An integrated decision support model for a knowledge city ...pdfs.semanticscholar.org/5a80/22f9c21c4c3bb88533644009f2e31bc23f4f.pdfKeywords Decision making, Emergent strategy, Knowledge

An integrated decision support model for aknowledge city’s strategy formulation

Kostas Ergazakis, Kostas Metaxiotis, John Psarras and Dimitrios Askounis

Abstract

Purpose – The concept of knowledge cities (KCs) offers advantages to any urban region. Many cities

globally claim themselves as being already KCs, while other cities have elaborated strategic plans in

order to integrate this concept into their operational structures. The examination of their approaches

reveals however that these initiatives are fragmented. The purpose of this paper is to present a

multi-dimensional and integrated decision support model for a KC’s strategy formulation.

Design/methodology/approach – Reference is made to a methodological approach (KnowCis) for the

integrated development of a KC, consisting of five main phases and taking into account nine different

dimensions. The strategy formulation phase is a particularly complex procedure for any authority (e.g.

local government or city’s development agency). The reasons for this complexity are related to the

amplitude of the KC concept, to the variety of the factors to be considered as well as to the challenge for

balancing the needs and interests of different target groups.

Findings – The proposed model consists of the following building blocks: identification of the

appropriate actions (based on the KnowCis methodology), modeling of the city’s current status as a KC

(via the development of related indicators), assessment of actions’ necessity (based on the indicators’

outcomes and through the benchmarking of other successful KCs cases), selection of the most

appropriate form for each proposed action (based on their efficiency during the last reference period)

and, finally, prioritisation of the proposed actions (based on a multi-criteria approach).

Research limitations/implications – The main suggestion for future research is the development of an

intelligent information system which will incorporate the building blocks of the proposed model.

Originality/value – The originality and value of the paper is that the proposed model can be a really

helpful decision support tool for any city which is developing a knowledge-based strategy.

Keywords Decision making, Emergent strategy, Knowledge management, Cities, Citizens

Paper type Research paper

Introduction

According to Carrillo (2006a): ‘‘few aspects of today’s world may characterize better the

dawn of the new millennium than the transformation of regions into knowledge societies’’.

Carrillo also underlines the fact that major international organisations (EU, World Bank, UN)

have all stressed the critical importance of the knowledge economy as a global reality

established over the turn of the century.

In this context, the concept knowledge cities (KCs) came recently to the front. It is a subfield

of knowledge-based development (KBD) and ‘‘it constitutes one of the most complex

phenomena ever faced by mankind and probably a critical one for its future evolution’’

(Carrillo, 2006a). There are various definitions of what a KC is and of its main characteristics

and advantages (Ergazakis et al., 2004, 2006a, b; Carrillo, 2004, 2006b; Chatzkel, 2006).

Many cities have undertaken considerable efforts and initiatives so as to be developed or to

enhance their status as a KC. However, their approaches were rather ad hoc, spontaneous

and not based on a pre-defined method (see Ergazakis et al., 2004, 2006a, c). The field still

DOI 10.1108/13673270710819816 VOL. 11 NO. 5 2007, pp. 65-86, Q Emerald Group Publishing Limited, ISSN 1367-3270 j JOURNAL OF KNOWLEDGE MANAGEMENT j PAGE 65

Kostas Ergazakis, School of

Electrical and Computer

Engineering, National

Technical University of

Athens (NTUA), Greece.

Kostas Metaxiotis, Greek

Ministry of Economy and

Finance, Athens, Greece.

John Psarras, School of

Electrical and Computer




Dimitris Askounis, School of

Electrical and Comuter




lacks a consensus regarding appropriate conceptual and methodological frameworks

(Carrillo, 2006b). This is the reason why the related research has begun to concentrate on

the direction to substantiate the fundamental principles of KCs and to define unified methods

for their design, development and operation.

Under this prism, the authors have recently introduced a method, called ‘‘KnowCis’’

(Ergazakis et al., 2006c). The method consists of five main phases and takes into account

nine different dimensions, so as to reflect the variety of social, economic and cultural life in a

city (Ergazakis et al., 2004). This paper focuses on the second phase of KnowCis, i.e. the

strategy formulation, which is a particularly complex procedure. The reasons for this

complexity are related to the amplitude of the KC concept and to the factors that should be

considered when a development strategy for any city is being formulated.

The main aim of this paper is to present a multi-dimensional and integrated decision-making

model so as to assist the authorities charged with the duty to develop a KC’s strategy. Next

section briefly presents the KnowCis method and the need for a decision support model. The

following section analyzes the model’s main building blocks, while the other section provides

information on its pilot application to a Greek municipality. The final section is devoted to

presenting some main conclusions and future research challenges.

The KnowCis method and the need for a decision support model

The KnowCis (Knowledge Cities) method was developed by the authors, in 2005 (Figure 1).

It is the outcome of a multi-annual research on the fields of knowledge-management (KM),

knowledge-based development (KBD) and knowledge cities. The first priority of the method

is the setting-up of a committee (knowledge city committee – KCC) which is co-responsible,

along with the city’s local government, for the consultation and co-ordination of the whole

effort, from its very beginning. Government representatives, and representatives of citizens,

enterprises and cultural organizations can equally participate in the KCC.

The method consists of five main phases:

1. Phase 1: Diagnosis. Before any attempt to outline a strategy, the KCC proceeds to a

thorough diagnosis of the current city’s status as a KC, based on studies, opinion polls

and qualitative evaluations.

2. Phase 2: Formulation of strategy. The diagnosis of the previous phase is important for the

formulation of strategy that will be adopted. This strategy considers nine dimensions

comprising sets of particular actions. The specific characteristics, the particularities,

strengths and weaknesses of the city determine which actions and interventions are

needed as well as the priority of each one. Their implementation contributes to the

attainment of various objectives which are substantial for the success of a KBD effort

(Ergazakis et al., 2006b).

3. Phase 3: Creation of detailed action plan. This phase is devoted to the creation of a

detailed action plan for realising the defined strategy. The action plan comprises specific

projects to be implemented (project-oriented approach) as well as interventions to

specific processes that need improvement (process oriented approach). Each project or

intervention is thoroughly selected, designed and prepared. They incorporate, by design,

a component related to continuous sharing of knowledge. Obviously, the appropriate

financing must have been reassured.

‘‘ Many cities have undertaken considerable efforts andinitiatives so as to be developed or to enhance their status asa knowledge city. ’’

PAGE 66 j JOURNAL OF KNOWLEDGE MANAGEMENTj VOL. 11 NO. 5 2007

Figure 1 The KnowCis methodology

VOL. 11 NO. 5 2007 j JOURNAL OF KNOWLEDGE MANAGEMENTj PAGE 67

4. Phase 4: Implementation. The KCC, the agencies and stakeholders participating in it, the

local government, and other public or private organisations and companies are

implementing the defined actions, measures and projects. In this way, each stakeholder

remains committed and contributes to the effort.

5. Phase 5: Measurement/evaluation. It is indispensable to measure the progress of the

whole effort and evaluate the performance of the city as a KC, based on indicators and

the consultation of evaluation experts.

Horizontal aspects are also considered in the method, i.e. the open and equal participation

of all citizens and stakeholders, the political and societal leadership/commitment and the

KM procedures related to the effort.

For further details on the method see Ergazakis et al., 2006c.

It should be noted at this point that, in general, the following factors are important and affect

the process of formulating development strategies for any city:

B Availability of resources that can be allocated for the implementation of the strategy.

B Main results achieved in the past and the efficiency of particular actions and measures.

B Existence of time limits which may affect the strategy formulation process.

B Expectations of various actors in the city, whose interests and needs may differ.

B Particular characteristics of the city or special circumstances.

B Threats and opportunities deriving from the international environment.

B General socio-economical context of the country.

Figure 2 presents these factors.

Moreover, as it is proposed by many researchers in the literature (Carrillo, 2002, 2004,

2006a, b; Chatzkel, 2006; Dvir and Pasher, 2004; Ergazakis et al., 2004, 2006a, b, c, d, e;

Garcia, 2004; Gonzalez et al., 2004; Malone and Yohe, 2002; Martinez, 2006; Montreal

Knowledge City Advisory Committee, 2003; Palacios and Galvan, 2006; Ploeger, 2001; Raza

et al., 2006; Rodriguez and Viedma, 2006), a successful KC should take under consideration

Figure 2 Main factors influencing the strategy formulation process for any city


many different aspects of social, economic and cultural life in the city, in order to achieve

desired strategic objectives in the context of the knowledge economy. Thus, phase 2 of

KnowCis (formulation of strategy) is the most complex and critical for the success of the

whole effort and it incorporates nine different dimensions and 25 actions.

In this way, it is understood that any authority (local government, city’s development

agencies, public institutions etc.) which is charged with the duty to select the appropriate

strategic interventions for a KC, should consider a wide variety of factors, as referred to the

last two paragraphs. Consequently the task of selecting and prioritising the needed

interventions and actions is becoming a multi-parameter and complex procedure. Thus, a

decision support model which could assist the decision makers in the above procedure

would be really useful. The main objective of this paper is to present such an approach

which, in combination with KnowCis, constitutes an integrated decision support model for a

KC’s strategy formulation.

The decision support model

The proposed model (depicted in Figure 3) consists of five main building blocks.

Building block 1. Identification

This building block concerns the identification, based on the international experience

regarding KCs and other KBD strategies and approaches, of 25 actions (Aij), belonging to

the nine dimensions (Di) (i ¼1,2 . . . 9). The actions are illustrated in Table I.

At this point, the ‘‘history’’ of identification of these dimensions, actions, and of the author’s

prior research in the fields of KM, KBD and KCs should be shortly referred: The first basic

step was the substantiation of a KC model, the foundation of KCs’ basic characteristics,

benefits and key success factors as well as the exploration of the concept’s relation with KM

and KBD strategies. For this purpose, a thorough review of published reports, papers, books

and web-sites has taken place (Ergazakis et al., 2004), including a review of contemporary

knowledge-based development strategies.

This review resulted in a set of five main challenges that these strategies should address,

namely: increase of knowledge intensity in the region; democratization of KM processes and

increased citizen’s participation; reinforcement of the business environment; replacement of

‘‘digital divide’’ with ‘‘digital inclustion’’; sustainable urban development. It has been

concluded that the concept of KCs, being a sub-field of KBD, is particularly appropriate and

advantageous because it has the necessary potential so as to comply with and satisfy these

challenges (Ergazakis et al., 2006b).

The next step was the thorough examination of city’s cases that successfully embraced KBD

strategies so as to be developed as KCs. The main conclusion was that their initiatives were

not unified and their decision-making processes regarding the strategy formulation were

deficient and incomplete. Nevertheless, even though the approaches had conceptual

differences, common characteristics could be found. The authors drew a pattern of

recurrence of these significant features and their key findings were expressed as

hypotheses for designing, developing and operating successful KCs. The majority of these

hypotheses were fully supported by the examined case studies. Consequently, they have

been incorporated, at a satisfactory level of trust, to a framework for successful KCs

(Ergazakis et al., 2006d).

This framework, the experience accumulated from the thorough study of ten (10) KCs case

studies, as well as the review of KBD strategies and approaches, has been the scientific

basis for defining the dimensions and actions which were incorporated to the KnowCis

method, of the related indicators and their thresholds (Figure 4). During the strategy

formulation process, a KC has to choose on which of the available actions the strategy will be

focused, under which form (e.g. continuation of specific projects and improvement of

processes) and in which order.


Figure 3 The proposed integrated model for a KC’s strategy formulation


Building block 2. Modelling

This building block concerns the modelling of the available actions and interventions that a

KC can select so as to formulate its strategy, via the development of appropriate decision

indicators. The identification of the indicators, as well as of their thresholds, was based to the

review of KBD strategies and of other KCs case studies. Moreover, an exhaustive review of

literature regarding intellectual capital (IC) measurement methodologies and techniques

has been accomplished (e.g. Aubert, 2005; Bontis, 2004; Bounfour, 2005; Cinca et al., 2003;

Edvinsson and Malone, 1997; Edvinsson and Stenfelt, 1999; Florida, 2002; Furman et al.,

2002; MERITUM, 2002; Oliver and Porta, 2006; Porter, 1998; Poyhonen and Smedlund,

2004; Roos, 1996; Sveiby, 1997, 2000, 2001; Viedma, 1999, 2002, 2003; World Bank, 1999,

2001; Wu and Hus, 2005). The decision indicators are categorized as follows:

Table I The dimensions and actions

D1: KM processes with the cityA1.1 Introduction of KM practices in local administration’s processesA1.2 Creation of formal and informal networks for knowledge sharingA1.3 Provision of incentives for knowledge sharingA1.4 Reinforcement of public libraries’ network

D2: City’s ICT infrastructure and citizens’ ICT literacy levelA2.1 Creation of high quality telecommunication networkA2.2 Assurance of low-cost access to broadband connectivityA2.3 Improvement of citizens’ ICT literacy levelA2.4 Creation/improvement of metropolitan web site

D3: Knowledge society citizens’ rightsA3.1 Definition and reassurance of knowledge society rights (accessibility, information,

education and training, participation) for all citizensA3.2 Creation of necessary conditions so as the citizens make use of these rights

D4: Research, business innovation and entrepreneurshipA4.1 Improvement of city’s performance in knowledge-intensive sectorsA4.2 Provision of incentives to companies for innovationA4.3 Support of entrepreneurship and promotion of new ideasA4.4 Support of research/efficient promotion of research results throughout the city’s knowledge

agents

D5: Challenge that KM poses to the public sectorA5.1 Continuous review of the international environment, for the latest developments concerning

the services that modern cities offerA5.2 Adaptation and integration of best practices to the policies, processes and services offered

by the city

D6: Networking and synergies among all city’s actors/with other KCsA6.1 Reinforcement of networking and interactions among all actors in the cityA6.2 Establishment of links and partnership with other KCs

D7: Availability and skill level of human capitalA7.1 Attraction and retention of high-level human capitalA7.2 Improvement of existing human capital’s skill level

D8: Inclusive, international and multi-ethnic character of the cityA8.1 Improvement of immigrants’ and minorities’ living conditionsA8.2 Enhancement and reinforcement of the cultural, recreational and sporting activities taking

place in the city and therefore of the city’s visitorsA8.3 Reinforcement of all the social groups’ participation in the public affairs

D9: KC concept’s publicity and visibilityA9.1 Creation of a special organisation responsible for the improvement of KC concept’s visibility

throughout the cityA9.2 Continuous promotion/publicity of achieved/envisaged results


B 25 main indicators, one for each Action:

Mij, i ¼ 1,2 . . . 9 and j ¼ 1, . . . x (1)

where:

Mij: The main indicator describing the corresponding action Aij;

i: The number of dimension

j: The number of the action and its corresponding main indicator in the examined

dimension

x: The quantity of main indicators in the examined dimension

These indicators represent the most essential information and knowledge regarding the

diagnosis of the current status of the city as a KC, based on the defined dimensions. The

main indicator is the key means of decision making regarding the necessity of each

action.

B 97 secondary indicators:

Sijk, i ¼ 1,. . .9, j ¼ 1,. . .x, k ¼ 1,. . .y (2)

where:

Sijk: The secondary indicator describing the corresponding action

Aij.


j: The number of the action in the examined dimension

k: The number of the secondary indicator used for the examination of the action Aij

x: The quantity of main indicators in the examined dimension

y: The quantity of secondary indicators used for the examination of the action Aij

These indicators represent additional and further information and knowledge regarding

the status of each dimension, and are also examined during the diagnosis. They are also

used by the model for estimating the necessity of each action.

It should be noted that these indicators are either measurable (M, with specific measurable

unit) or qualitative (Q). In the latter case, their values are defined through the following

scale: 5: Very good performance; 4: Good performance; 3: Slightly good performance; 2:

Inadequate performance. 1: Bad performance.

Figure 4 The identification of dimensions, actions and indicators


The main and secondary decision indicators for each dimension, as well as their type (Q or

M), are presented in Tables II and III.

Building block 3. Assessment

This building block concerns the assessment of the necessity for each action Aij. This is done

through the value control of the decision indicators, as identified on the previous building

block. For this purpose, the input from the phase 1 of the KnowCis is needed: the KCC (or

any other entity responsible for the formulation of strategy), conducts a series of studies,

opinion polls, qualitative assessments and debates, so as to measure and estimate the

values of each one of the decision indicators. As it has been stressed (Ergazakis et al.,

2006c), it is absolutely necessary that in this process equally participate representatives of

Table II The main indicators

D1: KM processes with the cityM1.1 Degree of KM practices’ incorporation in local administration processes QM1.2 Degree of networking among companies, organisations and other important

knowledge agents in the city (e.g. universities, research centres) QM1.3 Number of measures and actions for provision of incentives for knowledge

sharing MM1.4 Number of networked public libraries operating in the city M

D2: City’s ICT infrastructure and citizens’ ICT literacy levelM2.1 Status of city’s telecommunication’ network (connectivity, availability of

connections of ADSL, Wi-Fi, WiMAX, etc.) QM2.2 Mean cost of access to broadband connectivity (eMbps) MM2.3 Citizens’ ICT literacy level QM2.4 Status of present metropolitan web site Q

D3: Knowledge society citizens’ rightsM3.1 Number of initiatives for the reassurance of citizens’ rights in a knowledge society MM3.2 Number of actions and measures encouraging citizens to make use of their rights

in the knowledge society M

D4: Research business innovation and entrepreneurshipM4.1 Share of knowledge-intensive sectors in the city’s annual turnover (%) MM4.2 Number of programmes, actions and measures supporting innovation MM4.3 Number of programmes, actions and measures supporting entrepreneurship MM4.4 Rate of city’s budget allocated to research activities (%) M

D5: Challenges that KM poses to the public sectorM5.1 Rate of city’s allocated budget for the review of the international environment (%) MM5.2 Degree of best practices’ integration to the policies followed and the services

offered by the city Q

D6: Networking and synergies among all city’s actors/with other KCsM6.1 Degree of networking and interactions among all actors in the city QM6.2 Status of links and partnerships with other KCs Q

D7: Availability and skill level of human capitalM7.1 Number of initiatives designed so as to attract high-level human capital MM7.2 Rate of highly educated (at least university level) human capital (%) M

D8: Inclusive, international and multi-ethnic character of the cityM8.1 Degree of immigrants’ and minorities’ satisfaction from their life in the city QM8.2 Number of cultural, recreational and sporting activities taking place in the city MM8.3 Degree of citizens’ participation and interest for the public life and affairs in their

city Q

D9: KC concept’s publicity and visibilityM9.1 Degree of KC concept’s visibility to the public QM9.2 Effectiveness of publicity campaign/promotional activities Q


Table III The secondary indicators

D1: KM processes within the cityM1.1 S1.1.1: Percentage of local administration’s processes designed or created so as to incorporate KM practices (%) M

S1.1.2: Degree of familiarisation, acceptance and use of KM practices on the part of employees in municipalorganisations/companies Q

M1.2 S1.2.1: Degree and amplitude of knowledge sharing in the city QS1.2.2: Estimation of the number of communities of practice in the city MS1.2.3: Quality and thoroughness of available knowledge and information in the metropolitan web site QS1.2.4: Regular dispatching of city’s e-newsletter to interested citizens QS1.2.5: Availability of all data, information and knowledge concerning city’s government and administration, through themetropolitan web site QS1.2.6: Number of other cities’ web pages and portals providing access to important information and knowledgeconcerning the city M

M1.3 S1.3.1: Rate of total city’s budget allocated to the provision of incentives for knowledge sharing (%) MS1.3.2: Degree of acceptance and use of the above-mentioned incentives on the part of important city’s knowledgeagents (universities, research centres, companies) Q

M1.4 S1.4.1: Available book titles per person (book titles/1,000 persons) MS1.4.2: Quality of e-services provided by public libraries QS1.4.3: Rate of libraries’ members using the above-mentioned e-services (%) M

D2: City’s ICT infrastructure and citizens’ ICT literacy levelM2.1 S2.1.1: Number of companies providing telecommunication services M

S2.1.2: Number of companies owing private telecommunication networks MS2.1.3: Provision of modern telecommunication services (e.g. triple play) Q

M2.2 S2.2.1: Rate of broadband connections in relation to the total internet connections (%) MS2.2.2: Rate of citizens using the internet on a daily basis (%) MS2.2.3: Rate of schools with internet access (%) MS2.2.4: Rate of companies using the internet on a daily basis in their professional activites (%) M

M2.3 S2.3.1: Rate of households owning a PC (%) MS2.3.2: Number of PCs per school student (PCs/school student) MS2.3.3: Status of education and training activities in relation to ICTs QS2.3.4: Degree of use of available e-services by citizens and companies (through the metropolitan web site) QS2.3.5: Availability and degree of use of e-learning services Q

M2.4 S2.4.1: Amplitude and variety of available knowledge and information on the metropolitan web site QS2.4.2: Number of available e-services through the metropolitan web site MS2.4.3: Quality and degree of integration of available e-services QS2.4.4: Integration degree of various information systems used in the city’s administration QS2.4.5: Regular dispatching of city’s e-newsletter to interested citizens Q

D3: Knowledge society citizens’ rightsM3.1 S3.1.1: Transparency degree of local administration Q

S3.1.2: Existence of laws/regulations related to intellectual property issues and degree of their application/support on thepart of local administration QS3.1.3: Number of processes/structures allowing citizens to take part in decision-making processes for their city M

M3.2 S3.2.1: Degree of use of available e-services on the part of citizens and companies (through the metropolitan web site) QS3.2.2: Rate of citizens taking part (directly or indirectly) in the Knowledge City Committee and the implementation ofstrategy (%) MS3.2.3: Existence of structure for the acceptance of complaints/remarks from citizens and degree of its use QS3.2.4: Freedom of press and mass media Q

D4: Research, business innovation and entrepreneurshipM4.1 S4.1.1: Rate of companies belonging to knowledge intensive sectors (%) M

S4.1.2: Rate of labour force employed in knowledge intensive sectors (%) MM4.2 S4.2.1: Number of patents per person (patents/1 million citizens) M

S4.2.2: Number of actions for provision of incentives to innovative companies to be settled in the city MS4.2.3: Investments to knowledge and high-tech sectors, deriving from venture capitals (me) MS4.2.4: Rate of ICT sector’s turnover to the total GDP of the city (%) M

M4.3 S4.3.1: Number of new companies’ start-ups per citizen (companies’ start up/1,000 citizens) MS4.3.2: Rate of companies belonging to knowledge intensive sectors (%) MS4.3.3: Mean unemployment rate per sex, age and education level (%) MS4.3.4: Degree of transformation of new/innovative ideas to business plans and new products/services QS4.3.5: Rate of city’s inhabitants employed in the city’s companies (%) M

M4.3 S4.3.6: Provision of information and knowledge through the metropolitan web site, for business opportunities, access tocapital and loans, etc. QS4.3.7: Degree of ‘‘bureaucracy’’ for the start-up of a new company Q

(Continued)


Table III

M4.4 S4.4.1: Number of research centres/institutes operating in the city MS4.4.2: Quality of research results QS4.4.3: Degree of diffusion/sharing of research results to the city’s companies QS4.4.4: Rate of city’s companies with a R&D department (%) MS4.4.5: Number of scientific publications (publications/1 million citizens) M

D5: Challenges that KM poses to the public sectorM5.1 S5.1.1: Rate of local administrations’ employees charged with the review of the international environment (%) M

S5.1.2: Capability of local administration to ‘‘preserve’’ and ‘‘maintain’’ its experienced staff QS5.2.1: Number of improvements to the services offered by the city, due to knowledge acquired by the review of theinternational environment MS5.2.2: Mean cost of adaptation/creation of new services (e/service) MS5.2.3: Degree of citizens’ satisfaction with the services offered by the city Q

D6: Networking and synergies among all city’s actors/with other KCsM6.1 S6.1.1: Number of formal networks of co-operation and joint work M

S6.1.2: Number of workshops, seminars and special events related to the opportunities offered by the city, collaborationand co-operation possibilities etc. M

M6.2 S6.2.1: Number of collaborations and initiatives commonly organised with other cities MS6.2.2: Hi-tech and knowledge intensive exports as a rate of the total city’s exports (%) MS6.2.3: Rate of key capital cities accessible by direct flights (%) MS6.2.4: Rate of city’s students studying in universities of other cities (%) M

D7: Availability and skill level of human capitalM7.1 S7.1.1: Rate of immigrants with university level education (%) M

S7.1.2: Amplitude and quality of information and knowledge in relation to history, culture, arts, etc. offered through themetropolitan web site QS7.1.3: Number of foreign languages into which the metropolitan web site translated MS7.1.4: Rate of citizens speaking at least one foreign language (%) M

M7.2 S7.2.1: Average household spending on education as a percentage of household income (%) MS7.2.2: Number of actions/measures for the improvement/adaptation of educational and training services offered by thecity MS7.2.3: Rate of citizens participating in life-long learning activities (%) M

D8: Inclusive, international and multi-ethnic character of the cityM8.1 S8.1.1: Number of measures and actions addressed to immigrants and citizens with different cultural background M

S8.2.1: Rate of immigrants’ and minorities’ participation in cultural, recreational etc. activities (%) MM8.2 S8.2.1: Effective mareketing/promotion of cultural, recreational, etc. activities M

S8.2.2: Rate of citizens’ participation to cultural, recreational, etc. activites (%) MS8.2.3: Mean cost of participation per activity (e/activity) MS8.2.4: Average household spending on recreation and leisure activities as a percentage of household income (%) MS8.2.5: Rate of city’s visitors per year who intend to take part in cultural, etc. activities (%) MS8.2.6: Rate of labour force occupied in creative sectors (arts, design, architecture, etc.) (%) MS8.2.7: Amplitude and quality of information and knowledge in relation to history, culture, arts etc., offered through themetropolitan web site Q

M8.3 S8.3.1: Number of processes/structures allowing citizens to take part in decision-making processes for their city MS8.3.2: Rate of citizens participating in the above-mentioned structures/processes (%) MS8.3.3: Degree of use of available services and public spaces offered by the city QS8.3.4: Citizens’ degree of satisfaction with the local administration of their city QS8.3.5: Availability of all data, information and knowledge concerning city’s government and administration, through themetropolitan web site Q

D9: KC concept’s publicity and visibilityM9.1 S9.1.1: Rate of citizens being aware of their city’s initiative M

S9.1.2: Rate of companies and organisations being aware of their city’sn initiative MS9.1.3: Number of workshops/conferences organised for the promotion of the initiative MS9.1.4: Number of internet discussion forums related to the initiative MS9.1.5: Rate of citizens taking part (directly or indirectly) in the Knowledge City Committee and the implementation ofstrategy (%) M

M9.2 S9.2.1: Number of activities and special events organised for the promotion of achieved/envisaged results MS9.2.2: Rate of citizens who participated in the promotional events (%) MS9.2.3: Number of informative brochures distributed to citizens and companies MS9.2.4: Number of TV broadcasts/articles in local mass media, related to the initiative MS9.2.5: Number of TV spots/commercials in mass media M


all possible actors and stakeholders in the city (from the part of citizens, companies,

education sector, culture, etc.). Moreover, the use of web questionnaires, electronic

discussion forums and e-voting services can significantly help.

The process in this building block, as it is depicted in Figure 3, is based on the control of

indicator’s values:

B Value control of main indicators Mij. The process starts with the value control of each

indicator Mij, in relation to the threshold value, deriving from the ‘‘experience’’ (i.e. other

KCs case studies, benchmarking, etc.). In case of significant deviation between the

desired and actual indicator’s value, the model suggests the need for the corresponding

action Aij. In smaller deviations, the model continues with the control of secondary

indicators.

B Value control of secondary indicators Sijk. The value of each secondary indicator which is

affiliated to the corresponding action Aij, is compared to the threshold value. Even a small

deviation results to the necessity for the action. On the contrary, the convergence of real

values with the thresholds values, has as output the definitive non-choice of the action,

given that all its affiliated indicators (main and secondary) are within their desired values.

Building block 4. Actions’ form

After the assessment of each action’s necessity, the model focuses on the historic evolution

of the main and secondary indicators as well as on the projects/processes related to the

actions during the last year (action plan, phases 3 and 5 of KnowCis). The choices that the

model can propose for the form of each action are:

1. Continuation/preservation of existing projects/processes.

2. Design and implementation of new projects/processes.

3. Modification/re-engineering of existing projects/processes.

The process of selection of the most appropriate form is the following:

B Concerning the measurable indicators, the Evolution indicators EMij and ESijk are used.

They are based on the evolution of the main and secondary indicators:

EMij ¼Myear_n

ij

Myear_n21ij

2 1 ð3Þ

and

ESijk ¼Syear_n

ijk

Syear_n21ijk

2 1 ð4Þ

where:

EMij and ESijk: the evolution indicators


j: the number of the action and its corresponding main indicator in the examined

dimension

k: the number of the secondary indicator used for the examination of the action Aij

Myear_nij : the value of the main indicator for the present year (n)

Myear_n21ij : the value of the main indicator for the previous year (n21)

Syear_nijk : the value of the secondary indicator for the present year (n)

Syear_n21ijk : the value of the secondary indicator for the previous year (n 2 1).

Based on the value control of the evolution indicator in relation to its threshold value (which

is defined using the ‘‘experience’’ but also the preferences of the decision maker) and on

the existence of specific projects or processes for the particular action, the model

proposes the most appropriate form for the action.

B Concerning the qualitative indicators, the model does not use the equation (3), but

assesses their evolution using appropriate thresholds.


Building block 5. Prioritisation

The last building block receives input from the previous building blocks, i.e. the group of

necessary actions, so as to evaluate them and create a priority list. This evaluation is based

on the quantification of multiple qualitative judgements, based on a multi-criteria decision

making (MCDM) method, the ELECTRE III. Reader can refer to the Appendix for a short

presentation of the main mathematical equations of which the ELECTREE III is constituted

(Maystre et al., 1994; Rogers et al., 2000).

Multi-criteria analysis has been used in order to select from multi-attribute discrete options,

which is the case for our problem. Both in scientific literature and in real life, there are many

controversies about the most appropriate MCDM method (Jacquet-Lagreze and Siskos,

2001). The selection of a method for a specific multi-criteria problem is a complex procedure

which depends on the problem type, the quantity and quality of knowledge related to the

problem as well as the number of decision makers. Methods based on the utility function (such

as the multi-attribute value function and the analytic hierarchy process) require particularly

detailed information from the part of the decision maker. The mathematic representation and

comparison through the utility function, does not offer the possibility for non-comparison

between alternatives, which is a common phenomenon in real life. On the other side, methods

based on the outranking relation (such as the PROMETHEE and ELECTRE family) have the

characteristic that they require less information from the part of the decision maker. The binary

approach (concordance-discordance) is a basic advantage in comparison to other methods.

They also permit the non-comparison between alternatives. In this way, the decision maker

can express its hesitation about some alternatives. The PROMETHEE methods have the

characteristic of choosing the criterion functions between six types (Brans et al., 1986),

something that limits the criteria selection, on a problem such as the one that this paper

addresses. For these reasons, the authors have chosen to use the ELECTRE III method.

The first step is the definition of the criteria that will be used for the prioritisation of the

actions. For this purpose, the main outcomes of author’s prior research concerning main

challenges that contemporary KBD strategies should respond to (Ergazakis et al., 2006b),

have been considered. An unquestionable need is also to increase the knowledge intensity

in a region, which has as strategic objective to be developed as a KC (Montreal Knowledge

City Advisory Committee, 2003, pp. 18-19). Table IV presents these criteria.

The next step is the definition of decision maker’s preferences, through the introduction of

weights for each criterion. There are various methods for the weights’ definition: Hokkanen

and Salminen, 1994; Figueira and Roy, 2002 (Simos’ method); Rogers et al., 2000 (personal

construct theory – PCT); Mousseau (1993). In our case, the PCT method has been chosen,

which is relatively simple and easily understood to all the implicated parties in the decision

making process.

Moreover, the decision maker defines the thresholds of indifference, (q), preference (p), and

veto (v). The values q, p and v are subjective and express the preferences of the decision

maker. The decision maker must also define the values g(a) which reflect the value that is

attributed to an action, for each criterion. The possible values are: 5: Very high effect; 4: High

effect; 3: Slightly high effect; 2: Low effect; 1: Very low effect; 0: No effect.This means that an

action can have a particular effect, to any of the criteria. However, each dimension (and their

respective actions) contributes mainly to some of the criteria (Ergazakis et al., 2006c). The

main contribution of each dimension to one or more criteria is illustrated in Figure 5.

The final step concerns the prioritisation of proposed actions, based on the data provided by

the decision maker and to the standard mathematical equations used in the ELECTRE III

method.

The model’s application on a Greek municipality

The Greek municipality of Maroussi, located in the north suburbs of Athens, has already

been engaged in a KBD effort, adopting the KnowCis method. It should be noted that due to

important restrictions regarding available resources, it was impossible to fully implement the

KnowCis method. The reader could refer to Ergazakis et al., 2006e for further details. At the


present stage, the detailed action plan has been almost finalised, taking into consideration

the priority of the actions to be implemented. In what follows, the basic conclusions from the

first two phases of the method and especially regarding the simulation that took place by

using the developed decision support model, are presented

Phase 1: diagnosis of present situation

In the qualitative diagnosis of the municipality’s current situation, executives of local

administration as well as certain representatives of citizens, enterprises and cultural

organizations (working group) have participated. The basis for this diagnosis was each

dimension of the KnowCis method. Many of the included indicators acted as catalyst for a

series of discussions and debates. For each dimension, basic advantages and

disadvantages of the municipality have been identified, with regards to its capacity to

meet the requirements deriving from it.

Phase 2: formulation of strategy

As stated above, due to some important restrictions, it was impossible to carry out analytical

measurement of all the indicators included in KnowCis. Thus, the next stage of the pilot

Table IV The criteria

Increase of knowledge intensityC1. Degree of knowledge production This criterion evaluates the degree in which the

proposed action contributes to the degree ofdynamics of knowledge produced in the city aswell as to the strong flow of this knowledge whichis, in turn, conducive to various types ofinnovation

C2. Pace of assimilation and use of newknowledge

This criterion evaluates the degree in which theproposed action contributes to the pace ofassimilation and use of new knowledge, that is tosay to the city’s ability to consistently reapbenefits of new knowledge and new expertise

C3. Scope of knowledge circulation This criterion evaluates the degree in which theproposed action contributes to the scope ofknowledge circulation in the city, that is to say tothe amount dissemination and sharing ofknowledge (whether among the individuals ororganisations, across industry segments orgeographical regions), which is anotherbenchmark for quality in a KC

Respond to KBD challengesC4. Democratisation of KM processes/increasedcitizens’ participation

This criterion evaluates the degree in which theproposed action contributes to thedemocratisation of processes through whichknowledge is created, stored, shared and usedand consequently to the increase of broadcitizen’s participation in these KM processes

C5. Digital divide replacement by digitalinclusion

This criterion evaluates the degree in which theproposed action contributes to the replacementof digital divide by digital inclusion, as well as tothe availability and flow of the new ICTs’ benefitsto all citizens

C6. Reinforcement of business environment This criterion evaluates the degree in which theaction contributes to the creation and formationof an appropriate business environment, whichfosters innovation and favours the acquisitionand dissemination of knowledge and learning,and consequently is necessary for companies tosurvive, in the context of the knowledge-basedeconomy


application was the selection and the prioritization, from the part of the working group, of the

actions (among the ones existing in KnowCis) considering them necessary for the

municipality. This process was realized exclusively by this working group, without any

external influences and it was based on the former diagnosis, on available data from existing

polls and studies, on qualitative criteria, on the experience of the participants and finally on

their in-depth knowledge regarding the characteristics, the needs and the prospects of their

Municipality. Out of 25 actions, 22 were selected. They are presented in Figure 6.

The next stage of the pilot application was the simulation through the developed decision

support model. For this purpose, the following actions took place:

B All the available data from studies, polls and other measurements were collected and

used so as to calculate the values for some main and secondary indicators.

B For the remaining indicators it was impossible, in the framework of the particular research,

to conduct new polls, measurements and studies. For this reason, a qualitative

determination of their values, based on the experience of municipality’s executives and

representatives, took place.

B The threshold values of preference, indifference and veto were determined as well as the

value of each action for the used criteria, so as to reflect the preferences of the working

group.

The above data were supplied to the proposed decision support model. Its output was the

proposal of 23 necessary actions, prioritized as depicted in Figure 7.

Comparison with the corresponding results resulted based on the experience (Figure 8)

shows that:

Figure 5 The main contribution of each dimension to the criteria


1. The number of proposed actions is almost the same (23 using the decision support

model, 22 based on the experience).

2. The differentiations in the final priority lists are:

B Two (2) actions (9.09 percent) are in the same order of prioritization.

B Ten (10) actions (45.45 percent) differ one (1) place in the final prioritization.

B Six (6) actions (27.27 percent) differ two (2) places in the final prioritization.

B Three (3) actions (13.64) differ three (3) places in the final prioritization.

B One (1) action (4.55 percent) differs seven (7) places in the final prioritization.

Figure 6 Actions’ selection and classification based on the experience of the working

group


The main conclusion is that the variations between the two methods are not important. Of

course, in order for this conclusion to be verified with absolute confidence, it is obligatory to

precise accurately the values for all the indicators.

Conclusions and future research challenges

The concept of a KC and the advantages that can offer on a global and local scale are

important, so as to be ignored by the policy makers (Ergazakis et al., 2006a). In this respect

and given that the research on the field is still on embryonic phase, the need to identify and

Figure 7 Actions’ selection and classification based on the decision support model


propose integrated and unified methodologies for the development and formulation of

related strategies, is realistic.

The authors, based on a recently developed method (KnowCis), proposed a decision

support model for the formulation of KC’s strategy. The model consists of the following

building blocks: identification of the appropriate actions (based on KnowCis), modelling of

the city’s current status as a KC (via the development of related indicators), assessment of

the necessity for the actions (based on the indicators’ outcomes and through the

benchmarking of other successful KCs cases), selection of the most appropriate form of

each proposed action (based on their efficiency during the last reference period) and, finally,

prioritisation of the proposed actions (based on a multi-criteria approach). The model has

the following advantages:

B Integrated. Combined with the KnowCis method, it constitutes an integrated tool that can

be used by a city or urban region with the vision to develop a knowledge-based strategy.

B Flexible. The model is easily adaptable so as to incorporate and reflect the city’s specific

strengths, weaknesses, opportunities and objectives. This can be achieved by the right

selection of indicators and their thresholds, appropriate criteria and relative weights, and

actions.

B Encouraging preliminary results. As described in the last section, the preliminary results

from the pilot application of the decision support model to a Greek municipality are

encouraging, in the sense that variations between the two methods (based on experience

and based on the model) are not important. Of course, as already stated, it is more than

necessary to precise accurately the values for all the indicators, in order for this

conclusion to be verified with absolute confidence.

Two major streams of future research are the following:

1. The development of an intelligent decision support system incorporating the building

blocks of the proposed model. Such a system would help the decision maker to quickly

implement the proposed model on this paper. Expert systems technology could offer a

series of advantages for the design and development of such a system.

2. Further enhancement of KnowCis method so as to render it more relevant and realistic in

regards to the variety of aspects on a real city: incorporation of additional dimensions,

actions and indicators are simple examples of such enhancements.

Figure 8 Differentiations in actions’ final priority lists


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Appendix: ELECTRE III multi-criteria decision making

a and b represent two possible alternatives (actions in our case). g(a) represents the valuethat is attributed to an action, for each criterion. The following thresholds must be defined:

B Indifference (q) is the threshold below which the decision maker is indifferent as regardstwo alternatives.

B Preference (p) is the threshold above which the decision maker strongly prefers onealternative over another.

B Veto (v) is the threshold where a difference above which the decision maker must denyany advantage deriving from other criteria.

It is:

B apb (alternative a is preferred by b) ,g(a) . g(b) þp

B aqb (alternative a is preferred partially by b),q # g(a) – g(b) # p

B aIb (alternative a is indifferent to b) ,jg(a) – g(b)j # q

Using thresholds, ELECTRE III creates a new relation S. The relation asb implies that the

alternative a is at least as good as b or that a is not worse than b. There are two principles:

B a concordance principle which requires that a majority of criteria, after considering theirrelative importance, is in favor of the hypothesis – the majority principle; and

B a non-discordance principle which requires that within the minority of criteria which do notsupport the hypothesis, none of them is strongly against the hypothesis – the respect ofminorities’ principle.

In order to find if the hypothesis asb is true, a concordance matrix C(a,b) for each pair of

alternative action is being calculated:

Cða;bÞ ¼ 1

w

Xn

t¼1

wtctða;bÞ

where wt is the relative weight of criterion t, with w ¼Xn

t¼1

wt.


Also:

ctða;bÞ ¼

1; if gtðaÞ þ qt $ gtðbÞ0; if gtðaÞ þ pt # gtðbÞ; t ¼ 1; 2; . . .n

ptþgtðaÞ2gtðbÞpt2qj

; else

8>>><>>>:

The matrix of discordance for each criterion is also calculated, taking into consideration anadditional threshold (veto) which allows the refusal of the hypothesis asb, for every criterionfor which gt(b) . gt(a) þ vt is true. Thus, the matrix of discordance is calculated:

dtða;bÞ ¼

0; if gtðaÞ þ pt $ gtðbÞ1; if gtðaÞ þ vt # gtðbÞ; t ¼ 1; 2; . . .n

gtðbÞ2gtðaÞ2pt

vt2pt

8>>><>>>:

By combining the matrices of concordance and discordance, a degree of outranking isproduced. The credibility degree for each pair (a,b) is defined as:

Sða;bÞ ¼Cða;bÞ if dtða;bÞ # Cða;bÞ;t

Cða;bÞ ·t[Tða;bÞ

Q 12dtða;bÞ12Cða;bÞ

8><>:

Where T (a, b) is a team of criteria for which dt(a, b) . C (a, b).

The interpretation of credibility degree S(a,b) must be done with particular attention. It couldbe also characterized as ‘‘order of importance’’ in order to support the statement thatalternative a prevails b.

About the authors

Kostas Ergazakis is a researcher at the School of Electrical and Computer Engineering,National Technical University of Athens (NTUA), Greece. He is the corresponding author andcan be contacted at: [email protected]

Kostas Metaxiotis is an advisor to the secretary for the Information Society in the GreekMinistry of Economy and Finance.

John Psarras is an Associate Professor at the School of Electrical and ComputerEngineering, National Technical University of Athens (NTUA), Greece.

Dimitris Askounis is an Assistant Professor at the School of Electrical and ComputerEngineering, National Technical University of Athens (NTUA), Greece.


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An integrated decision support model for a knowledge city ...pdfs.semanticscholar.org/5a80/22f9c21c4c3bb88533644009f2e31bc23f4f.pdfKeywords Decision making, Emergent strategy, Knowledge