**A Development Vision and Strategy Model as a Response of Cities to the Challenges of Globalization**

Anita Maček and Vito Bobek

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/60979

#### **Abstract**

Exposure to new sources of competition across the world encourages cities to become more competitive and to allocate their resources more effectively and efficiently. Responding to the demands of many different groups and managing the allocation of resources between different claims is nowadays one of the most important challenging tasks for city governments.

To reach their desired destination, cities must be aware of where they are starting out. First, they should identify their strengths and weaknesses and after that define the position they want to strive for in the future. By defining the position of the future, they need to be aware of the significant trends and other factors that will influence the direction in which the future unfolds.

To overcome the challenges mentioned, many successful European cities designed a model that simplifies the monitoring of long-term goals. The important thing is that these goals are consistent with the city's development vision and strategy, and both must be based on values, wishes, and priorities of the local residents.

In the proposed chapter, the authors present the vision and strategy model that was developed for Slovenian cities.

**Keywords:** Vision and strategy model, cities, globalization challenges

#### **1. Introduction**

Cities have an important political, social, economic, and cultural role in their regions. They are the foundation of economic development and the core of exchange and trade; they help

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

promote personal freedom and are the centers of creativity, development, and innovation. Globalization has caused many societal changes and influenced the role of cities, which in turn must face the challenges globalization brings about.

There are numerous studies available on the consequences of globalization in various countries, but many fewer focus solely on cities, although the volume of research has been growing in recent years. Analysis of globalization effects on cities in South Africa showed that the economic, social, and cultural consequences are especially noticeable (see [1]). Globalization has caused an increase of foreign direct investment in Indian cities (in [2]) while study (see [3]) shows that direct investments were the main consequences of globalization in Cairo. Study on how globalization affects developing cities highlighted the need for a model which would help those cities overcome the obstacles of globalization (in [4]). Looking into different globalization effects, some studies analyse the connection between cities, trade, and economic growth (see [5]).

The challenges cities face because of globalization are strengthening the need to manage cities more effectively, and they demand the implementation of new decision-making models on all levels. In this chapter, we present the vision and strategy model which could serve as a tool by overcoming globalization challenges.

#### **2. Factors of cities' development**

In the past, various factors influenced the development of cities. With the emergence of globalization, however, this worldwide movement has become the main factor, putting the interplay between the globalization process and the local potential to the forefront and thus creating new opportunities for cities. People's individualism has created a so called I-genera‐ tion (individualistic, informal, informed, interactive, and international), and it affects the dialog between the city managements and the ever more demanding city residents. The third factor of development can be seen in the integration of all fields, which encourages new potential synergies. The development of information and communication technology is enabling an ever faster rate of progress. Through an increasing proportion of elderly popula‐ tions and lower fertility rates, the changing demographic environment in developed countries creates financial pressures on public health and retirement systems, and a smaller base of economically active people is creating new conditions on job markets.

A growing share of urban populations increases the impact cities have on the prosperity and welfare of regions and even entire countries. An important factor of urban development is also the migration of populations, which presents an especially big challenge in the areas of cohesion, integration, and employment, and at the same time generates new opportunities and threats for the creation of social capital, identity, and knowledge potential in cities, regions, and countries. What kind of effects the factors mentioned above have on cities depends on many circumstances; however, some of the effects can be negative and cities can only avoid them with a suitable strategy.

The negative effects of city development primarily apply to the consumption of natural resources, reflected by severe environmental problems, such as polluted air, polluted soil, polluted groundwater and surface water, etc. In some cities, where the population density grew faster than the capacities and plans of local authorities, this has caused social problems. In business, deindustrialization and the implementation of new technologies limited local income and drove some residential quarters to the brink of financial ruin, which also hurt the developmental efforts of entire cities. Various authors have thus developed several kinds of tools for the revival and further development of cities.

The efficiency factors relevant for city development can be grouped into three key groups (in [6-13]:

**•** Economic

promote personal freedom and are the centers of creativity, development, and innovation. Globalization has caused many societal changes and influenced the role of cities, which in turn

There are numerous studies available on the consequences of globalization in various countries, but many fewer focus solely on cities, although the volume of research has been growing in recent years. Analysis of globalization effects on cities in South Africa showed that the economic, social, and cultural consequences are especially noticeable (see [1]). Globalization has caused an increase of foreign direct investment in Indian cities (in [2]) while study (see [3]) shows that direct investments were the main consequences of globalization in Cairo. Study on how globalization affects developing cities highlighted the need for a model which would help those cities overcome the obstacles of globalization (in [4]). Looking into different globalization effects, some studies analyse the connection

The challenges cities face because of globalization are strengthening the need to manage cities more effectively, and they demand the implementation of new decision-making models on all levels. In this chapter, we present the vision and strategy model which could serve as a tool

In the past, various factors influenced the development of cities. With the emergence of globalization, however, this worldwide movement has become the main factor, putting the interplay between the globalization process and the local potential to the forefront and thus creating new opportunities for cities. People's individualism has created a so called I-genera‐ tion (individualistic, informal, informed, interactive, and international), and it affects the dialog between the city managements and the ever more demanding city residents. The third factor of development can be seen in the integration of all fields, which encourages new potential synergies. The development of information and communication technology is enabling an ever faster rate of progress. Through an increasing proportion of elderly popula‐ tions and lower fertility rates, the changing demographic environment in developed countries creates financial pressures on public health and retirement systems, and a smaller base of

A growing share of urban populations increases the impact cities have on the prosperity and welfare of regions and even entire countries. An important factor of urban development is also the migration of populations, which presents an especially big challenge in the areas of cohesion, integration, and employment, and at the same time generates new opportunities and threats for the creation of social capital, identity, and knowledge potential in cities, regions, and countries. What kind of effects the factors mentioned above have on cities depends on many circumstances; however, some of the effects can be negative and cities can only avoid

economically active people is creating new conditions on job markets.

must face the challenges globalization brings about.

58 Perspectives on Business and Management

between cities, trade, and economic growth (see [5]).

by overcoming globalization challenges.

**2. Factors of cities' development**

them with a suitable strategy.


In [6] economic diversity (industrial/service sectors, international/domestic, and large/small companies, old/new economy), educated workforce (availability, demand and supply ratios at universities, research institutes, in governments and in the private sector), connectedness (internal/external, physical/electronic/cultural), ability to lead strategically (vision, leadership, partnerships, politics), knowledge and innovation in companies and other organizations (investment in modern, knowledge-based equipment, investment in research and education, investment in innovation, work productivity), and quality of life (social, cultural, and envi‐ ronmental) are among the most important factors of cities' competitiveness.

The Huggins Index of Urban Competitiveness ([in 14]) emphasizes the importance of the local economy's ability to attract and maintain companies with stable or growing market shares, while simultaneously sustaining or improving the standard of living in the city. The index is divided into several subcategories, including knowledge-based companies, economic activity, company density, GDP per capita, productivity, wages, and unemployment. The urban resources base consists of location, age, favorable economic structure, company characteristics, ability to learn and innovate, communication, high-quality environment and services, and local leadership (see [15]).

"Cities of the future" will need to be able to provide and manage six interrelated forms of capital (strategic assets and resources of the city) (see [9]):


Source: ([9]).

**Figure 1.** Integrating capitals

*Intellectual and social capital* consists of people and knowledge resources, including urban residents' skills, competencies, and know-how. This form of capital is the main success factor for attracting investments. The level of social capital is reflected through the quality of formal and informal relationships in a city and is linked with a low crime rate, a low education level, and a lower level of segmentation and segregation.

*Democratic capital* (transparency, cooperation, and partnerships) stresses the need to include city residents in policy making and decision making. The awareness that city residents are no longer only voters and consumers but also cocreators of policies that shape the future of cities is coming to the forefront (ibid, 2005, p. 4). The information technology boom has made it possible to use democratic capital more extensively, and a number of cities are thus taking advantage of the internet, using it as a new channel for interaction between city leaderships and residents.

*Culture capital and leisure activity capital* encompasses values, behaviors, and public expressions and manifests itself through numerous attributes, which provide the city with a unique identity. Many cities build their unique identities by creating their own trademarks. In addition to a range of high-quality cultural services and lifestyles on offer, those trademarks also draw attention to other city attributes, which help attract and retain people's attention.

*Environmental capital* means natural resources, including clean, green, safe, and attractive environment. Balancing environmental impact with economic development is a big challenge. Pollution is a major problem for many cities, and their policies should therefore incorporate economic and environmental considerations.

*Technical capital* is the city's infrastructure and consists of both the basic infrastructure (housing, transport, water, and energy under strain) and the infrastructure needed for efficient commu‐ nication within the city administration.

*Financial capital* (money and assets) is important because cities nowadays are facing a number of financial challenges. In order to respond to them, cities have to establish accounting policies and analyses that allow them to understand their financial position. Often cities adopt an entrepreneurial approach to financing and providing services.

Although every city has its own unique identity, they face numerous common opportunities and challenges. The diversity and abundance of factors affecting city development require a unified strategy or model for long-term urban development that will effectively include all kinds of capital. Numerous successful European cities have already created their strategies, outlining their transformation into so-called knowledge or creative cities. These strategies are based on encouraging city residents' knowledge, creativity, and innovation, as well as the use of their varied experiences. Most urban city strategies favor a traditional, economic growthoriented development policy, with an emphasis on attracting investors. Cities provide them with the best opportunities, modern infrastructures, highly trained workforce, low taxes, highquality public services, and selective industrial policies that favor investments in modern strategic fields. This kind of policy requires strong support from all public authorities in their respective strategic fields, and also includes the establishment of research institutes and modern education programs.

Source: ([9]).

and residents.

**Figure 1.** Integrating capitals

60 Perspectives on Business and Management

and a lower level of segmentation and segregation.

economic and environmental considerations.

*Intellectual and social capital* consists of people and knowledge resources, including urban residents' skills, competencies, and know-how. This form of capital is the main success factor for attracting investments. The level of social capital is reflected through the quality of formal and informal relationships in a city and is linked with a low crime rate, a low education level,

*Democratic capital* (transparency, cooperation, and partnerships) stresses the need to include city residents in policy making and decision making. The awareness that city residents are no longer only voters and consumers but also cocreators of policies that shape the future of cities is coming to the forefront (ibid, 2005, p. 4). The information technology boom has made it possible to use democratic capital more extensively, and a number of cities are thus taking advantage of the internet, using it as a new channel for interaction between city leaderships

*Culture capital and leisure activity capital* encompasses values, behaviors, and public expressions and manifests itself through numerous attributes, which provide the city with a unique identity. Many cities build their unique identities by creating their own trademarks. In addition to a range of high-quality cultural services and lifestyles on offer, those trademarks also draw

*Environmental capital* means natural resources, including clean, green, safe, and attractive environment. Balancing environmental impact with economic development is a big challenge. Pollution is a major problem for many cities, and their policies should therefore incorporate

attention to other city attributes, which help attract and retain people's attention.

Regardless of which type of strategy is employed, it is important to have one in place in order to define a city's direction of development. If a development strategy is not defined, projects often remain unrealized, not taken advantage of to their full potential, or simply unsuitable for a certain area. In the following subchapter, we are presenting a vision and strategy model that cities can use by planning their development. The authors of this chapter participated by developing a model created based on the needs of the Slovenian market and which has been tested in various Slovene cities with success.

#### **3. The vision and strategy model for strategic management of a city**

The vision and strategy model for Slovenian cities has been developing for years. The research on existing strategies in foreign cities, an analysis of strategic documents at the European and national level, and an analysis of the needs and capabilities of cities in Slovenia served as the basis for the model.

We had several goals in designing the model. First, we wanted to create a model that would facilitate the equal inclusion of opinions and priorities of city residents and other stakeholders of the cities. Within the principles of model transformation, we were mindful of the stability and the efficiency principle. We created a model that makes it possible to define development objectives and measures needed for their realization for a period of up to 20 years.

At its core, the model focuses on five main pillars, which have been identified as those vital for a city's development. They are as follows:


The framework of VIS model is shown in Figure 2 below.

**Figure 2.** The vision and strategy model

All pillars are interconnected and mutually complementary. The main objective of each city is a strong economic position, and this has been primarily taken into account in the model's creation. By "strong economic position," we mean not only economic growth but also economic development, which is a reflection of progressive change in the socioeconomic social structure. The transport and communication pillar has been incorporated into the model because transport is part of the environment, and as a tertiary industry, it contributes greatly to economic development and the interconnection of different places. Transport and communi‐ cation infrastructure has to meet the needs of economic development. In addition to adequate construction and the technical state of the transport network, there is a need for good-quality and high-capacity roads, which have a positive impact on economic activities and traffic accessibility. An important role must also be given to environmental protection in the creation of the long-term development vision and strategy. The environment pillar thus includes the prevention of biodiversity loss as well as the implementation of efficient and smart electric power grids and the development of a more competitive low-carbon economy, which effi‐ ciently and sustainably uses resources and other forms of environmental protection. The fourth important pillar of the vision and strategy model is education. It is a fact that we live in a knowledge-based society and that the skills of residents influence their lives in the greatest possible way. We are all aware that knowledge is the best investment; therefore, personal development and growth have also been included in the vision and strategy model. The last pillar of the model is the quality of life, including cultural events, accessible sports and recreational facilities, social activities, residence safety, neighborly relations, and spiritual care. In all phases of the model, attention is directed toward the above-mentioned pillars, and each step of the model is structured around those pillars.

The model methodology demands the completion of a structured questionnaire among various interest groups, a field survey on residents' life satisfaction, a survey for the definition of the selected city's identity (recognizability), and an analysis of existing statistical indicators for the city. Below, we present an example step-by-step representation of the vision and strategy model.

The above picture shows the process, which consists of six steps. The first step of the process **Sheme 1.** The process for the creation of city vision and strategy model

environment for the selected city are defined.

is an analysis of the city's current situation. The above picture shows the process, which consists of six steps. The first step of the process is an analysis of the city's current situation.

#### *1. The AS-IS analysis*  **3.1. The AS-IS analysis**

includes the following:

At its core, the model focuses on five main pillars, which have been identified as those vital

All pillars are interconnected and mutually complementary. The main objective of each city is a strong economic position, and this has been primarily taken into account in the model's creation. By "strong economic position," we mean not only economic growth but also economic development, which is a reflection of progressive change in the socioeconomic social structure. The transport and communication pillar has been incorporated into the model because transport is part of the environment, and as a tertiary industry, it contributes greatly to economic development and the interconnection of different places. Transport and communi‐ cation infrastructure has to meet the needs of economic development. In addition to adequate construction and the technical state of the transport network, there is a need for good-quality and high-capacity roads, which have a positive impact on economic activities and traffic accessibility. An important role must also be given to environmental protection in the creation

for a city's development. They are as follows:

The framework of VIS model is shown in Figure 2 below.

**•** Transport and communication

62 Perspectives on Business and Management

**Figure 2.** The vision and strategy model

**•** Economy

**•** Environment **•** Quality of life **•** Education

> The AS-IS analysis is an evaluation of the current situation in a city. The first step encompasses a precise definition of demographic and social indicators, economic indicators, and environmental and natural resource-related indicators. In general, a study of available capital is conducted; strengths, weaknesses, opportunities, and threats are explored, and at the same time, the location's attractiveness and the importance of economic and sociopolitical The AS-IS analysis is an evaluation of the current situation in a city. The first step encompasses a precise definition of demographic and social indicators, economic indicators, and environ‐ mental and natural resource-related indicators. In general, a study of available capital is conducted; strengths, weaknesses, opportunities, and threats are explored, and at the same time, the location's attractiveness and the importance of economic and sociopolitical environ‐ ment for the selected city are defined.

Besides a statistical analysis of publicly accessible statistical data, the AS-IS analysis also



of defining the selected city's recognizability in the region

process is the evaluation of the selected city's competitiveness.

potential. In-depth interviews are carried out with those individuals.

9

Based on the data gathered with the help of the above-mentioned statistical methods, development potential and possible obstacles to development are defined. Within the AS-IS analysis, particular attention is paid to the city's budget, which is an important basis for later steps and for the definition of an individual city's competitiveness. The next step in the

Besides a statistical analysis of publicly accessible statistical data, the AS-IS analysis also includes the following:


Based on the data gathered with the help of the above-mentioned statistical methods, devel‐ opment potential and possible obstacles to development are defined. Within the AS-IS analysis, particular attention is paid to the city's budget, which is an important basis for later steps and for the definition of an individual city's competitiveness. The next step in the process is the evaluation of the selected city's competitiveness.

#### **3.2. Competitiveness evaluation**

Competitiveness is defined with the help of two methods—the benchmarking method and the SWOT analysis. Publicly accessible data from the Statistical Office and data gathered with the help of questionnaires, filled out by residents and various interest groups already in the first phase (AS-IS analysis), comprise the main statistical base for the competitiveness evaluation.

With the competitiveness evaluation, we also define general strategic questions that a city is faced with on a daily basis. We use the following matrix to define strategic issues for the city (Table 1).



**Table 1.** Strategic issues relevant for the city

With the help of the matrix above, we can analyze in which stage the city is and define the most important strategic questions and orientation our model of vision and strategy should answer.

#### **3.3. Creating the vision**

Besides a statistical analysis of publicly accessible statistical data, the AS-IS analysis also

**•** A public opinion analysis among residents of other cities in the country with the goal of

**•** An analysis of individual responses from a sample of focus groups, which serve to gather information on the *status quo* of various aspects of city life. The individual focus group participants are carefully selected, and some of them are visible members of the city in one of the fields that could turn out to represent the city's development potential. In-depth

Based on the data gathered with the help of the above-mentioned statistical methods, devel‐ opment potential and possible obstacles to development are defined. Within the AS-IS analysis, particular attention is paid to the city's budget, which is an important basis for later steps and for the definition of an individual city's competitiveness. The next step in the process

Competitiveness is defined with the help of two methods—the benchmarking method and the SWOT analysis. Publicly accessible data from the Statistical Office and data gathered with the help of questionnaires, filled out by residents and various interest groups already in the first phase (AS-IS analysis), comprise the main statistical base for the competitiveness evaluation.

With the competitiveness evaluation, we also define general strategic questions that a city is faced with on a daily basis. We use the following matrix to define strategic issues for the city

**Symptoms Strategic orientation/questions**

The city needs

culture



essential city services at lower cost - to reverse trends in a city

The city needs the appropriate - assistance of a transformation of city


**•** A public opinion analysis based on a life satisfaction survey among city residents

defining the selected city's recognizability in the region

interviews are carried out with those individuals.

is the evaluation of the selected city's competitiveness.

**3.2. Competitiveness evaluation**

Declining city - High level of inactive population

various years


outdate industries





stable or growing economic center - Very few income generating activities

(Table 1).

The city is in transition

**Broad strategic context**

includes the following:

64 Perspectives on Business and Management

Some cities and people see the vision and strategy as a piece of paper or as a tool for political promotion. In forming the model, we avoided this risk by including stakeholders and their ideas in the process of forming the vision of the city.

When creating the vision, we took into consideration the fact that every city needs a strong and interesting vision, which then serves as the basis of its long-term development strategy. It is important that the vision include stakeholders and their ideas from various fields, but simultaneously the general public must be made familiar with the vision in order to build their enthusiasm around it and enable them to identify with it. The creation of the development vision is based on the AS-IS analysis and the competitiveness evaluation.

Residents' values, including traditional and postmodern values, also play an important role in defining the vision. At the same time, indicators measuring the performance and potential of a city are also taken into consideration when creating the vision.

In this stage, we use the prioritization matrix, for which we formed an automated template in an Excel spreadsheet. This spreadsheet includes criteria for judging priorities.

In the next step, the development vision's compliance with existing strategic development documents on the EU and national levels are checked.

#### **3.4. Compliance of vision with EU and national policies**

In this step, the residents' knowledge of EU policies and national strategies is examined, and at the same time, the new city's vision is checked against those documents. First, compliance is checked against development documents at the EU level, then the state, then at the regional level, and finally at the city level. Simultaneously, a list of existing projects is made and their alignment with the new vision is reviewed as well. The next step is the definition of key objectives and operative programs.

#### **3.5. The definition of key objectives and the design of operative programs**

Key objectives and operative programs are prepared for each of the above-mentioned five pillars (economy, transport and communication, environment, education, and quality of life).

Each of the five development pillars is divided into three parts. Part one shows elements from the residents' point of view and it reflects their perception of the current situation and certain expectations. In the second part, clear, quantified, realistic, and challenging objectives are set, which form the basis of a transparent system for monitoring the execution of the development strategy. Quantified objectives are also complemented by values or satisfaction of residents. This ensures a permanent participation of the residents. The third part of each development pillar is a pool of potential concrete measures for the attainment of objectives set. The pool of measures stems from existing documents, the city's legal obligations, and best practice case studies, and it serves as a reminder for the actions taken by the city council and the city administration. Based on the measures pool, the city prepares project ideas in advance so that they are ready to be undertaken.

#### **3.6. The definition of indicator values for the monitoring of strategy implementation**

Using the statistical tool "expert choice," the last step consists of setting the indicator target values, applicable for the same period of time as the vision. Based on our experiences, 20 years is the most relevant period. After the calculated final values, which are influenced by weights assigned to individual indicators, cities have an efficient tool to monitor strategy implemen‐ tation progress.

When the process of creating a vision and strategy model is completed, the strategic develop‐ ment document must then be presented to city residents. Awareness of the direction of future development gives residents additional motivation and desire to be involved in the realization of measures that will contribute to the city's development.

#### **4. Introduction of the model in practice**

The model is designed in such a way that it can be used for any city. A committed city leadership, which motivates residents and creates an atmosphere where everyone wants to contribute to development, greatly contributes to the model's more efficient realization. Residents' values, which form the foundation of the vision, make it possible for individual cities to differ from their competitors, and they make it possible for the city to stand out and get noticed to the fullest possible extent. The model's introduction requires the prudent application of strategic thinking at all levels and in all dimensions. The special added value of the model, however, lies in residents' participation in all phases of the creation of the city's vision and strategy. The development vision and strategy created by using the model pre‐ sented above will give the city answers to the following questions:


Of course, the newly created development vision and strategy only represent the first step, which must be followed up by building trust in the city, establishing partnerships in various directions and on various levels, and attracting key factors necessary for the development and realization of projects that support the city's developmental objectives. This is a long-term process, which due to residents' participation from the early start, their inclusion in the projects, and their influence on the indicators facilitate a broad consensus that minimizes the possibilities of everyday political meddling in the city's development.

The model gives cities the ability to compete and position themselves in order to provide quality of life, jobs, and services that attract business and people. Cities with vision and strategy have better conditions for funding city projects via banks, national funding, or EU funding.

To this day, the vision and strategy model has been carried out in more than 20 Slovenian cities. For all these cities, the model has helped us define specific strategic goals and the way to achieve them. The model pays special attention to practical aspects of strategy delivery. Moreover, the model also emphasizes the significance of an integrated approach and the importance of building partnerships.

#### **5. Conclusion**

In the next step, the development vision's compliance with existing strategic development

In this step, the residents' knowledge of EU policies and national strategies is examined, and at the same time, the new city's vision is checked against those documents. First, compliance is checked against development documents at the EU level, then the state, then at the regional level, and finally at the city level. Simultaneously, a list of existing projects is made and their alignment with the new vision is reviewed as well. The next step is the definition of key

Key objectives and operative programs are prepared for each of the above-mentioned five pillars (economy, transport and communication, environment, education, and quality of life). Each of the five development pillars is divided into three parts. Part one shows elements from the residents' point of view and it reflects their perception of the current situation and certain expectations. In the second part, clear, quantified, realistic, and challenging objectives are set, which form the basis of a transparent system for monitoring the execution of the development strategy. Quantified objectives are also complemented by values or satisfaction of residents. This ensures a permanent participation of the residents. The third part of each development pillar is a pool of potential concrete measures for the attainment of objectives set. The pool of measures stems from existing documents, the city's legal obligations, and best practice case studies, and it serves as a reminder for the actions taken by the city council and the city administration. Based on the measures pool, the city prepares project ideas in advance so that

**3.6. The definition of indicator values for the monitoring of strategy implementation**

Using the statistical tool "expert choice," the last step consists of setting the indicator target values, applicable for the same period of time as the vision. Based on our experiences, 20 years is the most relevant period. After the calculated final values, which are influenced by weights assigned to individual indicators, cities have an efficient tool to monitor strategy implemen‐

When the process of creating a vision and strategy model is completed, the strategic develop‐ ment document must then be presented to city residents. Awareness of the direction of future development gives residents additional motivation and desire to be involved in the realization

The model is designed in such a way that it can be used for any city. A committed city leadership, which motivates residents and creates an atmosphere where everyone wants to

of measures that will contribute to the city's development.

**4. Introduction of the model in practice**

**3.5. The definition of key objectives and the design of operative programs**

documents on the EU and national levels are checked.

objectives and operative programs.

66 Perspectives on Business and Management

they are ready to be undertaken.

tation progress.

**3.4. Compliance of vision with EU and national policies**

Many challenges brought about by globalization have encouraged cities to orient themselves toward the future. Some have done that successfully and some are taking small steps with the help of individual projects and are therefore less successful. One of the reasons for a lower success rate is the absence of a development vision and strategy.

The above-presented process for developing the vision and strategy model of a city enabled its users to understand and analyze what will be at the center of attention for their city in the future. The process consists of six steps, and its successful implementation in practice depends on several different factors. One such important factor is the courage of the city's leadership to test new ideas and to encourage a robust dialogue between politicians, the administration, companies, associations, and individuals. The fact that the model foresees the inclusion of residents, various interest groups, experts from various fields, and the city's leadership in equal measure shows that the creators of the model are well aware that a strategy can only be successful if people recognize the fulfillment of their wishes and expectations in it. Practice shows that objectives, plans, and strategies in themselves do not lead to success; only people and the values that guide their realization can achieve this.

### **Author details**

Anita Maček1\* and Vito Bobek<sup>2</sup>


#### **References**


The above-presented process for developing the vision and strategy model of a city enabled its users to understand and analyze what will be at the center of attention for their city in the future. The process consists of six steps, and its successful implementation in practice depends on several different factors. One such important factor is the courage of the city's leadership to test new ideas and to encourage a robust dialogue between politicians, the administration, companies, associations, and individuals. The fact that the model foresees the inclusion of residents, various interest groups, experts from various fields, and the city's leadership in equal measure shows that the creators of the model are well aware that a strategy can only be successful if people recognize the fulfillment of their wishes and expectations in it. Practice shows that objectives, plans, and strategies in themselves do not lead to success; only people

and the values that guide their realization can achieve this.

\*Address all correspondence to: anita.macek@net.doba.si

ahmed031210.htm [Accessed: 10.03.2015].

mccann%20paper.pdf. [Accessed: 3.6.2013].

*Social Science*; 12:199–205.

1 DOBA Faculty of Applied Business and Social Studies Maribor, Slovenia

[1] McLachlan, G. 2001. The impact of globalization on the cities of southern Africa: a case study of the Port Elizabeth Metropolitan Area [Internet]. Available from: http:// www.isocarp.net/Data/case\_studies/cases/cs01\_4568/gmpaper.htm [Accessed:

[2] Mathur, P. Impact of globalization on cities and city-related policies in India. *Globaliza‐ tionandUrbanDevelopment*,pp. 43–58 [Internet]. 2005.Available from:http://link.spring‐

[3] Ahmed, S. N. 2010. *Impact of Globalization On A Southern Cosmopolitan City (Cairo): A Human Rights Perspective* [Internet]. Available from: http://www.countercurrents.org/

[4] Oduwaye, L. 2006. Effects of globalization on cities in developing countires. *Journal of*

[5] McCann, P., and Acs, Z. J. 2007. *Globalisation: Countries, Cities and Multinationals* [Inter‐ net]. Available from: http://www2.econ.uu.nl/users/marrewijk/pdf/ihs%20workshop/

er.com/chapter/10.1007%2F3-540-28351-X\_4?LI=true [Accessed: 20.12.2014].

**Author details**

**References**

Anita Maček1\* and Vito Bobek<sup>2</sup>

68 Perspectives on Business and Management

2 FH Joanneum Graz, Austria

10.03.2015].


## **Measuring Urban Development and City Performance**

Jasmina Mavrič and Vito Bobek

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/61063

#### **Abstract**

Cities represent the driving force of development in economic, social, and cultural life, reflecting also the spatial organization of human society. Taking into account the fact that cities are becoming generators of economic development and a source of growth for the national economy, there is an increasing urge to identify the stages of development and to establish a system for the ranking and positioning of cities and regions in this process (the level of categorization). This will allow the preparation of appropriate strategic and development guidelines for cities and urban regions to take place. In order to be able to compare the level of their efficiency in fostering develop‐ ment, there is an intensifying need to develop indicators that measure the performance of cities, are representative and comparable between countries, and allow verification to others. At present, there are many different urban indicators and institutions that compile and analyze them. Performance measurement systems, developed for internal use in some cities, already show a degree of measurement feasibility. The fundamental problem is that this variety of indicators lacks consistency and compa‐ rability (over time and between compared cities). Therefore, their use cannot be approved in a wider context (benchmark) of comparative situations. Upon the case of medium-sized cities, we consequently have to question the applicability of the methodology and indicators, used mostly in cases of large, global cities by interna‐ tionally recognized institutions. With the established set of indicators and assistance of computer programs for multiparameter decision-making processes (analytic hierarchical process [AHP]), this paper also seeks to investigate comparisons between performance of selected European cities (on a qualitative basis).

**Keywords:** city, region, urban development, globalization, urbanization, measure‐ ment systems, development of urban indicators, indicators, city ranking, quality of life, competitiveness

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### **1. Introduction**

Existing methodologies in comparison to performance and quality of urban city structure affect more or less a wider field of urban and regional disparities, while specific approaches cover only limited areas. [34] focuses exclusively on infrastructure impacts, Callois and Aubert (2007) empirically analyze the impact of social capital on regional development. The advantage of quoted approaches represents limited number of variables involved in analysis. In the area of measuring the quality of life, [53] provide an overview of indicators of sustainable devel‐ opment, as well as [54] and [55], but the interpretation of the indicators of quality of life is missing. In the field of competitiveness, [62] presents the synopsis of indicators measuring urban competitiveness on a European scale, while [39] indicate the multicast nature of sustainable development that consequently leads to the unclear definition of the measuring indicator. Missing thematic indicators can also be found in the context of measuring regional disparities, both at the level of the broader European countries [58] and in the narrow sense of the regions [36]. Comparing cities by the use of indicators that represent diverse aspects of urban life is only possible with the meaningful structured set system; easily adding a large number of indicators to achieve a single index may result in criticism of uncertainty and noticeable limitation of its interpretation. Similar effects can also be achieved by using a larger set of nonaggregated indicators; therefore, identification of appropriate, small number of relevant indicators is crucial. In the process of establishing the set of indicators, the inclusion of indicators with higher impact on the general differences between selected cities in different countries is necessary; an additional assumption incorporates the integration of indicators from the field of environmental, human, and social capital as well as the demographic point of view.

#### **2. Theoretical background and applied practices**

When searching for the most relevant performance indicators of city development, we proceed from the fact that more than two-thirds of the population live in urban areas. The urban environment provides a fertile ground for the development of science and technology, culture, and innovation. On the other hand, in cities, there is also more emphasis on the problems such as unemployment, discrimination, segregation of society, and poverty. The cities are also faced by challenges, associated with mitigating the effects of climate change, job creation, prosperity, and quality of life. Therefore, the development of cities has a decisive impact on the future of the economic, social, and territorial development. As highlighted by the recent European Commission survey entitled "Cities of the Future—Challenges, Ideas and Expectations" (EC, 2011), a phase of urban sprawl in recent decades has shown serious problems associated with the deterioration of urban areas due to lack of infrastructure construction and basic services. Promoting urban renewal as the driving force of prosperity and creating opportunities together with strengthening the link between cities and development, and between urban centers and surrounding areas, are the main challenges to provide stable economic growth.

Establishing a system of indicators for measuring performance development of selected cities included the consideration of contemporary city's complex aspects with reference to (a) the 72 attributes of a smart city,1 (b) the performance of the city, and (c) urban status or urban sustainability.

The case study example of Glasgow classifies city performance indicators as follows: (1) population (mortality, fertility, population projections), (2) economic participation (employ‐ ment, unemployment, vacancies), (3) poverty (access to bank accounts, children poverty, financial hardship, low-income households), (4) health (life expectancy, inability to work), (5) social capital (social inclusion, social networks, trust and reciprocity, civic participation), (6), environment (green environment, open space, air quality, recycling), (7) transport (transport volume, journeys to work and school, traffic accidents, cycling), (8) education (children education, the highest qualification obtained, the qualification of the working population, training of young people), (9) safety of local communities (overall level of crime, antisocial behavior, violence, unintentional injuries), (10) lifestyle, (11) cultural vitality (involvement in sport and cultural events), and (12) mind-set (religion, politics, involvement in the community, trust, national identity).

Indicators of sustainable development reflect the complex and dynamic structure of the urban environment. With the adoption of Agenda 21 (1992), this type of indicator was developed by a number of institutions, including the World Bank (UN—Urban Indicators Programme), followed by indicators of the World Health Organization (WHO), as the analytical tools for studying population health and quality of life in urban environment. A wider set of indicators also includes project SUD-LAB EC (European Commission) with an expanded database of European cities, where indicators are divided into the following categories: (a) air quality, (b) composed environment, (c) cultural endowments, (d) social disparities, (e) quality of transport, (f) urban management, and (g) waste management. For each of listed categories, a set of indicators is reflecting the level of urban functionality. Indicators of the EU-TISSUE Pro‐ gramme, in use in 15 European countries, relate to the areas of sustainable urban management (descriptive indicators), sustainable urban transport, sustainable urban construction, and sustainable urban design [2].

In accordance with the Charter on European Sustainable Cities and Towns, [60] lists six key areas of sustainable development and urban transformation: (1) active city/town, (2) beautiful town, (3) green city/town, (4) town with a better environment, (5) cooperation for a better city, and (6) town catalogue. The strategy of urban sustainability consequently includes urban sustainability performance indicators such as (1) local involvement (citizen's participation); (2) employment; (3) city deficit; (4) economic growth; (5) urban mobility; (6) urban metabolism, resources, and consumption; (7) environment and social expenditure; (8) urban safety; (9) public health; (10) social justice; and (11) global change.

Among indicators of central city area development, Niţulescu (2000) includes the following: (1) types of land using (constructions, green areas), (2) green areas surface from the total town center's surface, (3) percent of residential buildings from the total number of buildings from

**1. Introduction**

72 Perspectives on Business and Management

of view.

**2. Theoretical background and applied practices**

Existing methodologies in comparison to performance and quality of urban city structure affect more or less a wider field of urban and regional disparities, while specific approaches cover only limited areas. [34] focuses exclusively on infrastructure impacts, Callois and Aubert (2007) empirically analyze the impact of social capital on regional development. The advantage of quoted approaches represents limited number of variables involved in analysis. In the area of measuring the quality of life, [53] provide an overview of indicators of sustainable devel‐ opment, as well as [54] and [55], but the interpretation of the indicators of quality of life is missing. In the field of competitiveness, [62] presents the synopsis of indicators measuring urban competitiveness on a European scale, while [39] indicate the multicast nature of sustainable development that consequently leads to the unclear definition of the measuring indicator. Missing thematic indicators can also be found in the context of measuring regional disparities, both at the level of the broader European countries [58] and in the narrow sense of the regions [36]. Comparing cities by the use of indicators that represent diverse aspects of urban life is only possible with the meaningful structured set system; easily adding a large number of indicators to achieve a single index may result in criticism of uncertainty and noticeable limitation of its interpretation. Similar effects can also be achieved by using a larger set of nonaggregated indicators; therefore, identification of appropriate, small number of relevant indicators is crucial. In the process of establishing the set of indicators, the inclusion of indicators with higher impact on the general differences between selected cities in different countries is necessary; an additional assumption incorporates the integration of indicators from the field of environmental, human, and social capital as well as the demographic point

When searching for the most relevant performance indicators of city development, we proceed from the fact that more than two-thirds of the population live in urban areas. The urban environment provides a fertile ground for the development of science and technology, culture, and innovation. On the other hand, in cities, there is also more emphasis on the problems such as unemployment, discrimination, segregation of society, and poverty. The cities are also faced by challenges, associated with mitigating the effects of climate change, job creation, prosperity, and quality of life. Therefore, the development of cities has a decisive impact on the future of the economic, social, and territorial development. As highlighted by the recent European Commission survey entitled "Cities of the Future—Challenges, Ideas and Expectations" (EC, 2011), a phase of urban sprawl in recent decades has shown serious problems associated with the deterioration of urban areas due to lack of infrastructure construction and basic services. Promoting urban renewal as the driving force of prosperity and creating opportunities together with strengthening the link between cities and development, and between urban centers and surrounding areas, are the main challenges to provide stable economic growth.

<sup>1</sup>http://www.smart-cities.eu.

the center of the town, (4) percent of trade buildings from the total number of buildings from the town center, (5) percent of central functions buildings (administrative, international, unique endowment) from the total number of buildings from the center of the town, (6) built areas of public utility related to then inhabited areas, (7) employment density (number of working places related to the town center surface), (8) rate of employed population for each sector (industry, trade, services), (9) number of crossroads for the surface of the town center, (10) surface of pedestrian circulation for the surface of the town, and (11) surface of pedestrian circulation for the surface of roadway [2].

Among indicators of urban status ranks, Şuler's (2005) category of population and labor force indicators are as follows: (1) number of inhabitants, (2) population density (per hectare), (3) working places/1000 inhabitants, and (4) proportion of the population employed in the service sector. The category of living and quality of life indicators include the following: (5) number of residential buildings per 1000 inhabitants, (6) houses equipped with plumbing (% of buildings), (7) number of personal cars per 1000 inhabitants, (8) number of houses with bathrooms inside the building, (9) number of hospital beds per 1000 inhabitants, (10) number of doctors per 1000 persons, (11) financial/banking institutions (headquarters, working points), and (12) accessibility to lines of communication (railway station, bus station). The indicators of category society, culture, and leisure include the following: (13) education units (high school, secondary, postsecondary school), (14) secondary school in primary and secondary educa‐ tional units (%), (15) cultural and sports endowments (theaters, public libraries, gyms, auditorium, stadium), and (16) accommodation places/1000 inhabitants. Indicators of the urban network are specified as follows: (17) modernized streets (%), (18) streets with water pipes (%), (19) waste water treatment, (20) household gas distribution pipes (%), (21) sanitation motor vehicles for 100 km of street, (22) scavengers for 1000 inhabitants, and (23) green area surface m2 /inhabitant.

[2] defines an index of local development as an integrated indicator, including the importance (weights) of individual elements as category of infrastructure (4), followed by the economy (3), community (2), and the public administration (1):

$$I\_{dl} = \left[ \left( I\_i \times \mathbf{4} \right) + \left( I\_c \times \mathbf{3} \right) + \left( I\_{nc} \times \mathbf{2} \right) + \left( I\_{ap} \times \mathbf{1} \right) \right] / \text{ 10},\tag{1}$$

where *I*dl is the local development index, *I*<sup>i</sup> is the infrastructure index, *I*<sup>e</sup> is the local economy index, *I*mc is the local community index, and *I*ap is the public administration index.

Category infrastructure includes utilities, transport infrastructure, health infrastructure, natural resources, and natural environment. Economy includes financial services and insur‐ ance, labor, and public budget. Public administration includes public administration, services and support to small and medium-sized enterprises, urban planning, communication, and information dissemination. Among the indicators of development, Bӑnicӑ (2010) introduces the community spirit, safety of citizens, tourist attractions, cultural/sports facilities, and cultural/historical heritage.

[63] formed an indicator of the public urban transport quality using available Eurostat database indicators, including the following subindicators: (1) the proportion of journeys to work by public transport, (2) the length of the public transport network, (3) the number of stops of public transport per km2 , (4) the price of a monthly public transport ticket, (5) the number of stops per 1000 population, (6) the number of stops per 1 km of public transport network, (7) the ratio between the public transport network on fixed infrastructure and flexible connections, and (9) the proportion of land for transport use.

World Bank Urban Development Indicators include the following: (1) proportion of urban population with access to improved health services, (2) proportion of urban population with access to water resources, (3) number of motor vehicles per 1000 population, (4) number of passenger cars per 1000 inhabitants, (5) emissions of PM10 (micrograms per m3 ), (6) proportion of poverty, (7) fuel prices, (8) fuel consumption per capita, and (9) the percentage of the urban population.

Significant importance among urban sustainability indicators belongs to European Common Indicators (ECI), first established in 1999–2003, by the research institute Ambiente Italia. Within a base with more than 1000 indicators, 10 key indicators, reflecting the development trends of European cities in accordance with the principles of social inclusion, local governance and democracy, local/global integration of the city, local economy, environment, cultural heritage, and quality of the institutional environment was selected: (1) citizens' satisfaction with the local community, (2) local contribution to global climate change (CO2 emissions per capita), (3) local mobility and passenger transportation, (4) availability of local public open areas and services, (5) quality of the air (emissions of PM10), (6) children's journeys to and from school, (7) sustainable management of the local authority and local enterprises, (8) noise pollution, (9) sustainable land use, and (10) products promoting sustainability.

With reference to the cited attributes of a smart city,2 city performance, and urban sustaina‐ bility, a system of indicators, whose structure is presented in the following text, for measuring performance development of the city was developed. Areas of measurement, enabling the international comparison of cities, covered six areas: (1) demography, labor market, and economy; (2) quality of life; (3) society, culture, and leisure activities; (4) research and devel‐ opment; (5) accessibility of urban networks and international connectivity; and (6) manage‐ ment of sustainable resources. Within listed areas of measurement system, categories enabling grouping of individual indicators and appropriate weighting of the their relative importance were set. Relevant indicators resulted from knowledge of current topics and problems of urban development as well as the renewal priorities of local development model. The indicator system, based on current challenges of a multicultural society, was reaching the areas within the sphere of local communities, trust in institutions, prosperity, quality of life, environmental change, social exclusion, unemployment, poverty, polarization, and demographic changes. From this perspective, it can be regarded as a dynamic system, where 53 selected indicators serve as a basis, always possible to upgrade and adapt to the situation and degree of urban development.

the center of the town, (4) percent of trade buildings from the total number of buildings from the town center, (5) percent of central functions buildings (administrative, international, unique endowment) from the total number of buildings from the center of the town, (6) built areas of public utility related to then inhabited areas, (7) employment density (number of working places related to the town center surface), (8) rate of employed population for each sector (industry, trade, services), (9) number of crossroads for the surface of the town center, (10) surface of pedestrian circulation for the surface of the town, and (11) surface of pedestrian

Among indicators of urban status ranks, Şuler's (2005) category of population and labor force indicators are as follows: (1) number of inhabitants, (2) population density (per hectare), (3) working places/1000 inhabitants, and (4) proportion of the population employed in the service sector. The category of living and quality of life indicators include the following: (5) number of residential buildings per 1000 inhabitants, (6) houses equipped with plumbing (% of buildings), (7) number of personal cars per 1000 inhabitants, (8) number of houses with bathrooms inside the building, (9) number of hospital beds per 1000 inhabitants, (10) number of doctors per 1000 persons, (11) financial/banking institutions (headquarters, working points), and (12) accessibility to lines of communication (railway station, bus station). The indicators of category society, culture, and leisure include the following: (13) education units (high school, secondary, postsecondary school), (14) secondary school in primary and secondary educa‐ tional units (%), (15) cultural and sports endowments (theaters, public libraries, gyms, auditorium, stadium), and (16) accommodation places/1000 inhabitants. Indicators of the urban network are specified as follows: (17) modernized streets (%), (18) streets with water pipes (%), (19) waste water treatment, (20) household gas distribution pipes (%), (21) sanitation motor vehicles for 100 km of street, (22) scavengers for 1000 inhabitants, and (23) green area

[2] defines an index of local development as an integrated indicator, including the importance (weights) of individual elements as category of infrastructure (4), followed by the economy

= ´+´+ 4 3 2 1 / 10, ( ) ( ) ( ) ( ) é ù

index, *I*mc is the local community index, and *I*ap is the public administration index.

where *I*dl is the local development index, *I*<sup>i</sup> is the infrastructure index, *I*<sup>e</sup> is the local economy

Category infrastructure includes utilities, transport infrastructure, health infrastructure, natural resources, and natural environment. Economy includes financial services and insur‐ ance, labor, and public budget. Public administration includes public administration, services and support to small and medium-sized enterprises, urban planning, communication, and information dissemination. Among the indicators of development, Bӑnicӑ (2010) introduces the community spirit, safety of citizens, tourist attractions, cultural/sports facilities, and

ë û ´+ ´ *dl i <sup>e</sup> mc ap II I I I* (1)

circulation for the surface of roadway [2].

74 Perspectives on Business and Management

surface m2

/inhabitant.

cultural/historical heritage.

(3), community (2), and the public administration (1):

<sup>2</sup> Specifically explained in Section 6.

#### **3. Selection of indicators**

The selection of appropriate indicators included research and exploration, evaluation, and selection of relevant databases, through which adequate indicators of measurement as a basis for determining the level of the city performance development and consequently a useful tool for ranking of comparable medium-sized European cities was obtained. Indicators in the study were selected on the basis of following assumptions: (1) objectivi‐ ty (clear, easy to understand, precise, and unambiguous); (2) relevance, measurability, and reproducibility (quantitative, systematic observable); (3) validity (with the possibility of verification and data quality control); (4) statistical representativeness (at the city level); (5) comparability/standardization—longitudinal (over time) and transverse (between cities); (6) flexibility (with the possibility of continuous improvement); (7) efficiency/performance (as decision making and local management planning tool); (8) accessibility (available databas‐ es, use of existing data); (9) interaction (social, environmental, economic); and (10) consisten‐ cy and temporal stability. Last but not least, the selection of appropriate indicators was also related to the concept of data homogeneity. In searching for the relevant data, many of the existing semantic information about the state of the city and urban region were expected to be available; therefore, the data credibility was highlighted.

#### **4. Selection of cities**

In Europe, more than 600 cities and urban regions are classified as medium-sized with a population between 100,000 and 500,000 inhabitants (selection criteria). In the case of a single manual data collection, the data processing for such number of cities are practical‐ ly impossible. Therefore, the reselection of urban sample in terms of a data source (all selected cities should be covered by a specific source, e.g., Urban Audit) was necessary to eliminate the risk of the diverse resources' use, related to the area and the region of the city, induced by the dimension of the selected city sample. In case of insufficient data, the use of different spatial levels (Eurostat database is corresponding to NUTS2, representing regions and provinces, while the Eurobarometer data correspond to NUTS0/national level) was imminent. Quoted databases focus on the European cities' profiles, which further narrowed the selection frame. The final selection of cities was defined (Table 2) on the basis of the following: location (criterion 1: all selected cities are located in Europe), database (criterion 2: inclusion in the database Urban Audit), definition in terms of a smart city (criterion 3: placed in the "Smart Cities" base), comparability in terms of urban size (criterion 4: comparable population size: medium-sized cities with the range of 100,000 to 200,000 inhabitants), and regional significance (criterion 5: capital region or important regional center). With reference to fulfilled criteria, research cities represented Maribor (Slovenia), Pleven (Bulgaria), Linz (Austria), Erfurt (Germany), Trieste (Italy), and Brugge (Belgium).


**3. Selection of indicators**

**4. Selection of cities**

The selection of appropriate indicators included research and exploration, evaluation, and selection of relevant databases, through which adequate indicators of measurement as a basis for determining the level of the city performance development and consequently a useful tool for ranking of comparable medium-sized European cities was obtained. Indicators in the study were selected on the basis of following assumptions: (1) objectivi‐ ty (clear, easy to understand, precise, and unambiguous); (2) relevance, measurability, and reproducibility (quantitative, systematic observable); (3) validity (with the possibility of verification and data quality control); (4) statistical representativeness (at the city level); (5) comparability/standardization—longitudinal (over time) and transverse (between cities); (6) flexibility (with the possibility of continuous improvement); (7) efficiency/performance (as decision making and local management planning tool); (8) accessibility (available databas‐ es, use of existing data); (9) interaction (social, environmental, economic); and (10) consisten‐ cy and temporal stability. Last but not least, the selection of appropriate indicators was also related to the concept of data homogeneity. In searching for the relevant data, many of the existing semantic information about the state of the city and urban region were expected

In Europe, more than 600 cities and urban regions are classified as medium-sized with a population between 100,000 and 500,000 inhabitants (selection criteria). In the case of a single manual data collection, the data processing for such number of cities are practical‐ ly impossible. Therefore, the reselection of urban sample in terms of a data source (all selected cities should be covered by a specific source, e.g., Urban Audit) was necessary to eliminate the risk of the diverse resources' use, related to the area and the region of the city, induced by the dimension of the selected city sample. In case of insufficient data, the use of different spatial levels (Eurostat database is corresponding to NUTS2, representing regions and provinces, while the Eurobarometer data correspond to NUTS0/national level) was imminent. Quoted databases focus on the European cities' profiles, which further narrowed the selection frame. The final selection of cities was defined (Table 2) on the basis of the following: location (criterion 1: all selected cities are located in Europe), database (criterion 2: inclusion in the database Urban Audit), definition in terms of a smart city (criterion 3: placed in the "Smart Cities" base), comparability in terms of urban size (criterion 4: comparable population size: medium-sized cities with the range of 100,000 to 200,000 inhabitants), and regional significance (criterion 5: capital region or important regional center). With reference to fulfilled criteria, research cities represented Maribor (Slovenia), Pleven (Bulgaria), Linz (Austria), Erfurt (Germany), Trieste (Italy), and Brugge (Belgium).

to be available; therefore, the data credibility was highlighted.



**Table 1.** City performance measurement indicators


Source: http://ec.europa.eu/eurostat/ramon/nomenclatures

**Table 2.** Selected cities and the corresponding regions (NUTS classification)

#### **5. Data structure and categorization**

#### **5.1. Database**

The database of the research was largely represented by an Urban Audit indicator set for core cities, available as a part of a broader Eurostat collection. The base of data analysis (accessed February 2012) covered 30 countries: the EU-27, along with Turkey, Switzerland, Norway, and 372 urban units (city and the wider urban area) and specific metropolitan areas. The temporal span, used in the research, included periods 2010–2012, 2007–2009, and 2003–2006, exception‐ ally 1999–2002, but only to illustrate the missing measurement in the time series. In addition to Urban Audit, research also implied regional databases of EUROSTAT (appsso.euro‐ stat.ec.europa.eu), and the index of quality of life in each country was defined by using ranking of International Living survey. Taking into account the selection of cities from different countries in terms of validity and international comparability, and to avoid inaccuracies due to diverse methodological approaches, the research additionally incorporated data from the Statistical Office of the Republic of Slovenia (www.stat.si), Austria (Statistik Austria; www.sta‐ tistik.at), Italy (SISTAN Sistema statistico nazionale; www.sistan.it and www.istat.it), Germa‐ ny (www.destatis.de), Belgium (statbel.fgov.be), and Bulgaria (www.nsi.bg). Urban Audit database, used in 75.47% of cases, was followed by Eurostat database with 22.64% and other data sources (1.89%); overall data coverage rate reached 87%. Limitations of the research referred to the missing data; the inclusion of the secondary databases that would otherwise fill out the data gap could be due to the chosen methodology of data collection and evaluation, which will result in the reduced data comparability of data and furthermore between cities within individual indicators. Dropping variables was potentially admissible in cases of minor influence on the dependent variable (*y*), which, in most cases, proved to be the best choice since it pointed out the problems associated with data collection (listwise/casewise deletion of missing data of the valuation criterion). Options of replacing missing data represented single imputation as the arithmetic mean (overall mean) or multiple imputation methods (e.g., program Amelia II). When using programs of multicriteria decision making (Expert Choice) in research, only indicators without data gaps were evaluated.

#### **6. Criteria weighting**

**Table 1.** City performance measurement indicators

78 Perspectives on Business and Management

Source: http://ec.europa.eu/eurostat/ramon/nomenclatures

**5. Data structure and categorization**

**5.1. Database**

**Table 2.** Selected cities and the corresponding regions (NUTS classification)

**City NUTS0 NUTS1 NUTS2 NUTS3**

Maribor Slovenia SI0: Slovenia SI02: West Slovene region SI012 Podravje region

The database of the research was largely represented by an Urban Audit indicator set for core cities, available as a part of a broader Eurostat collection. The base of data analysis (accessed February 2012) covered 30 countries: the EU-27, along with Turkey, Switzerland, Norway, and 372 urban units (city and the wider urban area) and specific metropolitan areas. The temporal

Pleven Bulgaria BG3: North I SE Bulgaria BG31: Northwest BG314 Pleven Linz Austria AT3: Westösterreich AT31: Oberösterreich AT312 Linz-Wels Erfurt Germany DEG: Thüringen DEG0: Thüringen DEG01 Erfurt Trieste Italy ITH: North-East ITH4: Friuli-Venezia Giulia ITH44 Trieste Brugge Belgium BE2: Vlaams Gewest BE25: Prov. West-Vlaandeeren BE251 Arr.Brugge

#### **6.1. Determining the weights of indicators: different approaches**

Weighting of indicators emphasized the suitability requirements, with the value of the weight indicating the impact of each criterion on the final goal (objective). Weighting methods are different, are very widely used, and are scalable in many cases applied, where 0 equals the insignificant impact of the indicator, range 1–3 represents a significantly less important indicator, range 4–7 represents a little less important indicator, and range 8–10 represents an equally important indicator in terms of the relative importance with the most important criteria [1, 30]. In the case of a clearly defined target group, the determination of relevant weightings is also possible by using the questionnaire survey. Stepwise methods label 5–6 as low impor‐ tance indicators (complementary, supplementary, secondary, incidental, indirect, and no impact), 7–8 as average significant indicators (imperative, mandatory, or required indicator), and 9–10 as high importance indicator (fundamental, essential, decisive, definitive, and guidelines).

The weighting is also possible with the prioritization of functional variables in the form of a matrix (CICAPSO, 2012), consisting of the *power zone* with a low dependence of variable *x* (abscissa axis) and a high impact *y* (ordinate axis); *connection zone*, linked with a high depend‐ ence of *x* and a high influence of *y*; *isolated zone*, with a low dependence of *x* and a low impact of *y*; and *exit zone*, with the high dependence of *x* and low impact of *y*. The weightings in the power zone are the most important, influential, and less dependent; those identified in the connection zone are often associated with conflicts, relevant by influence, but at the same time very dependent. In the isolated zone, the weights with low or no dependence and influence on other, mostly useful at the end of the evaluation, can be found. As last in the exit zone, weights of minor importance and high dependence, with the purpose for understanding the power and connection zone, are located.


Dependence (x)→

Source: [9].

#### Source: CICAPSO SAC-Centro Internacional de Capacitacion y Soporte (2012). Conceptualization of the system of indicators in research was based on the relevance of the individual categories, taking **Table 3.** Matrix of weights.

"smart regulation" indicators.

weight of 0.05 (Winter, 2010).

into account the relative importance of weights on the objective measurement: performance development of selected cities. Considering that the system of indicators represents a baseline tool, the weighting depends on the purpose of the decision maker in terms of defining the specific goal of measuring and monitoring. Comparability of the indicators was previously reached by using available, credible databases (Section 5.1). In the case of the desk research data collection, the z-transformation method, which provides standardization and data aggregation, is suggested. In the concept of the "smart city," establishing a standardized indicator value of each city was followed by determining the weightings in accordance with the coverage degree of indicators (lower weightings indicated that values of indicators were not covering all cities). Indicators were assumed to have equal influence on a Conceptualization of the system of indicators in research was based on the relevance of the individual categories, taking into account the relative importance of weights on the objective measurement: performance development of selected cities. Considering that the system of indicators represents a baseline tool, the weighting depends on the purpose of the decision maker in terms of defining the specific goal of measuring and monitoring. Comparability of the indicators was previously reached by using available, credible databases (Section 5.1).

particular category (currently 70 cities with 74 indicators represent 87% level of coverage). Indicators of "smart economy" include innovative spirit (3 indicators with a weight of 0.17), entrepreneurship (2 indicators with a weight of 0.17), economic image/trademark (1 indicator with a weight of 0.17), productivity (1 indicator with a weight of 0.17), and flexibility of labor market (2 indicators with a weight of 0.17). "Smart mobility" indicators represent the following: local accessibility (3 indicators with a weight of 0.25), (inter-)national accessibility (1 indicator with a weight of 0.25), availability of ICT infrastructure (2 indicators with a weight of 0.25), and sustainable, innovative, and safe transport systems (3 indicators with a weight of 0.25). Among indicators of "smart environment," the attractiveness of natural conditions (2 indicators with a weight of 0.25), pollution (3 indicators with a weight of 0.25), environmental protection (2 indicators with a weight of 0.25), and sustainable resource management (3 indicators with weighting of 0.25) are considered. Indicators of the category "smart people" include the following: level of qualification (4 indicators with a weight of 0.14), affinity to lifelong learning (3 indicators with a weight of 0.14), social and ethnic plurality (2 indicators with a weight of 0.14), flexibility (1 indicator with a weight of 0.14), creativity (1 indicator with a weight of 0.14), cosmopolitanism/open-mindedness (3 indicators with a weight of 0.14), and participation in public life (2 indicators with a weight of 0.14). Indicators of the category "smart life" represent the following: cultural facilities (3 indicators with a weight of 0.14), health conditions (4 indicators with a weight of 0.14), individual safety (3 indicators with a weight of 0.14), housing quality (3 indicators with a weight of 0.14), education facilities (3 indicators with a weight of In the case of the desk research data collection, the *z*-transformation method, which provides standardization and data aggregation, is suggested. In the concept of the "smart city," establishing a standardized indicator value of each city was followed by determining the weightings in accordance with the coverage degree of indicators (lower weightings indicated that values of indicators were not covering all cities). Indicators were assumed to have equal influence on a particular category (currently 70 cities with 74 indicators represent 87% level of coverage). Indicators of "smart economy" include innovative spirit (3 indicators with a weight of 0.17), entrepreneurship (2 indicators with a weight of 0.17), economic image/trademark (1 indicator with a weight of 0.17), productivity (1 indicator with a weight of 0.17), and flexibility of labor market (2 indicators with a weight of 0.17). "Smart mobility" indicators represent the following: local accessibility (3 indicators with a weight of 0.25), (inter-)national accessibility (1 indicator with a weight of 0.25), availability of ICT infrastructure (2 indicators with a weight of 0.25), and sustainable, innovative, and safe transport systems (3 indicators with a weight of 0.25). Among indicators of "smart environment," the attractiveness of natural conditions (2

0.05). Weightings for individual categories of indicators 1–53 are presented in Table 1.

0.14), touristic attractiveness (2 indicators with a weight of 0.14), and social cohesion (2 indicators with a weight of 0.14). Participation in decision-making processes (4 indicators with a weight of 0.33), public and social services (3 indicators with a weight of 0.33), and transparent governance (2 indicators with a weight of 0.33) form the category of

By determining the adequate weighting, the research in this section also considered weighting of indicators, measuring the competitiveness of cities in the context of the knowledge economy, where the greatest importance was given to categories of quality of life (weighting 0.20) and knowledge base (weighting 0.20), followed by the categories of innovation (weighting 0.10) accessibility (weighting 0.10), urban diversity (weighting 0.10), productivity (weighting 0.10), and social connectivity (weighting 0.10). Areas of agglomeration and economic heritage were defined with a

With reference to quoted concepts, the largest weighting importance in research was assigned to the categories of quality of life, environment, lifelong learning, development of information, and communication technology and city brand (weighting 0.20), followed by labor market, productivity, entrepreneurship, innovation, and mobility (weighting 0.15). The importance of social cohesion, governance, and urban diversity was defined with a weight of 0.10; a minimum effect on the performance development measurement was attributed to demographic categories (weighting

In terms of weighting credibility, the study also considered Mercer's classification and evaluation indicators (weights) of quality of life (Quality of Living Report) in European cities (Urban Audit database, benchmarking analysis of 246 European cities). The study of 10 dimensions, namely, (1) quality of life, economic environment, (2) political and social environment, (3) sociocultural environment, (4) health and medicine, (5) schools and education, (6) public services and transport, (7) recreation, (8) consumer goods, (9) housing possibilities, (10) natural environment, and 39 quality of life indicators showed a certain degree of area similarity to the selected indicators' system in the research (demography, labor market, economy, quality of life, society, culture and leisure activities, and R & D). Mercer's weights in specific indicators with a weight of 0.25), pollution (3 indicators with a weight of 0.25), environmental protection (2 indicators with a weight of 0.25), and sustainable resource management (3 indicators with weighting of 0.25) are considered. Indicators of the category "smart people" include the following: level of qualification (4 indicators with a weight of 0.14), affinity to lifelong learning (3 indicators with a weight of 0.14), social and ethnic plurality (2 indicators with a weight of 0.14), flexibility (1 indicator with a weight of 0.14), creativity (1 indicator with a weight of 0.14), cosmopolitanism/open-mindedness (3 indicators with a weight of 0.14), and participation in public life (2 indicators with a weight of 0.14). Indicators of the category "smart life" represent the following: cultural facilities (3 indicators with a weight of 0.14), health conditions (4 indicators with a weight of 0.14), individual safety (3 indicators with a weight of 0.14), housing quality (3 indicators with a weight of 0.14), education facilities (3 indicators with a weight of 0.14), touristic attractiveness (2 indicators with a weight of 0.14), and social cohesion (2 indicators with a weight of 0.14). Participation in decision-making processes (4 indicators with a weight of 0.33), public and social services (3 indicators with a weight of 0.33), and transparent governance (2 indicators with a weight of 0.33) form the category of "smart regulation" indicators.

ence of *x* and a high influence of *y*; *isolated zone*, with a low dependence of *x* and a low impact of *y*; and *exit zone*, with the high dependence of *x* and low impact of *y*. The weightings in the power zone are the most important, influential, and less dependent; those identified in the connection zone are often associated with conflicts, relevant by influence, but at the same time very dependent. In the isolated zone, the weights with low or no dependence and influence on other, mostly useful at the end of the evaluation, can be found. As last in the exit zone, weights of minor importance and high dependence, with the purpose for understanding the

Power zone Connection zone

Source: CICAPSO SAC-Centro Internacional de Capacitacion y Soporte (2012). Conceptualization of the system of indicators in research was based on the relevance of the individual categories, taking into account the relative importance of weights on the objective measurement: performance development of selected cities. Considering that the system of indicators represents a baseline tool, the weighting depends on the purpose of the decision maker in terms of defining the specific goal of measuring and monitoring. Comparability of the indicators was

In the case of the desk research data collection, the z-transformation method, which provides standardization and data aggregation, is suggested. In the concept of the "smart city," establishing a standardized indicator value of each city was followed by determining the weightings in accordance with the coverage degree of indicators (lower weightings indicated that values of indicators were not covering all cities). Indicators were assumed to have equal influence on a particular category (currently 70 cities with 74 indicators represent 87% level of coverage). Indicators of "smart economy" include innovative spirit (3 indicators with a weight of 0.17), entrepreneurship (2 indicators with a weight of 0.17), economic image/trademark (1 indicator with a weight of 0.17), productivity (1 indicator with a weight of 0.17), and flexibility of labor market (2 indicators with a weight of 0.17). "Smart mobility" indicators represent the following: local accessibility (3 indicators with a weight of 0.25), (inter-)national accessibility (1 indicator with a weight of 0.25), availability of ICT infrastructure (2 indicators with a weight of 0.25), and sustainable, innovative, and safe transport systems (3 indicators with a weight of 0.25). Among indicators of "smart environment," the attractiveness of natural conditions (2 indicators with a weight of 0.25), pollution (3 indicators with a weight of 0.25), environmental protection (2 indicators with a weight of 0.25), and sustainable resource management (3 indicators with weighting of 0.25) are considered. Indicators of the category "smart people" include the following: level of qualification (4 indicators with a weight of 0.14), affinity to lifelong learning (3 indicators with a weight of 0.14), social and ethnic plurality (2 indicators with a weight of 0.14), flexibility (1 indicator with a weight of 0.14), creativity (1 indicator with a weight of 0.14), cosmopolitanism/open-mindedness (3 indicators with a weight of 0.14), and participation in public life (2 indicators with a weight of 0.14). Indicators of the category "smart life" represent the following: cultural facilities (3 indicators with a weight of 0.14), health conditions (4 indicators with a weight of 0.14), individual safety (3 indicators with a weight of 0.14), housing quality (3 indicators with a weight of 0.14), education facilities (3 indicators with a weight of 0.14), touristic attractiveness (2 indicators with a weight of 0.14), and social cohesion (2 indicators with a weight of 0.14). Participation in decision-making processes (4 indicators with a weight of 0.33), public and social services (3 indicators with a weight of 0.33), and transparent governance (2 indicators with a weight of 0.33) form the category of

By determining the adequate weighting, the research in this section also considered weighting of indicators, measuring the competitiveness of cities in the context of the knowledge economy, where the greatest importance was given to categories of quality of life (weighting 0.20) and knowledge base (weighting 0.20), followed by the categories of innovation (weighting 0.10) accessibility (weighting 0.10), urban diversity (weighting 0.10), productivity (weighting 0.10), and social connectivity (weighting 0.10). Areas of agglomeration and economic heritage were defined with a

With reference to quoted concepts, the largest weighting importance in research was assigned to the categories of quality of life, environment, lifelong learning, development of information, and communication technology and city brand (weighting 0.20), followed by labor market, productivity, entrepreneurship, innovation, and mobility (weighting 0.15). The importance of social cohesion, governance, and urban diversity was defined with a weight of 0.10; a minimum effect on the performance development measurement was attributed to demographic categories (weighting

In terms of weighting credibility, the study also considered Mercer's classification and evaluation indicators (weights) of quality of life (Quality of Living Report) in European cities (Urban Audit database, benchmarking analysis of 246 European cities). The study of 10 dimensions, namely, (1) quality of life, economic environment, (2) political and social environment, (3) sociocultural environment, (4) health and medicine, (5) schools and education, (6) public services and transport, (7) recreation, (8) consumer goods, (9) housing possibilities, (10) natural environment, and 39 quality of life indicators showed a certain degree of area similarity to the selected indicators' system in the research (demography, labor market, economy, quality of life, society, culture and leisure activities, and R & D). Mercer's weights in specific

0.05). Weightings for individual categories of indicators 1–53 are presented in Table 1.

Dependence (x)→

Isolation zone Exit zone

Conceptualization of the system of indicators in research was based on the relevance of the individual categories, taking into account the relative importance of weights on the objective measurement: performance development of selected cities. Considering that the system of indicators represents a baseline tool, the weighting depends on the purpose of the decision maker in terms of defining the specific goal of measuring and monitoring. Comparability of the indicators was previously reached by using available, credible databases (Section 5.1).

In the case of the desk research data collection, the *z*-transformation method, which provides standardization and data aggregation, is suggested. In the concept of the "smart city," establishing a standardized indicator value of each city was followed by determining the weightings in accordance with the coverage degree of indicators (lower weightings indicated that values of indicators were not covering all cities). Indicators were assumed to have equal influence on a particular category (currently 70 cities with 74 indicators represent 87% level of coverage). Indicators of "smart economy" include innovative spirit (3 indicators with a weight of 0.17), entrepreneurship (2 indicators with a weight of 0.17), economic image/trademark (1 indicator with a weight of 0.17), productivity (1 indicator with a weight of 0.17), and flexibility of labor market (2 indicators with a weight of 0.17). "Smart mobility" indicators represent the following: local accessibility (3 indicators with a weight of 0.25), (inter-)national accessibility (1 indicator with a weight of 0.25), availability of ICT infrastructure (2 indicators with a weight of 0.25), and sustainable, innovative, and safe transport systems (3 indicators with a weight of 0.25). Among indicators of "smart environment," the attractiveness of natural conditions (2

previously reached by using available, credible databases (Section 5.1).

power and connection zone, are located.

80 Perspectives on Business and Management

Impact (y) ↑

Source: [9].

**Table 3.** Matrix of weights.

"smart regulation" indicators.

weight of 0.05 (Winter, 2010).

By determining the adequate weighting, the research in this section also considered weighting of indicators, measuring the competitiveness of cities in the context of the knowledge economy, where the greatest importance was given to categories of quality of life (weighting 0.20) and knowledge base (weighting 0.20), followed by the categories of innovation (weighting 0.10) accessibility (weighting 0.10), urban diversity (weighting 0.10), productivity (weighting 0.10), and social connectivity (weighting 0.10). Areas of agglomeration and economic heritage were defined with a weight of 0.05 [62].

With reference to quoted concepts, the largest weighting importance in research was assigned to the categories of quality of life, environment, lifelong learning, development of information, and communication technology and city brand (weighting 0.20), followed by labor market, productivity, entrepreneurship, innovation, and mobility (weighting 0.15). The importance of social cohesion, governance, and urban diversity was defined with a weight of 0.10; a minimum effect on the performance development measurement was attributed to demographic catego‐ ries (weighting 0.05). Weightings for individual categories of indicators 1–53 are presented in Table 1.

In terms of weighting credibility, the study also considered Mercer's classification and evaluation indicators (weights) of quality of life (Quality of Living Report) in European cities (Urban Audit database, benchmarking analysis of 246 European cities). The study of 10 dimensions, namely, (1) quality of life, economic environment, (2) political and social envi‐ ronment, (3) sociocultural environment, (4) health and medicine, (5) schools and education, (6) public services and transport, (7) recreation, (8) consumer goods, (9) housing possibilities, (10) natural environment, and 39 quality of life indicators showed a certain degree of area similarity to the selected indicators' system in the research (demography, labor market, economy, quality of life, society, culture and leisure activities, and R & D). Mercer's weights in specific areas are defined as follows: political and social environment (weighting 0.283); economic environment (0.048), which includes employment in the services sector (NACE classification J-K); area of health and medicine (0.229), which also includes life expectancy in years; schools and education (ISCED with weight of 0.041); public services and transportation (0.157), including air passengers using nearest airport; recreation (0.109); housing possibilities (0.062); and the natural environment (0.071), including rainfall [33].

### **7. Multiattribute decision models and system of indicators' simulation with computer-based programs**

#### **7.1. Methods for decision support**

After the system of indicators for monitoring performance development of the city had been set, the purpose of the study was to enable quality decision making in a systematic, organized manner. The preparation of scenario and the selection of the chosen strategy involved either verbal or numerical representation of inputs in principle, which required the inclusion of artificial intelligence. Multicriteria models represent a useful tool to support decision making in complex decision situations, where a large number of factors and variants affect the final decision. Supporting software tools in designing a decision model evaluate variants and offer a range of different analyses for detailed decision's verification and justification [6, 7].

Systematic decision-making processes for supporting smart decisions should be based on combining normative theories and cognitive aspects, forming an integral part of decision making in practice. According to [23], problem solving can be done in several ways: intuitively, routinely—by adopting the past used procedures, or random selection—by systematic rational thinking using relevant information. In the latter, the decision maker measures the values of alternatives by each individual criterion or by multiple criteria simultaneously [11].

The general approach of decision analysis originates from axioms of the game theory, by John von Neumann and Oskar Morgenstern. The main steps represent problem structuring, estimating the likelihood of possible outcomes, determining their utility, evaluating alterna‐ tives and selecting strategies. Briefly, the process of multiattribute decision making involves problem identification and its structuring, the model building, and activities of problem solving planning, wherein[5] have foreseen also returning from each following to the previous phase [11].

The major role in decision making according to multiple criteria goes to classification or ranking. Identifying the decision maker's relative importance of each criterion can be ex‐ pressed as a priority (the criterion is more important than the other) or weighting, which declares the relative importance of the various criteria [10].

In the research, comparison of the cities' development performance was carried out using the analytic hierarchical process (AHP) method, developed by Thomas L. Saaty. In accordance with the theory of AHP, multicriteria problems are initially presented in the form of a hierarchical model. Several papers demonstrated the AHP efficiency in different areas [19, 21, 26, 31, 32, 37, 51, 52, 59, 64]. The oldest reference we have found dates from 1972 [41]. After this, a paper in the *Journal of Mathematical Psychology* [42] precisely described the method [26]. The method's basis represent pairwise comparisons of the two criteria at the same level in relation to the element on the next (higher) level. In order to help the decision maker to provide the pairwise comparisons, Saaty created a 9-point intensity scale of importance between pair of elements (Table 4). If the estimation a.., is assigned to criterion *i* in comparison with *j*, then to criterion *j* when compared with *i,* the reciprocal value is assigned [44, 48, 50].

Weighting criteria and priorities to alternatives are not assigned directly by decision makers; they are calculated from the judgments, entered by comparing the importance of criteria and preferences of alternatives in pairs in verbal, graphic, or numerical manner [10].

A top-down approach of AHP method leads from the goal to the alternatives, while the bottomup approach represents expression of judgments about alternatives before expressing judg‐ ments on the criteria [16, 38].


**Table 4.** The fundamental scale for pairwise comparisons.

Evaluating the importance of criteria and preference of alternatives, according to individual criteria, includes a criteria importance estimation by setting the appropriate weights; for AHP, the sum of the weights for each group of criteria is considered equal to 1 (hierarchical manner of determining the weights).

Attributes (criteria at the lowest hierarchical level) are represented as follows [10]:

*z*1, *z*2, …, *zk* , while

classification J-K); area of health and medicine (0.229), which also includes life expectancy in years; schools and education (ISCED with weight of 0.041); public services and transportation (0.157), including air passengers using nearest airport; recreation (0.109); housing possibilities

**7. Multiattribute decision models and system of indicators' simulation**

After the system of indicators for monitoring performance development of the city had been set, the purpose of the study was to enable quality decision making in a systematic, organized manner. The preparation of scenario and the selection of the chosen strategy involved either verbal or numerical representation of inputs in principle, which required the inclusion of artificial intelligence. Multicriteria models represent a useful tool to support decision making in complex decision situations, where a large number of factors and variants affect the final decision. Supporting software tools in designing a decision model evaluate variants and offer a range of different analyses for detailed decision's verification and justification [6, 7].

Systematic decision-making processes for supporting smart decisions should be based on combining normative theories and cognitive aspects, forming an integral part of decision making in practice. According to [23], problem solving can be done in several ways: intuitively, routinely—by adopting the past used procedures, or random selection—by systematic rational thinking using relevant information. In the latter, the decision maker measures the values of

The general approach of decision analysis originates from axioms of the game theory, by John von Neumann and Oskar Morgenstern. The main steps represent problem structuring, estimating the likelihood of possible outcomes, determining their utility, evaluating alterna‐ tives and selecting strategies. Briefly, the process of multiattribute decision making involves problem identification and its structuring, the model building, and activities of problem solving planning, wherein[5] have foreseen also returning from each following to the previous

The major role in decision making according to multiple criteria goes to classification or ranking. Identifying the decision maker's relative importance of each criterion can be ex‐ pressed as a priority (the criterion is more important than the other) or weighting, which

In the research, comparison of the cities' development performance was carried out using the analytic hierarchical process (AHP) method, developed by Thomas L. Saaty. In accordance with the theory of AHP, multicriteria problems are initially presented in the form of a hierarchical model. Several papers demonstrated the AHP efficiency in different areas [19, 21, 26, 31, 32, 37, 51, 52, 59, 64]. The oldest reference we have found dates from 1972 [41]. After this, a paper in the *Journal of Mathematical Psychology* [42] precisely described the method [26].

declares the relative importance of the various criteria [10].

alternatives by each individual criterion or by multiple criteria simultaneously [11].

(0.062); and the natural environment (0.071), including rainfall [33].

**with computer-based programs**

**7.1. Methods for decision support**

82 Perspectives on Business and Management

phase [11].

*w*1, *w*2, …, *wkwk* represent the weights.

It is assumed that:

$$zw\_1 + zw\_2 + \dots \\ zw\_k = \mathbf{1}; \\ zw\_m \ge 0; \\ m = \mathbf{1}, \ \mathbf{2}, \dots, k,\tag{2}$$

*w* = *w*1*w*2...*wk <sup>T</sup>* and

$$a\_{\vec{\eta}} = \frac{w\_{\cdot}}{w\_{\cdot}}; \mathbf{i} = \mathbf{1}, \ \mathbf{2}, \ldots, k; \mathbf{j} = \mathbf{1}, \ \mathbf{2}, \ldots, k, \text{ meaning:} \tag{3}$$

attribute *zi is aij*- times as important as the attribute *zj* .

By calculating the values of alternatives with respect to each attribute is:

*vm(Ai )* value of alternative *Ai* with respect to the attribute *zm* and

*vm(Aj )* value of alternative *Aj* with respect to the attribute *zm*.

Given n objects, e.g., attributes or alternatives, we suppose that the decision maker is able to compare any two of them. In preference modelling, this assumption is called comparability. For any pairs (*i, j*); *i, j* = 1, 2,..., *n*, the decision maker is requested to tell how many times the *i*-th object is preferred (or more important) than the *j*-th one, which result is denoted by *aij*:

$$a\_{\boldsymbol{i}} = \frac{\upsilon\_m(A\_{\boldsymbol{i}})}{\upsilon\_m(A\_{\boldsymbol{i}})}; \boldsymbol{i} = \textbf{1}, \; \textbf{2}, \ldots, n; \boldsymbol{j} = \textbf{1}, \; \textbf{2}, \; \ldots, n. \tag{4}$$

ratios of values of alternatives, indicating that alternative *Ai* is with respect to attribute *zm* a*ij*times as good as alternative A*<sup>j</sup>* [10].

By pairwise comparison, regarding the importance of the criteria, a square matrix *A* whose elements are the ratios of a*ij* criteria weights [10, 15, 22] can be composed as follows:

$$A = \begin{bmatrix} a\_{i\mid} \end{bmatrix}. \tag{5}$$

The characteristics of the matrix are as follows [10]:

$$a\_{\vec{\eta}} > 0, a\_{\vec{\eta}} = \frac{1}{a\_{\vec{\mu}}}, a\_{\vec{\iota}} = \mathbf{1} \text{ and } \tag{6}$$

$$a\_{iu}a\_{m\rangle} = |a\_{i\rangle}, \text{ i. } m, \text{ j.} = 1, \text{ } 2, \dots, k. \tag{7}$$

The consistency of matrix is confirmed in the case of:

$$Aw \,=\, kw.\tag{8}$$

In practice, the consistency is usually incomplete; therefore,

*w* = *w*1*w*2...*wk*

*vm(Ai*

*vm(Aj*

*<sup>T</sup>* and

84 Perspectives on Business and Management

*)* value of alternative *Ai*

*)* value of alternative *Aj*

times as good as alternative A*<sup>j</sup>*

*ij*

*j*

attribute *zi is aij*- times as important as the attribute *zj*

= = ¼= ¼ ; 1, 2, , ; 1, 2, , , meaning: *<sup>i</sup>*

with respect to the attribute *zm* and

Given n objects, e.g., attributes or alternatives, we suppose that the decision maker is able to compare any two of them. In preference modelling, this assumption is called comparability. For any pairs (*i, j*); *i, j* = 1, 2,..., *n*, the decision maker is requested to tell how many times the *i*-th object is preferred (or more important) than the *j*-th one, which result is denoted by *aij*:

with respect to the attribute *zm*.

( ) = = ¼= ¼ ; 1, 2, , ; 1, 2, , , *m i*

ratios of values of alternatives, indicating that alternative *Ai* is with respect to attribute *zm* a*ij*-

By pairwise comparison, regarding the importance of the criteria, a square matrix *A* whose

elements are the ratios of a*ij* criteria weights [10, 15, 22] can be composed as follows:

1 >= = 0, , 1 and *ij ij ii ji*

*aa a*

*<sup>w</sup>* (3)

*<sup>a</sup> i nj n v A* (4)

<sup>=</sup> <sup>é</sup> <sup>ù</sup>. *A a*ë û*ij* (5)

*<sup>a</sup>* (6)

*Aw kw* . = (8)

= =¼ , , , 1, 2, , . *im mj ij aa a imj k* (7)

.

*<sup>w</sup> a i kj k*

By calculating the values of alternatives with respect to each attribute is:

( )

[10].

*m j*

*v A*

*ij*

The characteristics of the matrix are as follows [10]:

The consistency of matrix is confirmed in the case of:

$$A\mathfrak{w}\_{\ \ \ \ \! \! /}A\mathfrak{w}\_{\ \! \! /} = \lambda\mathfrak{z}\mathfrak{w}\_{\ \! \! /} \tag{9}$$

where *λ* represents the eigenvalue and *w* the eigenvector of the matrix *A*, which belongs to the eigenvalue *λ*. Only if *k* = *λ*, the consistency of the decision maker is complete. [5, 10] defined a measure of consistency or consistency index (CI) as follows:

$$\text{CI} = \frac{\lambda\_{\text{max}} - k}{k - 1} \tag{10}$$

where *λ*max represents the principal eigenvalue and *k* the size of matrix.

The calculation of the consistency of the decision maker is defined as follows [10, 50]:

$$CR = \frac{CI}{R} \tag{11}$$

where *CR* is the consistency ratio, and *R* represents the random consistency index.

The consistency index is compared to a value, derived by generating random reciprocal matrices of the same size, to give a consistency ratio (CR), which is meant to have the same interpretation, regardless the size of the matrix. The comparative values from random matrices are as follows for 3 ≤ *k* ≤ 10 [5].


**Table 5.** Comparative values.

A consistency ratio of 0.1 or less is generally considered to be acceptable. Evaluating the importance of the criteria results [15, 22] in:

$$
\begin{bmatrix}
\mathfrak{a}\_{12} & \mathfrak{a}\_{13} & \mathfrak{a}\_{1k} \\
& \mathfrak{a}\_{23} & \mathfrak{a}\_{2k} \\
& & \mathfrak{a}\_{k-1,k}
\end{bmatrix}
\tag{12}
$$

Advantages of the method include (1) unity (a single, comprehensive, and flexible model for unstructured problems), (2) interdependence (of the system elements), (3) complexity (com‐ bining deductive and systemic approaches to problem solving), (4) hierarchical structure, (5) measurement (descriptive expressed properties by corresponding scale), (6) consistency (foresees the consistency of judgments for determining priorities), (7) synthesis, (8) exchange (considers relative priorities and enables selection of the best alternative), (9) judgment and consensus (combining various judgments in the result), and (10) reiteration (allows reconsid‐ eration of the problem definition, correction of judgments, and improved understanding of the problem) [48].

[10] classifies activities of solving the multicriteria decision-making problem as (a) structuring the problem (criteria tree), (b) determining weights of the criteria, (c) calculating aggregated values of alternatives, (d) alternatives ranking, and (e) sensitivity analyses.

In accordance with the method of AHP, by using leading supporting software Expert Choice, research compared previously selected cities (Table 1), with the aim to identify the perform‐ ance of urban development, using the criteria (indicators) and alternatives (variants), arranged in a hierarchical model. Synthesis results replied to the question of the performance develop‐ ment effectiveness of selected national city compared to chosen European cities.

### **8. Problem modeling**

The structuring of a decision making process started by defining the global objective (goal setting)—selecting the most development successful among six preferential cities, followed by entry of criteria, which represent six areas: (1) demography, labor market, and economy; (2) quality of life; (3) society, culture, and leisure activities; (4) research and development; (5) accessibility, urban networks, and international connectivity; and (6) management of sustain‐ able resources. The process continued with defining alternatives (cities: Maribor, Pleven, Linz, Erfurt, Trieste, and Brugge) and structuring the problem-specific criteria and subcriteria entry.

The chosen indicators were derived from the set of 53 indicators (Table 1), where selection was narrowed to 24 indicators (3, 6, 7, 9, 13, 15, 20, 22, 23, 25, 26, 31, 33, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50 and 52) due to the availability and completeness of data (no data gaps) for all criteria and all alternatives, thus providing credible values regarding to the attribute and the global objective. In addition to the presented weighting approaches (Chapter 6), the importance of each criterion in comparison with the importance of other criteria of an area (1 to 6, a total of 24 indicators = criteria) following the concept of classifying indicators (Table 3) was introduced. Weights in the power zone are the most important and influential (indicators: 3, 7, 15, 22, 25, 26, 42, 43, 45, 46), those identified in the connection zone are important regarding the influence, but at the same time significantly dependent from others (indicators: 9, 20, 31, 39, 44, 47, 48), while weights located in the isolated zone, with small influence above others, are the most useful at the end of the estimation (indicators: 6, 13, 23, 33, 38, 50, 52).

Figure 1 demonstrates the process of problem structuring using criteria tree. Weights are based on available data and methods for calculating the factor weights (Saaty). At each node of the

Source: Expert Choice processing of collected data.

**Figure 1.** Structuring the problem (criteria tree).

Advantages of the method include (1) unity (a single, comprehensive, and flexible model for unstructured problems), (2) interdependence (of the system elements), (3) complexity (com‐ bining deductive and systemic approaches to problem solving), (4) hierarchical structure, (5) measurement (descriptive expressed properties by corresponding scale), (6) consistency (foresees the consistency of judgments for determining priorities), (7) synthesis, (8) exchange (considers relative priorities and enables selection of the best alternative), (9) judgment and consensus (combining various judgments in the result), and (10) reiteration (allows reconsid‐ eration of the problem definition, correction of judgments, and improved understanding of

[10] classifies activities of solving the multicriteria decision-making problem as (a) structuring the problem (criteria tree), (b) determining weights of the criteria, (c) calculating aggregated

In accordance with the method of AHP, by using leading supporting software Expert Choice, research compared previously selected cities (Table 1), with the aim to identify the perform‐ ance of urban development, using the criteria (indicators) and alternatives (variants), arranged in a hierarchical model. Synthesis results replied to the question of the performance develop‐

The structuring of a decision making process started by defining the global objective (goal setting)—selecting the most development successful among six preferential cities, followed by entry of criteria, which represent six areas: (1) demography, labor market, and economy; (2) quality of life; (3) society, culture, and leisure activities; (4) research and development; (5) accessibility, urban networks, and international connectivity; and (6) management of sustain‐ able resources. The process continued with defining alternatives (cities: Maribor, Pleven, Linz, Erfurt, Trieste, and Brugge) and structuring the problem-specific criteria and subcriteria entry. The chosen indicators were derived from the set of 53 indicators (Table 1), where selection was narrowed to 24 indicators (3, 6, 7, 9, 13, 15, 20, 22, 23, 25, 26, 31, 33, 38, 39, 42, 43, 44, 45, 46, 47, 48, 50 and 52) due to the availability and completeness of data (no data gaps) for all criteria and all alternatives, thus providing credible values regarding to the attribute and the global objective. In addition to the presented weighting approaches (Chapter 6), the importance of each criterion in comparison with the importance of other criteria of an area (1 to 6, a total of 24 indicators = criteria) following the concept of classifying indicators (Table 3) was introduced. Weights in the power zone are the most important and influential (indicators: 3, 7, 15, 22, 25, 26, 42, 43, 45, 46), those identified in the connection zone are important regarding the influence, but at the same time significantly dependent from others (indicators: 9, 20, 31, 39, 44, 47, 48), while weights located in the isolated zone, with small influence above others, are the most

Figure 1 demonstrates the process of problem structuring using criteria tree. Weights are based on available data and methods for calculating the factor weights (Saaty). At each node of the

values of alternatives, (d) alternatives ranking, and (e) sensitivity analyses.

ment effectiveness of selected national city compared to chosen European cities.

useful at the end of the estimation (indicators: 6, 13, 23, 33, 38, 50, 52).

the problem) [48].

86 Perspectives on Business and Management

**8. Problem modeling**

hierarchy, a matrix will collect the pairwise comparisons of the decision maker for the criteria and subcriteria, e.g., subcriterion of the total working population is three times more important than the proportion of the population employed in the service sector, equally important as the unemployment rate, and 1.5-times more important than average disposable income (Figure 2).

The total working population includes employment not only in the services but also in other sectors (agriculture, hunting, forestry, fishing, mining, manufacturing, construction, etc.); consequently, the importance assigned is greater. Compared with the rate of unemployment, its importance is equal, owing to the fact that the entire working population and unemploy‐ ment rate represent an important factor of social inclusion. Confirming the strength of the importance judgment, theoretical principles define "labour force participation rate," ex‐ pressed as [3]:

Pop = total population LF = labor ("labour force") = U + E

LFpop = total working population p = participation rate = LF / LFpop

(males and females 15–64)

E = number of employees ("employed") e = rate of employment = E / LF

U = number of unemployed persons u = unemployment rate = U / LF.


Source: Expert Choice processing of collected data.

**Figure 2.** Comparison matrix of the first node (graphical scale).

The increase of the unemployment rate can be simultaneously reflected by the increase of employment, e.g., if a larger number of new workers are entering the workforce segment, but only a small fraction actually becomes employed, an increase in the number of unemployed exceeds the growth in employment. The rate of presence in the labor market is therefore a key component of long-term economic growth, almost as important as productivity [3].

One of the AHP's strengths is the possibility to evaluate quantitative as well as qualitative criteria and alternatives on the same preference scale of nine levels, also verbal (Figure 3). The subcriterion - number of students in upper and further education ISCED 3-4 per 1000 resident population, is according to the criterion of society, culture, leisure activities, equally important as subcriterion - number of students in higher education ISCED 5-6 per 1000 resident popu‐ lation. The strength of the given importance judgment is based on the results of the research about skills, needed in Europe by focusing on the 2020 objectives, carried out by European Centre for the Development of Vocational Training in 2008.

Defining future employment opportunities, the research highlighted the importance of qualifications ISCED 3-4 and 5-6 against the others (trend of "replacement demand"). Forecasts include 105 million new jobs by 2020 (2006–2020); among them, 41 million require a high level of qualification (ISCED 5-6). The existing qualification structure must be, in accordance with quoted, necessarily transformed as the foreseen remaining 55 million new jobs expectedly require medium-level education (ISCED 3-4), which traditionally include vocational qualifi‐ cations, while less than 10 million new jobs include lower qualification levels (CEDEFOP, 2008, p. 13).

Pairwise numerical criteria comparisons (Figure 4) showed that criterion employment in hightech industries and knowledge-intensive sectors (NUTS 2) is three times more important than the R & D expenditure in % of GDP (NUTS 2). The strength of the criterion importance judgment was expressed on the basis of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) survey measuring R & D personnel, carried out by Institute for Statistics in 2009. Salaries of researchers in high-tech and knowledge-intensive sectors


Source: Expert Choice processing of collected data.

**Figure 3.** Judgment scale (verbal).

The increase of the unemployment rate can be simultaneously reflected by the increase of employment, e.g., if a larger number of new workers are entering the workforce segment, but only a small fraction actually becomes employed, an increase in the number of unemployed exceeds the growth in employment. The rate of presence in the labor market is therefore a key

One of the AHP's strengths is the possibility to evaluate quantitative as well as qualitative criteria and alternatives on the same preference scale of nine levels, also verbal (Figure 3). The subcriterion - number of students in upper and further education ISCED 3-4 per 1000 resident population, is according to the criterion of society, culture, leisure activities, equally important as subcriterion - number of students in higher education ISCED 5-6 per 1000 resident popu‐ lation. The strength of the given importance judgment is based on the results of the research about skills, needed in Europe by focusing on the 2020 objectives, carried out by European

Defining future employment opportunities, the research highlighted the importance of qualifications ISCED 3-4 and 5-6 against the others (trend of "replacement demand"). Forecasts include 105 million new jobs by 2020 (2006–2020); among them, 41 million require a high level of qualification (ISCED 5-6). The existing qualification structure must be, in accordance with quoted, necessarily transformed as the foreseen remaining 55 million new jobs expectedly require medium-level education (ISCED 3-4), which traditionally include vocational qualifi‐ cations, while less than 10 million new jobs include lower qualification levels (CEDEFOP, 2008,

Pairwise numerical criteria comparisons (Figure 4) showed that criterion employment in hightech industries and knowledge-intensive sectors (NUTS 2) is three times more important than the R & D expenditure in % of GDP (NUTS 2). The strength of the criterion importance judgment was expressed on the basis of the United Nations Educational, Scientific, and Cultural Organization (UNESCO) survey measuring R & D personnel, carried out by Institute for Statistics in 2009. Salaries of researchers in high-tech and knowledge-intensive sectors

component of long-term economic growth, almost as important as productivity [3].

Centre for the Development of Vocational Training in 2008.

Source: Expert Choice processing of collected data.

88 Perspectives on Business and Management

**Figure 2.** Comparison matrix of the first node (graphical scale).

p. 13).

Source: Expert Choice processing of collected data.

**Figure 4.** Judgment scale (numerical).

represent a significant part of expenditures for research and experimental development, taking into account the total R & D personnel by sector and occupation as well as the level of qualification and full-time employment ("FTE method"). The consideration of the head count (HC) methodology, half-, part-, and full-time FTE, consequently led to overestimated expen‐ diture (research and experimental development) in % of GDP, which again reinforced the validity of the criterion importance judgment (UNESCO, 2009). The matrix was shown as perfectly consistent (inconsistency ratio = 0.00).

When calculating the final value of alternatives, in the synthesis, where local priorities change to global, the additive model is used in a research. Expert Choice allows two modes of synthesis: (a) the distributive (the sum of the priorities on each level equals 1), used in the case of the desired alternative selection, better in relation to the other, and (b) an ideal mode, used in the case of obtaining only the best variant, regardless to any other [10]. If the priorities are already known, the distributed mode is the only approach, which retrieves these priorities. Introducing or removing (Troutt, 1988) a copy [4] or a near copy [17] of an alternative results in a rank reversal of the appeared alternatives. The latest was subject to criticism [17, 27, 28, 56] but also legitimization [24, 40, 43, 46, 47, 49, 59]. In accordance to Wang and Luo (2009), the rank reversal is not unique to AHP but to all additive models [29]. In this study, the distributive mode was selected; adding or removing alternatives was reflected in the adjust‐ ment of ratios and rankings.

The final values of alternatives to the objective (main goal) of "the best city performance development" (Figure 5) were as follows: Erfurt (0.191), Linz (0.188), Brugge (0.180), Trieste (0.159), Pleven (0.142), and Maribor (0.134).

Source: Expert Choice processing of collected data.

**Figure 5.** Selection of the best city performance development: the final values of alternatives.

#### **8.1. Interpretation of results**

By analyzing the evaluation results (Table 6) using the criterion of demography, labour market —employment, economy (and its subcriteria), the city of Maribor reached a value of 0.120, reflecting the weakest result in comparison with other cities, with 57.97% realization of "the best city performance development" main goal, as compared to Linz. Trieste reached this objective by 95.17, Brugge by 89.37, and Erfurt by 67.63%. According to the criterion quality of life, Maribor reached a rating of 3 by 59.84% realization of the main objective; its position worsened with a rating of 5 according to the criterion society, culture, and leisure activities (56.83% of main goal accomplishment). Improved classification (rating 4) was achieved in the field of research and development. However, by the criteria of accessibility of urban networks and international connectivity (46.70% as compared with the leading Trieste and the value of 0.212) and management of sustainable resources (51.11%), the weakest goal realization was recorded. Considering all quoted (sub)criteria of areas 1–6, the latter was most successfully reached by the city of Erfurt, followed by Linz, Brugge, Trieste, and Pleven, with last rating belonged again to Maribor (realization of 70.16%).

With the purpose of determining the stability of the resulting solutions [12], respectively, the sensitivity of the result by varying criteria weights (the latter identifies in changing values and the order of the alternatives), the sensitivity analysis in forms of "performance," "dynamic"


\*Rating 3 is shared by Erfurt and Trieste (equal values).

**Table 6.** Comparison of the evaluation results.

already known, the distributed mode is the only approach, which retrieves these priorities. Introducing or removing (Troutt, 1988) a copy [4] or a near copy [17] of an alternative results in a rank reversal of the appeared alternatives. The latest was subject to criticism [17, 27, 28, 56] but also legitimization [24, 40, 43, 46, 47, 49, 59]. In accordance to Wang and Luo (2009), the rank reversal is not unique to AHP but to all additive models [29]. In this study, the distributive mode was selected; adding or removing alternatives was reflected in the adjust‐

The final values of alternatives to the objective (main goal) of "the best city performance development" (Figure 5) were as follows: Erfurt (0.191), Linz (0.188), Brugge (0.180), Trieste

By analyzing the evaluation results (Table 6) using the criterion of demography, labour market —employment, economy (and its subcriteria), the city of Maribor reached a value of 0.120, reflecting the weakest result in comparison with other cities, with 57.97% realization of "the best city performance development" main goal, as compared to Linz. Trieste reached this objective by 95.17, Brugge by 89.37, and Erfurt by 67.63%. According to the criterion quality of life, Maribor reached a rating of 3 by 59.84% realization of the main objective; its position worsened with a rating of 5 according to the criterion society, culture, and leisure activities (56.83% of main goal accomplishment). Improved classification (rating 4) was achieved in the field of research and development. However, by the criteria of accessibility of urban networks and international connectivity (46.70% as compared with the leading Trieste and the value of 0.212) and management of sustainable resources (51.11%), the weakest goal realization was recorded. Considering all quoted (sub)criteria of areas 1–6, the latter was most successfully reached by the city of Erfurt, followed by Linz, Brugge, Trieste, and Pleven, with last rating

With the purpose of determining the stability of the resulting solutions [12], respectively, the sensitivity of the result by varying criteria weights (the latter identifies in changing values and the order of the alternatives), the sensitivity analysis in forms of "performance," "dynamic"

ment of ratios and rankings.

90 Perspectives on Business and Management

(0.159), Pleven (0.142), and Maribor (0.134).

Source: Expert Choice processing of collected data.

belonged again to Maribor (realization of 70.16%).

**8.1. Interpretation of results**

**Figure 5.** Selection of the best city performance development: the final values of alternatives.

"gradient," "two-dimensional (2D) plot," and "head to head" (between two alternatives) was performed.

Performance sensitivity graph in the Figure 6 indicates which alternatives are better or weaker at a particular criterion (Čančer, 2009), e.g., Erfurt is the best according to the criteria of research and development and sustainable resource management. Pleven is the best according to the criterion quality of life and weakest regarding society, culture, leisure activities, and research and development. Maribor is the weakest in terms of the criteria demography, labour market —employment, and economy; the accessibility of urban networks; and the sustainable resource management. Trieste is the best regarding the accessibility of the urban network.

The gradient analysis enabled to identify influence on the final value of alternatives due to individual criteria weightings alterations [12]. Dynamic sensitivity analysis (Figure 7) indicates the weight increase of the criterion of society, culture, and leisure activities from 0.150 to 0.164 or more for the second-ranked alternative to become the best one.

Head-to-head analysis, by comparing two alternatives, clearly demonstrated the superior one by accomplishing individual criterion and global goal (Čančer, 2009). As apparent from the Figure 6, the city of Pleven was more successful according to the criterion of demography, labor market—employment, economy, quality of life criterion and the criterion of accessibility of urban network, while the city of Maribor indicated better performance according to the criteria society, culture, and leisure activities, as well as research and development. Pleven gathered higher final value, namely, for 0.0144.

Analysis of the 2D plot led to identifying the dominated and nondominated alternatives regarding the pair of selected criteria [12]. As shown in Figure 6, according to the criteria of demography, labor market—employment, economy, and quality of life, Linz, Trieste, Brugge, and Pleven represent the nondominated alternatives, while Erfurt and Maribor belong to dominated alternatives.

Source: Expert Choice processing of collected data.

**Figure 6.** Sensitivity analysis.

Source: Expert Choice processing of collected data.

**Figure 7.** Change in weightings.

Analysis of the 2D plot led to identifying the dominated and nondominated alternatives regarding the pair of selected criteria [12]. As shown in Figure 6, according to the criteria of demography, labor market—employment, economy, and quality of life, Linz, Trieste, Brugge, and Pleven represent the nondominated alternatives, while Erfurt and Maribor belong to

dominated alternatives.

92 Perspectives on Business and Management

Source: Expert Choice processing of collected data.

**Figure 6.** Sensitivity analysis.

#### **9. Conclusion and future development**

The research aimed at testing the development efficiency of such a methodology for measuring performance success of urban development, which would be useful within the national as well as international (European) comparable city sample. For the testing purposes, the selection of cities followed certain criteria. The determination of adequate measurement indicators, closely associated with evaluation of known methodological concepts (Smart City, city performance, and urban status and sustainability) and relevant databases, resulted in obtained useful tool: a system of 53 selected indicators by field measurement, meaningfully divided into six areas and added categories to enable ranking of comparable medium-sized European cities.

Using AHP and its supporting Expert Choice program tool for quantitative data analysis, which included narrowed set of 24 indicators (no data gaps), the research sought out for the confirmation of selection decision possibility in quoted city sample. AHP evaluation of alternatives provided clarity in multiattribute decision-making process, resulting in ranking in accordance with a defined hierarchy and relative importance of decision criteria (criteria tree and weightings). Achieving the best possible decision due to complex problem structure therefore demands a trade-off between prefect modeling and usability of the model.

Meanwhile, advantages of the hierarchical problem modeling included the possibility to adopt verbal judgments and the verification of the consistency, Expert Choice incorporated intuitive graphical user interfaces, automatic calculation of priorities and inconsistencies, as well as several ways to process a sensitivity analysis. It has to be pointed out that, beside the traditional application of AHP and supporting software, a new trend in use, namely combining others methods, e.g., neural networks, SWOT analysis, and others, is emerging, as AHP is still part of certain theoretical discussions, resulting from the limitation due to assumed criteria independence and hierarchy problems due to appropriate judgment scale.

#### **Author details**

Jasmina Mavrič1\* and Vito Bobek2

\*Address all correspondence to: jasmina.mavric@krs.net

1 Economic institute Maribor, Maribor, Slovenia

2 University of Applied Sciences, FH Joanneum, Graz, Austria

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