , E2 , ..., En on the compound variable E .

37


For evaluation of a group S of social factors:  c11 c12 c c S S1 , S 2 , ..., S n ,    21 22  ... ...  c n1 c n 2

... c1n   S1  ... c 2 n   S 2  , ... ...   ...    ... c nn   S n 

(16)

where c11 , c12 , ..., c nn are the significance parameters of the influence of factors determining social environment S1 , S 2 , ..., S n on the compound variable S . To evaluate ecological environment:

 d11 d12 d d 22 T T1 , T2 , ..., Tn ,    21  ... ...  d n1 d n 2

... d 1n  T1  ... d 2n  T2  , ... ...   ...    ... d nn  Tn 

(17)

where d11 , d12 , ..., d nn are the significance parameters of the influence of factors determining technological environment T1 , T2 , ..., Tn on the compound variable T . To evaluate nature environment:  f11 f12 ... f1n   A1  f f 22 ... f 2 n   A2  21  A A1 , A2 , ..., An ,   , (18)  ... ... ... ...   ...      f n1 f n 2 ... f nn   An  where f 11 , f12 , ..., f nn are the significance parameters of the influence of factors determining ecological environment A1 , A2 , ..., An on the compound variable A . For evaluation of a group L of legal and institutional environment factors:

 g11 g LL1 , L 2 , ..., L n ,    21  ...   g n1

g12 ... g1n   L1  g 22 ... g 2 n   L2  , ... ... ...   ...    g n 2 ... g nn   Ln 

(19)

where g11 , g12 , ..., g nn are the significance parameters of the influence of factors determining legal environment L1 , L2 , ..., Ln on the compound variable L . The model for assessment of macro surrounding as a composition of these groups can be presented: k p1 k p 2 ... k pn   P  k    e1 k e 2 ... k en   E  k k s 2 ... k sn   S  M P, E, S , T , A, L    s1 (20)   , k k ... k T t 1 t 2 tn     k a1 k a 2 ... k an   A     k l1 k l 2 ... k ln   L  38


where k p1 , k e1 , k s1 , …, k ln1 , k ln are the significance parameters of direct and interaction impact of respective components P , E , S , T , A , L on the general level M of macrosurrounding level M. As it was stressed, use of the basic evaluation models (mentioned before in a specific situation) is related to separation of significant factors adequate to the situation, i.e. to identification of factors and to their primary qualitative analysis. Without going into detail about the peculiarities of identification, it may be only stressed that it is an important evaluation stage. Thus sets of identified (significant in this situation) macro factors are formed, as well as a respective composition of identified components (when only some of the six components are further analyzed). This corresponds to an offered three-stage qualitative assessment system. The system must be open, i.e. a possibility to include additionally the specific primary indicators must be foreseen. An assessment comprises the design of scenarios interpreting the government macroeconomic policy trends, perspectives of state economic development and variants of marketing management decisions. During the transition period, the country's economic developments and prediction of their evaluation is even more important for businesses. They are oriented towards the results of quantitative evaluation, which help to determine the most favorable ones from the available variants. An essence of three-stage system of quantitative assessment developed below is provided in the following consequence:  the identification and expertise assessment of macro factors determining the specific components;  the assessment of components determining the compound index for each of them;  the assessment of macro surrounding determining the level index as a complex measure. The use of the basic assessment models (mentioned before in a specific situation) is related to separation of the significant indicators adequate to the situation, i.e. to identification of indicators and to their primary qualitative analysis. The indicators must be ranked during their identification according to the significance of their influence conditioned by the following main attributes: the level of influence, relevancy to the situation and occurrence of new opportunities or threats. The theoretical methods for determination of relation between weight coefficients, for ranking (rating scale methods), etc. and concordance model helped to achieve the objectiveness of the ranking. In any case, the system must retain only these factors that meet the selected level of significance. The preparation of scenarios of every component as well as the scheming of general macroeconomic environment scenarios is recommended. It is important to detail the methods of scenario design or formation. They are mostly descriptive however many authors are stressing their perspectiveness esp. when applying for the forecasting of possible changes in business macro environment. Between them, the applications of scenario method for the determination of the alternative strategies of a company as well as its marketing strategies in connection with disposable resources, in particular, must be evaluated. The scenario method may be applied as a result in cases when the reliable information is insufficient and, as result, the decisions with account of uncertain situation may respond more correctly to the perspective changes. Besides, the scenario method is a mean for directed monitoring helping operatively correct the strategy under review, in particular, permitting to analyze the common influence of many various factors or their combinations to the process under review. In the process of scenarios’ formation, their aims and tasks are revealed, the substantiated factors and participants of interaction are determined, primary and final scenarios generated (Ratcliffe, 2002). When editing those scenarios, the experts must systemize the disposable data, to format the compositions of the factors and to edit the logically determined alternatives with account of their probabilities and differences, in particular, also in the cases of marketing management decisions. 39


The scenarios may be defined as a description of a total of most uncertain factors influencing specific object or process. Both several (sometimes alternative) scenarios and combinations of factors determining them must be discussed in cases if there are any possibilities to foresee the changes of situation unambiguously. The scenarios imitating objective situation are usually formulated subjectively but they help to reveal the competitive situation and exclusive abilities of the company if the imitation is based on the detailed business factors selected individually and/or on their component compositions influencing the perspectives of business unit. Some notices on scenario analysis: it is most effective when perspective surrounding situations (or some its components) cannot be unambiguous and even not defined reliably. Then it is recommended to create some scenarios (alternative but expected) of entrepreneurship environment by individual components and general scenario; their analysis may be reglamented methodically by following procedures: Firstly, a common environmental component (quite important for the company by its outlook or in terms of influence) is selected. Secondly, the factors of specific business situation corresponding to the selected environmental components are identified and their combined perspectives are descripted by concluding some alternative variants. Thirdly, 2–3 plausible and capture scenarios (one can distinguish primary and finite) are formulated accordingly to the situation; they can be analyzed by experts as combined variants of the situation. Fourthly, experts must evaluate the environmental component of each of the scenarios analyzed, to forecast the likely effects (both the opportunities and threats) and the expected effects of sector-wide and specific to the company concerned. Fifth, potential characteristic of the company situation is awarded according to forecasted opportunities and threats; as one of expected scenario, it helps to forecast some problems and to reduce the potential impact of the general environment component and consequences to the company. Since both maximizing and minimizing criteria (indicators) are included, their values must be normalized. Using the provided methodology, as we shall see, a normalization procedure will not be required in evaluation of macroeconomic environment. Following these provisions, a measure unit and its value must be selected as well for each identified indicator when using the multiple criteria evaluation method. A non-dimensional expression of this measure (in decimal points) is also acceptable. This value, in any case, is determined on the basis of expert evaluation, as it was stressed, applying the concordance method mentioned before as well. In the outcome of identification and qualitative (expertise) assessment of distinguished primary indicators, the appropriate indicators according to every group (and also corresponding to the designed scenarios of the groups), were conditioned. The estimation process of entrepreneurship surrounding favorability may be realized according to these principles (using multiple criteria evaluation methods) as is schematically shown in fig. 4. The review accomplished above confirmed that it is useful to format the macro factor pillars influencing the magnitude valued by complex way. From the valuation system, they are as partial criteria determining the different advantages to the business development.

40


Accomplishment of assessing conditions Accumulation of country entrepreneurship investigations data

Adaptation of pillars of essential primary indicators

A. Expertise of values (in 100 point system) and significances of primary indicators, their listing into pillars

Supplementary assessment

CP

Comparison Procedure: W ≥ 0,7

B. Estimation of indexes of objective indicator pillars

Foresight of the investigated (modelled) variants. Formation the modelling process algorithm

C. Estimation of general index. Modelling and evaluating the changes

Repeating calculations

CP

Comparison Procedure in accordance with assessing conditions

D. Summing–up results of consolidated estimation and modelling

Fig. 4. Estimation of Macro Surrounding Components for Country’s Entrepreneurship

5.2. Assessing the Lithuania’s Business Macro Environment: Case of New Construction Companies Based on the aforementioned principles and models and applying the provided methodology, a research of macro environment components and factors, their identification, qualitative macro environment analysis, adaptation of basic models using the identified primary factors, as well as quantitative evaluation of macro environment was performed for Lithuanian new construction companies. After analyzed the basic sets of factors, all six components of macro environment were identified and became an object of an expanded PESTEL analysis. Nevertheless the significances and influence level of specific factors were expertise evaluated differently, thus concordance coefficients W were calculated. The necessary level of accommodation of experts’ (respondents’) opinions ( Wmin =0.7-0.8) was achieved, however. It was applied both when macro factors as significant (thus determining the contents of macro environment components), when making qualitative evaluation and determining the significance of the identified factors and when determining the significance of influence of each identified factor on size of a respective component. Besides, the experts’ (respondents’) also determined the significance of influence of each identified macro environment component on complex macro environment level index. The following generalised expression was used for qualitative evaluation of separated 41


factors: highly favourable (++), favourable (+), neutral (+–), unfavourable (–) highly unfavourable (– –). Evaluation of macro environment (according to components) could be the following: sufficiently favourable, medium favourable, unfavourable (Zvirblis, Krutkiene, Vitkunas, 2008). The 100-point system suggested by the authors was confirmed for determination of quantitative values of identified factors. It was decided that 100 points mark absolutely favorable (positive) influence of a factor on a company, 70-80 points mark highly favorable influence, 60-70 points mark favorable influence, 50–60 points mark medium favorable influence, 40–50 points mark unfavorable influence, 30–40 points mark highly unfavorable influence. In such case we do not need a sign of influence direction, because all factors have a single-direction influence. Both current situation and perspective forecasted changes within 23 years were comprised (as specified by respective possible variants, 1 (status quo situation) and 2 (trend situation), of evaluation) (Zvirblis, Krutkiene, Vitkunas, 2009). The results of macro factor identification for Lithuanian new construction companies and qualitative expert evaluation of distinguished primary macro factors give us sets of factors determining each macro environment component. They are the basis for further complex evaluation of macro environment. They are used to make (on the basis of basic models (21) – (27)) corresponding equations (using the previously validated SAW method and assuming that the identified factors are not interdependent) for scoring (in points) of macro environment components. Identified macro factors, results their qualitative and quantitative evaluation, as well as the evaluation of significance of their influence on a respective environment component and the calculations of concordance coefficients W are summarized in Table 8.

42


Table 8. Primary factor identification for environment and assessment of their values and significance of influence, calculations of concordance coefficients W (Lithuanian new construction companies)

Macro environment components and the determining essential factors Political environment (P): EU membership Stability of government Influence of political parties on business Corruption factor Economic environment (E): Macroeconomic advancement Level of population income Investment climate Development of free economic zones Size of taxes, their change Public procurement Social environment (S): Implementation of social programs Demographic situation Problem of skilled construction workers Migration processes Technological environment (T): Priority of progressive technologies Use of innovations Updating of machinery Ecological environment (A): State of infrastructure Environmental requirements Specific requirements to construction work Legal environment (L): Legal regulation of competition Laws on construction Institutional decisions

Agreed marking

Qualitative evaluation

P1 P2 P3 P4 W

Evaluation in points

Weights

1

2

(+ +) (+ −) (+ –) (– –)

75 60 60 40 0,754

70 55 65 45 0,743

0.3 0.3 0.2 0.2 0,782

E1 E2 E3 E4 E5 E6 W

(++) (+ ) (+) (+) (−) (– –)

65 55 60 50 45 40 0,733

60 60 65 55 50 45 0,716

0.2 0.2 0.15 0.15 0.15 0.15 0,748

S1 S2 S3 S4 W

(+ ) (–) (–) (– –)

60 50 30 30 0,616

60 55 35 35 0,632

0.2 0.2 0.3 0.3 0,691

T1 T2 T3 W

(+) (+ ) (–)

60 50 50 0,784

60 55 55 0,758

0.4 0.3 0.3 0,819

A1 A2 A3 W

(+) ( –) (–)

60 40 40 0,691

65 50 45 0,676

0.4 0.3 0.3 0,723

L1 L2 L3 W

(+ ) (+−) (−)

60 40 40 0,766

60 50 45 0,738

0.3 0.3 0.4 0,792

Thus in general we have the following real model (corresponding to specific conditions of quantitative evaluation of Lithuanian construction companies’ macro environment): P E

i 4

4

i 1 i 6

i 1

 ai Pi a11P1  a22 P2  a33P3  a 44 P4 ,  ai  1;

(21) 6

 bi Ei b11E1  b22 E 2  b33E3  b44 E 4  b55 E5  b66 E6 ,  bi  1;

i 1

i 1

43

(22)


S

i 4

4

i 1

i 1

 ci S i c11S1  c22 S 2  c33S 3  c44 S 4 ,  ci  1;

i 3

3

i 1

i 1

i 3

3

i 1

i 1

i 3

3

i 1

i 1

(23)

T   d i Ti  d11T1  d 22T2  d 33T3 ,  d i  1;

(24)

A   f i Ai  f11 A1  f 22 A2  f 33 A3 ,  f i  1;

(25)

L   g i Li g11L1  g 22 L2  g 33 L3 ,  g i  1.

(26)

We have the following model (it based on direct impact of components’) for complex evaluation of construction companies’ macro environment (determination of the index of macro environment level in points): M

i 6

6

i 1

i 1

 k i M i k p1P  k e1E  k s1S  kt1T  k a1 A  k l1L,  k i  1.

(27)

The following weights of influence of separate components (values of coefficients of the equation (27)) are determined: k p1  0,2 ; k e1  0,2 ; k s1  0,3 ; kt1  0,1 ; k a1  0,1 ; kl1  0,1 (W =0.783). The multi-variant calculations made on basis of the equations (21)–(27), permitted to analyze the influence of identified macro environment factors and their possible combinations (according to comparative variants reflecting possible factor combinations considering possible compositions of components). The results of evaluation of macro environment components are shown in Table 9. Table 9. The evaluation of components according to specific combinations of primary factors

Marketing environment components Political environment (P) Economic environment (E) Social environment (S) Technological environment (T) Ecological environment (A) Legal environment (L)

Scoring (in points) according to combinations of primary factors Status quo situation Trend situation 60.5 59.5 43.5 46 40 44 54 57 48 54.5 46 51

Complex evaluation of macro environment level index within the comparative variants M (I) and M (II) (Table 10) was performed. Besides, having selected different compositions of components (with three very significant components) and having redistributed values of influence coefficients in the following way: k p1  0,25 , k e1  0,40 , k s1  0,35 , variants M (III) and M (IV) were modeled additionally (Table 9). When modeling influence of primary factors, factors providing the most favorable opportunities and factors arousing threats were analyzed in detail.

44


Table 10. The Evaluation of Marketing Environment Level Index According to Comparative Variants (compositions of components)

Compositions of environment components (comparative variants) M(I) M(II) M(III) M(IV)

Environment level index (in points) Trend Status quo situation situation 47.6 50.6 49.1 49.1 46.5 48.8 47.9 47.4

The summary of the results of the preformed analysis and evaluation of marketing environment factors of Lithuanian new construction companies’ (although they may be analyzed from various perspectives) showed that:  the political environment has the most favorable influence (EU membership is outstanding among the factors), because it scored 59.5-60.5 points. However, the corruption factor is evaluated as extremely unfavorable;  the social environment is evaluated as the worst: the status quo situation scored only 40 points (migration processes are considered an extremely unfavorable factor now and in the future; the influence of implementation of social programs is evaluated as the most favorable);  the following factors can be specified as having favorable influence (among other macro environment components): legal regulation of competition, macroeconomic advancement, investment climate; and the following factors as having the least favorable influence: specific requirements to construction work, public procurement;  the M(I) variant (trend situation) is evaluated as the most favorable among compositions of all analyzed marketing environment components according to its influence (due to higher values of indices of all components except for political environment), and this corresponds to medium favorable level of environment. It must be stressed that when situation changes (when value of any of identified factors changes or new significant factors emerge) and a possibility of new significant events occurs (in general this happens periodically), it is expedient to perform a directed modeling and to have reliable forecasts of the influence of marketing environment factors. 5.3. Assessing socioeconomic factors influencing Lithuania‘s productive sector enterprises The models for complex assessing the favorability of socioeconomic factors (influencing enterprise strategic decisions in the productive sector) are presented (Zvirblis, Buracas, 2009). The groups of macro factors selected preliminary by economic factors, social factors, export - import factors, and representing such groups having influence to strategic marketing management solutions of Lithuania‘s productive sectors (including chemical industries as in a case study: Purlys, Žvirblis, 2007) are presented in the table 12. Below it is shown how macroeconomic factors becomes determining the export potential of companies in this sector. Principal scheme of the algorithm for the evaluation of socioeconomic factors is presented in Fig. 5.

45


Composed by authors. Fig. 5. Algorithm for the Evaluation of Socioeconomic Factors

46


The scenarios of the socioeconomic factor groups and general socioeconomic environment scenario are presented below (Table 11). As a case for illustration, two scenarios were designed for each group of indicators (respectively “I” and “II”) on the basis of composition used for the creation of the general macroeconomic environment scenarios. A perspective was regarded and a principle was taken into consideration that one of the scenarios, if possible, must be oriented towards the real situation (with account of impact on marketing strategy of the company). Table 11 presents the designed scenarios of separate indicator groups and general macroeconomic environment scenarios (respectively MI, MII and MIII, they reflect the appropriate scenarios for every group of indicators); the scenarios are called as ‘’Recession’’, “Bright Time”, “Perspective Situation. For assessment of the socioeconomic factors in case of Lithuania’s chemical industry, the complex evaluation system presented below permits to form the entire totality of the socioeconomic indices groups as partial criteria adequate to the peculiar situation with account of expertizing. It is important that group of experts would be completed accordingly to their competence in the fields of marketing management and business finances. Under this methodology, the identification of substantial indicators was fulfilled for the Lithuanian chemical enterprises by expert evaluation. Experts must distinguish the indicators with both forecasted positive and negative effects, and a comparative strength of distinguished factors (e.g. highly favorable, favorable, unfavorable, highly unfavorable, etc.) as well as trends of their change. The table 12 represents indicators evaluated first of all by comparative intensity of distinguished factors’ impact (p. ex., strongly favorable (+ +), favorable (+), unfavorable (), strongly unfavorable (- -), etc.). These indicators were evaluated quantitatively by experts and lately identified with account of it. The necessary reliability of evaluation was achieved so as the value of the concordance coefficients W amounted to 0.7 – 0.8. The procedure of rejection of the best and worst evaluations in every stage was also applied.

47


Table 11 Scenarios for Separate Indicators Groups and General Scenarios of Lithuanian Socioeconomic Environment Scenario; component compositions MI (Recession) EI+SI+AI+ LI

MII (Bright Time) EII+SI+AI+LII

Content of scenario (according to each group of indicators and compositions of components) (EI) The development of state economy (influencing GDP) and level of inflation would have negative impact; direct foreign investment conditions also would have positive impact; tax system have strong negative impact after foreseen alterations; finance system (included credits and percentages) have negative influence on business; quantity of companies‘ bankruptcies would grow. (SI) Situation in labor market and emigration / immigration processes would stay as negative indicators; the influence of wages level would be unfavorable indicator; the shortage for cheap labor force would cause less of problems. (AI) Coverage of export and import will remain as a negative indicators; export conditions may change to better or worse, protection standards and regulation of specific requirements would have negative impact from the point of view of a company. (LI) Legal regulation of economics would have negative influence; laws (EU included) regulating export and import would have negative attitudes (from the point of view of company); decisions of state institutions would be unfavorable for a company. (E1I) The development of state economy (influencing GDP), level of inflation and direct foreign investment conditions would have the positive impact in the future; tax system in future would not have such strong negative impact; finance system (included credits and interest rates) would have positive influence on business; quantity of company’s bankruptcies would decrease. (S1) Situation in labor market and emigration / immigration processes would stay as negative indicators; the influence of wages level would be unfavorable indicator; shortage in cheap labor force would be causing less of problems. (AI) Coverage of export and import would remain as negative indicators; export conditions may change to better or worse, protection standards and regulation of specific requirements (from the company‘s point of view) would have negative impact. (LII) Legal regulation of economics would be more positive in the future; laws (EU included) regulating export and import would have more positive than negative attitudes (from the company‘s point of view); decisions of state institutions would be more favorable for a company.

48


Scenario; component compositions MIII (Perspective Situation) EII+SII+A1I+L1I

Content of scenario (according to each group of indicators and compositions of components)

(EII) The development of state economic (influencing GDP), direct foreign investment and level of inflation would have the positive impact in the future; tax system in future would not have such strong negative impact; finance system (included credits and percentages) would have positive influence on business; quantity of companies‘ bankruptcies would decrease. (SII) Situation in labor market and emigration / immigration processes would have less negative indicators; the influence of wages level would change into better; shortage in cheap labor force would cause more problems. (A1I) Coverage of export and import would be characterized by more positive indicators; export conditions may change to better or worse, protection standards and regulation of specific requirements (from the company‘s point of view) would have negative impact; export would be more promoted. (L1I) Legal regulation of economics would be more positive in the future; laws (EU included) regulating export and import would have more positive than negative attitudes (from the company‘s point of view); decisions of state institutions would be more favorable for a company. EI – economic indicators; SI – social indicators; AI – export- import indicators; LI - legal regulation indicators. Source: composed by authors.

Lately the group of identified indicators adequate to the situation was evaluated by SAW method and formatted on the basis of me and II scenario variants. The adopted evaluation models are presented as follows. The group of economic indicators as partial criteria for the evaluation of index E(I):

E ( I )  i 1 bi Ei ; i 1 bi  1, i 5

i 5

(28)

where bi − the coefficients of direct significance for the level of influence of primary identified indicators; E i ( GDP, level of inflation etc.) The group of social indicators as partial criteria for the evaluation of index S(I):

S ( I )  i 1 ci Si ; i 1 ci  1 , i 4

i 4

(29)

where c i − the coefficients of direct significance for the level of influence of primary identified indicators S i ( mean wages, level of unemployment etc.) The group of export-import indicators as partial criteria for the evaluation of index A(I):

A( I )  i 1 f i Ai ; i 1 f i  1 , i 4

i 4

(30)

where f i − the coefficients of direct significance for the level of influence of primary identified indicators Ai (export possibilities, its promotion system etc.) . The final results of the evaluation of identified indicators are presented in table 12.

49


Table 12 Results of Qualitative and Quantitative Assessment for Identified Indicator Groups of Socioeconomic Environment Socioeconomic indicator groups and determining essential indicators

Agreed marking

Qualitative evaluation

Assessment in points I

II

Group of economic indicators (E)

Weights 0,4

Direct foreign investments

E1

(+)

5,5

6,0

0.2

Change of GDP

E2

(- )

4,5

5,5

0,15

System of finances (credits and interest rates)

E3

(- )

4,5

5,0

0.2

Economic regulation level

E4

(- )

4,0

5,5

0.2

Taxation favorability

E5

(--)

3,0

4,0

0,25

4,3

5,0

Level index E (I) Group of social indicators (S):

0,35

Real wages

S1

(+ )

6,0

6,0

0.3

Labor market flexibility

S2

(–)

4,0

4,5

0.3

Unemployment level

S3

(–)

4,5

5,5

0.2

Requirement for qualified workers

S4

(–)

4,5

4,5

0.2

4,8

5,1

Level index S (I) Group of export- import indicators (A):

0,25

Export possibilities

A1

(-)

4,0

4,5

0.35

Export inducement system

A2

(-)

5,0

5,0

0.3

Changes in currency rates

A3

(--)

3,5

4,5

0.15

(--)

3,5

4,0

0,2

4,1

4,6

Import changes

A4

Level index A(I)

Case of Lithuanian chemical industry. Evaluations by the authors according to scenarios I and II and determination of their influence weights.

The value of socioeconomic environment index M(I) is determined by applying additive proportional assessment method and after finding the significances of partial criteria: M ( I )  k e E ( I )  k s S ( I )  k a A( I ) , (31) where k e , k s , k a − coefficients of impact influence of partial criteria E ( I ), S ( I ), A( I ) on the value of socioeconomic environment index M(I). It was determined by expert way that k e  0,4; k s  0,35 and k a  0,25 . The socioeconomic environment index was evaluated according to three general scenarios (MI, MII, MIII); also the predetermined evaluation both of group indicators and environment indices was performed by 3 most significant indicators from every group of them (MIV - MIX). The results of calculations are as follow (table 13):

50


Table 13. Socioeconomic Environment Level Index According to General Scenario Variants Compositions of indicator groups MI

Level index (in points) Recession

Bright

Perspective

Time

Situation

4,4

MII

4,7

MIII MIV

5,0 4,6

MV

4,8

MVI MVII

5,1 4,5

MVIII

4,9

MIX

5,0

Other scenarios also may be simulated in the process of multivariate calculations on the basis of the models (28) – (31) according to the algorithm presented in the fig. 7; other scenarios may be also formulated according to the changing situation. The other comparative variants may be also analyzed, in particular those when uniform significance is attributed to all primary factors or partial criteria. In such cases the expert evaluation procedure of the significances of those indicators or partial criteria is unnecessary. The performed complex assessment of essential socioeconomic indicators (Lithuanian chemical industry case) showed that indicators of export – import group have comparatively (and may have in the perspective) the most unfavorable influence (it scored respectively 4,1 and, within the context of the forecasted perspective situation, 4,6 point). The social indicator group is scored 4,8 and 5,1 point (the medium favorable level), and the economic indicator group scored as follows: in real situation – 4,3 point (unfavorable influence); the perspective situation – 5,0 point (medium favorability). It was determined after calculation of the level index of various indicator group combinations that the macroeconomic environment can be evaluated 4.4 – 4,6 point according to Recession scenario (unfavorable influence), and 4.7 – 4,9 point according to Bright Time scenario and 5,0 – 5,1 point according to Perspective situation scenario (medium favorability). The simplified solutions are possible when the comparative analysis of analogous socioeconomic indicators was accomplished, in particular, for Baltic States (Zvirblis, Buracas, 2009).

51


Chapter 6. Complex Evaluation of SMEs Financial Potential 6.1. Principles of complex evaluation First of all, an adequate system of criteria for assessment of the situation has to be established when evaluating the financial potential of SME. You can rely on both the static concept of evaluation criterion (among other things, it allows the company to limit the database to be stored) and the dynamic performance criterion of the concept (including evaluation of key indicators of changes in the dynamics). Moreover, in most cases it is appropriate to address the challenge of focusing on the development scenario purposefully shaped micro surrounding components, including competitors. Besides, it is appropriate to rely on other components of the environment for business formalization (somebody principal expressions of these components in the form of vectors) at the conceptual evaluation of competitors’ potential. It allows assessing, in the general case, not only of the quantitative measure of criteria (factors) determining these components but also evaluation of their impact and direction of this impact. So in very general case, vector expression of a company's financial potential {P} is:

P  P(P ( P)  , P (V ) ,..., P ( F ) ) ,

(32)

    

where P ( P ) , P (V ) ,..., P ( F ) - subvectors of evaluation criteria. After entering the parameters of criteria impact on the potential, we have such vector {P} expression:

 P (P)   (V )  P P  H p .  , ...  (F )   P 

 

(33)

 

where H p – matrix of criteria P ( P ) , P (V ) ,..., P ( F ) impact on the potential significance. Analogically, subvectors of lower level may be expressed by defining them through totality of integral indicators Rk (m – their number) and significance parameters of their impact :

 RK 1  R  . K 2  ,  ...     RKm 

P   G  (K )



(K ) P

(34)



where GP( K ) − matrix of impact significance parameters for integral indicators RKi . The analysis confirmed that first of all, the groups of the target financial indicators covering the primary financial indicators, which have significant influence over the size of the evaluated dimension, must be singled out in the process of complex evaluation of companies’ 52


activity. In the valuation system aspect, they are partial criteria determining the basic criterion of activity‘s financial efficiency. First of all, such indicator groups as profitability, financial status also financial management and resource management, must be singled out. Those groups consist mostly of primary financial indicators determined by mandatory audited data and contained according to each company's financial statements (in particular the balance sheet and profit (loss) statements). Besides, the cash flows from changes in equilibrium (in terms of management efficiency) as well as compatibility with the financial resources, also the basic criteria of the company’s value added creation efficiency were distinguished in the analysis fulfilled. It is accepted that the assessment of companies - potential competitors is determined in principle by the totality of basic criteria set out. Anyway, in peculiar case, it is appropriate to add the specific criteria, revealed by the SWOT analysis and competitor identification process, for assessment of the potential. On this background, the quantitative evaluation of the three-tier system is proposed, which allows forming the totality of basic criteria, sub criteria and indicators appropriate to the situation. It covers the primary financial indicators concluding the groups of targets indices, the indices of operational financial efficiency and other basic criteria and potential index evaluation, taking into account the significance of the relevant parameters.

Research of competitors and identification of factors

Formalization of evaluation criteria system

Normalization of primary indices’ values

Expert evaluation of primary indices (10 point scale)

Expert evaluation of significances of primary indices

Dretermination of indices of factor groups on basis of equations (38 -41)

Dretermination of general potential index (43 - 47) pagrindu Pk (I) on basis of equation (43)

Fig. 6. Procedures of Evaluation of SMEs Financial Potential

The main procedures of financial potential evaluation are shown on Fig. 6. The evaluation algorithm presented (realized, for example, using the adapted MS Excel) provides 53


partial criteria, basic criteria and the summary procedures of determining the amount of repetition. By such a way, in turn the target groups of companies - potential competitor’s indices are established. As a result, the competitors are ranked according to their potential (which is the essence of formalized assessment report). Above-based multicriteria evaluation methods are applied when specifying the principles of assessment of the potential of k-th company-competitor (in certain target groups of competitors). In this case, the index of the financial potential PK I  as a generalized measure - is determined on the basis of normalized values of above basic criteria, and expert assessment of their direct impact on the size of the potential significance of the parameters (uniform assessment of all the target groups of competitors). Target groups of the financial indicators are based on expert assessment of normalized indices of the background values of financial indicators and their direct impact on the level of significance of the coefficients of indices (also uniform assessment of all the target groups of competitors). The dimensionless expression (by split unit) is adopted in the choice of normalization of the primary indicators and parameters of the overall integrated assessment of the measure. In the case, the normalization procedure can be applied directly to an assessment interval [0, 1] and recalculated for maximized ratios by the formula:

max R  R R   1  max R  min R j

jr

,

j

j

(35)

j

where R jr − real target valuation; max R j - maximal target valuation; min R j − minimal target valuation (of those acceptable values). The significances of minimized indices are recalculated by formula:

max R  R R   max R  min R j

jr

,

j

j

(36)

j

In specific case, the basic criteria of a complex quantitative evaluation could be included in the developed assessment system if there are not sufficient data. In this case, they should be evaluated by experts (as normalized values) in the range [0, 1], while the value [1] is adequate to the best assessment (corresponding to the ideal case). This is true for criteria of cash-flow equilibrium and compatibility of the company's financial resources (financial companies demonstrate the abilities to implement strategic decisions). Anyway, the correlative assessment models below must be adapted (by qualitative analyzes) according to a specific business situation, as well as by covered criteria and indicators as a whole (taking into account the information accumulated in the database). It allows the option of a wide turn to the evaluation of the technology components of the general business environment and strategic marketing solutions into company quantification (the motivation) system. This is especially important for dynamic business environment changes, in order to effectively use the totality of enterprise means. In principle, to amount the identified companies - competitors as the whole, their potential can be evaluated by each competitor in the same basis by applying different significances of the criteria and sub criteria parameters. This means that each competitor can be seen by some (different) priority criteria. Not detailing of the technique adapted in this case to the layout, it can only indicate that such an assessment can be seen as a ranking problem and solved as a multicriteria evaluation of alternatives with account of priories. The specific assessment task formulated in such a way can be solved, p. ex., when using of multiple criteria analysis systems known as ELECTRE or PROMETHEE (Podvezko & Podvezko, 2009). A simplified ranking can be applied in the comparative analysis of potential; its 54


essence in the comparison of twin companies - competitors based on appropriate criteria (such as financial indicators of group indexes). 6.2. Basic evaluation models for integrated financial potential groups A basic adaptive quantitative assessment models were drawn for the financial capacity assessment of the entreprising (especially manufacturing companies, as an extended case) by applying the principles presented above. As already pointed out, these basic models are easily adapted to different situations, so they use in the assessment of companies’ financial potential in other economic sectors (such as construction, service, and marketing). First of all let‘s concentrate on the models of financial valuation indicators based on individual target groups. By applying the SAW method discussed previously, these Pi units can be assessed against the criteria for a single benchmark model: n

Pi  

i 1

m

m

 p R ; p j 1

ij

ij

j 1

ij

 1,

(37)

where p ij – parameter of direct impact of j- th primary variable on the significance in ith target group (n = number of groups), Rij – normalized values of the primary indicators identified in the specific situation and assigned to the appropriate target groups (m - number of group). The totality of exclusive primary financial and other indicators and sub-criteria, and the basic criteria to consider the general case is presented in table 14. Table 14

Totality of Basic & Partial Criteria, and Primary Financial & Other Indicators Basic criteria

Partial criteria

Essential primary indicators

Financial efficiency of activity

Profitability of activity

Profitability of equity

Financial status

Efficiency of financial management

Efficiency of resource management Financial

compatibility Cash flows equilibrium Value chain

Return on equity Gross margin of profitability Net margin of profitability Return on assets Return on investment General coverage ratio Quick ratio Rate of maneuver General ratio of solvency Loan repayment rate Accounts receivable for company Company debt to creditors Stock turnover time Material stock renewal The sales volume per employee Gross profit per employee in volume Asset use efficiency

Evaluated by expert way Evaluated by expert way Evaluated by expert way

55


The assessment models of index of separate indicator groups (based on the primary financial and other indicators of normalized values) have the following expression: For evaluation of operating profit (as a partial criterion) index Pp : m

m

a 1

a 1

Pp   pa R pj ;  pa  1 ,

(38)

where p a − coefficients R pj of significance of primary financial indicators (return on equity, gross profit, net profit, and so on). For evaluation of financial status (as a partial criterion) index Pb : m

m

b 1

b 1

Pb   pc Rbj ;  pc  1 ,

(39)

where p c − coefficients Rbj of significance of primary financial indicators (critical liquidity, maneuverability, solvency, etc.). For evaluation of financial status (as a partial criterion) index Pe : m

m

c 1

c 1

Pe   pd Rej ;  pd  1 ,

(40)

where p d − coefficients Rej of significance of primary financial indicators (duration of inventory turnover, accounts receivable company, creditor 'of indebtedness, etc.). For evaluation of financial status (as a partial criterion) index Pv : m

m

e 1

e 1

Pv   pg Rvj ;  pg  1 ,

(41)

where p g − coefficients Rvj of significance of primary financial indicators (sales per employee, asset utilization efficiency, etc.). After determining these sub-criteria values using the SAW method, - the financial performance index P f as the basic criterion is determined on the basis of the model:

Pf   h fi Pfi  h f 1 Pp  h f 2 Pb  h f 3 Pe  h f 4 Pv ,

(42)

i 1

where hi − coefficients of impact of partial criteria Pi significance on operating financial indicators. Then, using the additive proportional assessment, the general index Pk (I ) of a k-th company's financial potential is determined by the formula: Pk ( I )  a1V1  a2V2  ...  a f Pf , (43) where a1 , a2 ,..., a f – parameters of significance of separate basic criteria V1 ,V2 ,..., Pf impact on the financial potential index Pk(I) (n – number of parameters); V1 ,V2 ,..., Pf − normalized values of basic criteria (compatibility with available financial resources, cash flow sustainability, operational efficiency, value chain effectiveness, etc.). The following comparative versions also can be examined when all primary parameters or partial criteria (determining a partial criterion) are given an equal importance. In this case, the expert evaluation for these indicators or partial criteria of impact on the potential is unnecessary. However, the significances of overall impact are different, so they must be 56


evaluated by experts according to the methodology. It is important that an expert group to be formed exclusively by excellence in financial management. Prepared technique was approved by the comparative evaluation of a group of specialized logistics services’ companies - potential competitors. Current data based on financial performance was evaluated using the criteria of financial efficiency, thus covering the four groups of primary indicators listed above (in Table 14). It was found that potential index Pk (I) is ranging in this group from 0.63 to 0.75, but the index business potential exceeds 0.7 for only 35 percent of Cos. Thus the formation of a three-tier assessment system meets the evaluation of versatility, completeness and reliability principles, primarily because it includes the primary financial indicators, which are mostly calculated by the audited company's balance sheet and profit (loss) statements. It also includes the following parameters determined on the basic criteria of values for each of the different groups of indicators and indices by the expert way. On this basis, the presumptive measure - the financial capacity index – is determined which may be a criterion for ranking the target group of companies - competitors.

57


Chapter 7. Estimation of Development Level of Goods Markets in Newly EU Countries 7.1. Model of consolidated evaluation In the previous chapters provided a theoretical basis for formalization of environmental pillars; these principles are the basis by solving the problem of consolidated evaluation of the goods markets efficiency (Zvirblis, Buracas, 2010; Joksiene, Zvirblis, 2010). At first, it is necessary to use the different weights of advantage and disadvantage of primary indicators PI for adequate differentiation of their (PI) significances. Second, it is necessary to allow the direct and indirect influence of essential factors. When formalizing the markets as a system of components, it must describe the dependences for these components and whole determinative factors, taking into account the direction of the impact (vector) each of them. The principal (general matrix) expression for certain component- vector {L} can look as follows:

 g11 g12 g g 22 {L}= f [{ {L1} , {L2 } , ..., {Ln } ]   21  ... ...   g n1 g n 2

... g1n  {L1} ... g 2 n  {L2 } , ... ...   ...    ... g nn  {Ln }

(44)

where g11 , g12 , ..., g nn are the weights of the direct and indirect influence of essential factors {L1} , {L2 } , ..., {Ln } determining descriptive component of the vector {L} ; n- number of primary indicators. The generalized model – expression of vector { A(M ) } - for evaluation of the functioning level of the country good markets as a system of components may be represented in the following way:

A  CA ,A ,...,A  , (M )

1

2

m

(45)

where C  - the matrix of the significance parameters of direct and interactive impact of respective market components A1, A2 ,..., Am  on the general measure - vector { A(M ) } of market level, m- number of respective components. Undoubtedly, the applicability of these models is concerned with the transformation according to the applicable evaluation method and takes into account the respective market components allowing the totality of identified primary factors in a specific situation. To estimate the level index S(I) of the pillar (S) of markets size and competitive advantage indicators (as the first partial criteria) in the consolidated examination process by SAW multiple criteria method, the following equation may be used: i r

i r

i 1

i 1

S ( I )   f i S i ;  f i  1, ir

where

f i 1

i

(46)

− the sum of weight coefficients of direct impact of listed primary indicators S i

(Table 12) on level index S(I); r – number of primary indicators, determining level index S(I).

58


The level index E(I) of markets dominance, tariff barriers and financial indicators as second partial criteria of the pillar (E) may be defined as follows: i n

i n

i 1

i 1

E ( I )   bi Ei ;  bi  1,

(47)

where bi − the weight coefficient of direct impact of essential primary indicator E i (Table 15) on level index E(I); n – number of primary indicators, determining level index E(I). When applying these models in practice, only listed PI adequate to selected impact weights in the every specific pillar (according to their ranking results) are taken into account. The value of general markets functioning level index Am (I ) may be established on previously determined indexes S(I) and E(I) allowing the significance parameters of these partial criteria:

Am ( I )   km Am ks S ( I )  ke E ( I );  km  1,

(48)

where ks , ke − significance parameters (determined by expert way) of partial criteria S ( I ), E ( I ) respectively determined impact on general markets functioning level index Am (I ) . 7.2. Typical primary indicators specifying the market components and results of the consolidated evaluation: Lithuania‘s case Two pillars of the typical PI determining adequate underlying components of goods markets were constructed on the ground of the general principles formulated before and presented analytical investigation. There are pillar (S) of pillar of markets size, nature of competitive advantage, marketing sophistication indicators (Table 16) and pillar (E) of markets dominance, tariff barriers and financial indicators (Table 17). It is necessary to notice the PI specifics for adequate pillars. In the first pillar (S) such are domestic and foreign market size, nature of competitive advantage. The extent of market dominance, tariff barriers, extent and effect of taxation, financial market sophistication can be distinguished in the second pillar (E). So, the influence of typical PI significance to the general level of functioning markets can play quite different values. Adequate to the Lithuania’s situation in 2010 (I) and trend scenario (II), the competitive indicators (according to 10 points score) and their significance coefficients (non-dimensional) were assessed by the expert group. According to the expert application methodology, a satisfactory accuracy of estimations of a few factors was achieved by a research team constituted by 9 professional experts: 4 bank analysts and 5 analysts of business strategy. The necessary reliability of expert examination as is shown according to the main expert assessment reliability parameters (Table15) was achieved. The main reliability parameters values are: the concordance coefficient W amounted to 0.69 – 0.77 by assessing the PI level (88 percent of W>0.7) and to 0.70-0.76 by determining the indicator weights. The concordance coefficient significance parameter χ2 de facto is acceptable at the pre-selected level α= 0.05 and at the pre-selected level α= 0.01, because marginal values min [χ2] are exceed (in detail it is shown in Table 15).

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Table 15 2

Main Reliability Parameters by Expert Assessment (W and χ values) Number of primary indicators

Concordance coefficient W Assessing the primary indicators

The values of W significance χ2 and min [χ2]

Determining the indicator weights

De facto

[χ2] as α= 0.01 [χ2] as α= 0.05

r=9 (d.f.8)

0.69- 0.75

0.70-0.76

22,34

20.090

15.507

n=8 (d.f.7)

0.70-0.77

0.71-0.75

18,97

18.475

14.067

The performed investigation and evaluation of Lithuania’s goods markets competitive indicators show that some primary indicators - degree of customer orientation, prevalence of trade & tariff barriers may be scored 5-6 point (Table 16 and 17), whereas prevalence of foreign ownership, effectiveness of anti-monopoly policy may be evaluated as poor (