The themes of technological innovation, entrepreneurship, and organizing

Choosing Locations for Technology and Innovation Support Centers: Methodological Proposal and Brazilian Studies

Mario Otavio Batalha

Federal University of Sao Carlos, Brazil

Daniela Tatiane dos Santos

Federal University of Sao Carlos, Brazil

Nelson Guedes de Alcantara

Federal University of Sao Carlos, Brazil

Sergio Ronaldo Granemann

University of Brasilia, Brazil

ABSTRACT

This chapter discusses the structuring of problems of location of Technology and Innovation Support Centers (TISC) through multicriteria analyses to identify factors of demand and supply of these services. The methodology uses quantitative and qualitative elements, establishing a sequence of steps that include a variety of aspects ranging from criteria preferences to global valuation of the model. Multicriteria analysis was applied to the choice of geographic locations for Brazilian Technology Centers, allowing for the identification of the most suitable regions for the creation of technology centers and revealing particular characteristics of the dynamics of such services in the regions in question.

DOI: 10.4018/978-1-61350-165-8.ch025

Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

INTRODUCTION

Many developing countries have made consider­able efforts to reduce technological disadvantages that prevent them from implementing innovations to establish high quality standards and internalize core activities of technical progress (World Bank,

2008) . These efforts have received government incentives for innovation, and involved numer­ous companies that have come to recognize the well-nigh inseparable link between innovation and competitiveness. These companies require a series of technological services (assays, calibration, standardization, inspection, new product devel­opment, etc.) of a strongly horizontal nature and with multiplying effects on the economy. More­over, the supply of such services can contribute to consolidate more prosperous and industrially advanced regions, reinforcing the dynamic effects of technological demand (Feller, Ailes & Roessner, 2002; Kakuta & Luz, 2001).

The supply of technology support services plays a dynamizing role in an economy, favoring the innovation process by overcoming techni­cal obstacles than hinder market action. Many Technology and Innovation Support Centers (TISCs) provide a technical basis for specialized knowledge that reinforces innovation generated in companies. The Danish TISCs are a well known ex­ample - a set of nine large networking laboratorie s whose activities are broadly cross-sectional and whose research and development efforts extend far beyond the mere supply of technical services (Andersen et al., 2009). The proximity of a TISC which can provide technological resources to a large number of companies can be considered an element that contributes to cost reductions through transaction economies (Williamson, 1989), and whose main characteristic is that of facilitating the acquisition of services. The presence of a TISC can also compensate for competencies companies lack, enabling them to concentrate on their core business, such as the production process itself.

Above and beyond business interests, a TISC is also important for other economic and govern­ment agents (Zucker, Darby & Armstrong, 1998,

2002) . Professors and researchers could use TISC laboratory facilities for research, and a TISC could be an important tool for politicians to support and boost regional development. However, it should be noted that politics will not, per se, ensure the success of a TISC.

Brazilian experience indicates that purely bureaucratic government initiatives to establish Technology and Innovation Support Centers (TISCs) in regions that lack a preexisting indus­trial structure have failed. Among the factors that have most contributed to the many faulty choices for TISC locations is the lack of qualified human resources. Thus, when evaluating a location for a TISC, it is necessary to consider regions that have a relatively consolidated education, productive and technological structure, and will therefore benefit more from it.

This chapter starts from the above premises, using elements of demand and supply of technol­ogy services to structure problems relating to the location of TISCs. Multicriteria analysis has not been widely used in the literature on innovation management. However, methods of multicriteria analysis can contribute considerably to decision making about technology choices by public in­stitutions. The use of methods such as the one proposed here is aimed at finding solutions for the location of TISCs that are less subject to the inevitable political pressures that such decisions engender.

The starting point for this work is the need to identify cities in the Central West (CW), North (N), and Northeast (NE) regions of Brazil with the best conditions to establish a TISC in the area of materials, limited to one center per region. This project was funded by FINEP (Study and Project Financing Agency), which is linked to the Brazilian Ministry of Science and Technology. Materials Technology Centers (MTC) contribute to the core competencies and innovations of lo­

cal industries, regions, states, and to the country, offering technological support in science and engineering areas. In countries such as Brazil, MTCs favor innovation-related activities through the development of new materials, e. g., biomedi­cal and smart materials, advanced materials for practical applications such as civil infrastructures, compressor engineering and manufacturing, or the fabrication of materials and structures. The strategic management of these Centers would be a fundamental instrument for achieving cumula­tively sustainable competencies that could reduce the technological gaps existing among nations with different levels of development.

The multicriteria method used here was the Analytic Hierarchy Process - AHP (Saaty, 1990), which allowed for the development of a decision process based on quantitative and qualitative criteria. The results obtained with this method were consistent and did not pose any problems of comparison between the criteria and possible alternative solutions.

The AHP was used as a method for location choice, and the results obtained indicate the suit­ability of this approach. The application of AHP to location decisions has been tested in different types of problems and in different areas. One of the drawbacks of this method, however, is the number of location criteria that can be considered in the analysis of the problem. The AHP allows for the consideration of a maximum of 9 criteria or a group of criteria, although the ideal situa­tion would be the use of a minimum of 5 to 7 criteria and a maximum of 7 to 9 criteria. There is no doubt that the determinants involved in the choice of technology centers are much greater than those considered in this work. Therefore, to use the AHP rationally required grouping the criteria with similar characteristics around aspects of supply and demand for technological services.

This chapter is divided into five main sections, besides this introduction. The second section fo­cuses on a review of the literature on the process of choosing the location of technology centers, as well as some factors concerning the demand and supply of technology services. The third and fourth sections focus on applying the multicriteria analysis for decision making about the location of centers, as well as the main results and discus­sion. The last section presents the conclusions of this study.

TECHNOLOGY SUPPORT CENTERS AND CHOICE OF LOCATION

Technology support centers can be defined as institutes engaged not only in providing goods and basic laboratory services but also research activi­ties and the development ofproducts and processes (COTEC, 2003). Their most routine activities involve the short-term demands of companies, mainly those relating to projects aimed at gener­ating economies of scale and meeting immediate market needs. However, more advanced studies that favor long-term industrial competitiveness can and should also be conducted by these centers.

Numerous countries have focused efforts towards establishing and consolidating TISCs. There are several different models for creating institutes with reduced technological intensity (technology transfer centers, support centers to increase competitiveness, technology acquisi­tion consortiums) or even more daring ventures involving institutes of high density technology (public research and development centers, private R&D centers, mixed R&D centers, and NGOs). A large part of such institutes have achieved results that are highly relevant for the economy of the countries and regions where they are established.

Nevertheless, a study by Beise and Stahl (1999) that analyzed the effects of public research on industrial innovations in Germany between 1993 and 1995 found that many public technology cen­ters failed to transfer knowledge to companies. Even so, many companies would not be able to implement innovations in the absence of these technology centers. The authors also point out that the role of stimulating innovation is, in most cases, attributed to the universities that contain technol­ogy centers rather than to the centers themselves.

A set of preconditions related to the character­istics of the area where the center is to be located seems to be crucial for the successful establish­ment and development of technology centers. Some studies on center location, such as that of Krugman, Fujita and Vernables (1999), have been based, to a certain extent, on concepts established by the classic theory of industrial location (Von Thunen, 1826; Teruya, 1999; Predohl, 1928). In general, the aim is to analyze the location of economic activities by identifying factors such as raw materials transportation cost and labor cost.

According to Predohl (1928), economic location is a function of the compared costs of transportation of the different production factors (capital, work, and land) and the relative prices. Having defined the costs involved and the pos­sible revenue to be obtained, it would be possible to determine the best combination of factors that would allow for choice optimization. This is a systematic approach to understanding the geographic advantages offered by each location where investments are to be made.

The classical theory about location choice offers important contributions towards an under­standing of the elements that foster the attraction of enterprises. However, such an approach would reduce decision making to strictly quantitative ele­ments that may not always be relevant to a TISC, and that are only partially applicable in explaining the pattern of location of technology enterprises. For example, the availability of labor and parts for equipment maintenance is more relevant to the success of a TISC than the availability of raw material.

In this context, the contribution of industrial clusters has been highlighted by Gordon and Mc­Cann (2000) and by Morosini (2004). Research and development institutes, like the TISCs situated in industrial centers, would obtain economies of scale and scope by cooperating with companies and other institutes. It should be noted that the mere presence of such institutes does not ensure the success of a cluster’s technology and inno­vation activities. Its success depends on a set of other factors such as interaction and cooperation among local agents, the presence of qualified labor in the region, relationships with suppliers, and, in some cases, the existence of government incentives. Jaffe, Trajtenberg and Henderson (1993) investigated the effects of the geographic proximity of spillovers. An understanding of the location choice of many Technology Centers may point to the characteristics of technology diffu­sion in a given region. On the other hand, Beise and Stahl (1999) discard the hypothesis that the proximity of public research centers would be a determining factor for the implementation of technology innovation by companies. Moreover, many authors have attempted to adapt some of the classical elements of industrial location to the specificities and needs for the establishment of regional technology development policies, including the creation of TISCs.

Public sector policies and the innovation potential of a region are issues that have been ad­dressed by authors such as Breschi and Malerba

(2001) and Salerno and Kubota (2008). It is worth highlighting the importance contemporary theory has attributed to the State’s role as a promoter of regional development policies. This role leads to the recognition that natural resources and other production factors do not suffice to justify in­dustrial location in those areas based on classical competitive advantages.

The location of technology centers is only partially determined by government incentive poli­cies. The dynamics oftechnological innovation has also provided decisive interpretations in analyzing the success of TISCs. It has been found that the location concentration of technology centers has been determined by complex and specialized fac­tors, particularly by scientifically and technically qualified labor, communication infrastructure, and sophisticated consumer markets.

These factors have made it possible to take advantage of synergies that result from acting in networks. Many technology centers have the ability to combine the needs of industry and the capabilities of local universities in the construc­tion of new standards of quality in products and processes. Thus, the various factors set forth in the classical and contemporary literature concerning the reasons for locating technology centers in a given region should be specified based on elements ofthe demand and supply of technology services, which is the subject of this study.

DEMAND AND SUPPLY OF TECHNOLOGY SERVICES

Table 1. Services generally supplied by technology centers

Generation and acquisition of knowledge

Preparation for production

Preparation for commercialization

Generation of new products and processes (R&D projects)

Standardization and quality

Market studies and business plans

Support for the acquisition of technology

Pilot facilities

Support for investments in new markets

Education and access to new ideas

Process modernization and automation

Support for the creation of new companies (spin offs)

Access to qualified resources

Assays, tests and certifications

Support for the launch of new products

Support for the acquisition of imported equip­ment

Support for the creation of new produc­tion lines

Support for intellectual property

Source: Adapted from COTEC (2003).

The services of technology centers can be classi­fied according to different criteria (COTEC, 2003). One of them classifies technological services ac­cording to their functionality, which may involve knowledge sharing activities (courses, lectures, and training), activities that promote interaction between different economic agents, or even func­tions that favor the supply of specialized services to a company. This functionality may lead toward an approach that specifies the services of technol­ogy centers according to different stages of the production process, encompassing the generation and acquisition ofknowledge and technology and the preparation for production and commercializa­tion, as illustrated in Table 1.

Some elements of the demand for technology center services are related to the specificities of a region’s industrial structure. First of all, it can be stated that the supply of regional technological services appears to derive from the characteristics of the size of companies situated in the region. Wren and Storey (2002) found that the probabil­ity of using these services is positively correlated to the number of employees in a company with a workforce of up to 150, and negatively correlated to companies with a larger workforce. A possible explanation for this finding is the internalization of research and development by companies whose larger size enables them to carry out activities in-house which were previously outsourced.

Smaller companies, but with a solid technol­ogy basis, are strongly dependent on a variety of technological services. The technological level achieved by a company is undoubtedly a determin­ing condition for the demand of services from a technology center (Quevedo & Mas-Verdh, 2008). It can be stated that companies lacking in techno­logical dynamism do not make permanent use of services such as patent-related activities, quality control, or product certification. However, such needs are common in companies that have already reached higher qualitative standards. Smallbone (1993) found evidence that consolidated or mature companies, regardless oftheir size, are more likely
to require outsourced technological services. This indicates the need for the adoption of measures, besides those traditionally implemented in tech­nology centers, aimed at stimulating younger companies to develop innovative ideas.

The export dynamics of companies seems to be related directly to the technological content of the production exported by given regions. Exposure to competition from foreign companies leads to the search for competitive advantages in terms of product and service differentiation, and expands the areas to be covered by technology support centers (Quevedo & Mas-Verdf, 2008). In this context, the need for higher levels of competitiveness may explain the greater use of TISCs. In developing countries, companies seek­ing to expand their share in international trade should adapt to international standards and norms through technological services aimed directly at overcoming technical and commercial barriers. These barriers can be obstacles to growth if a region is unable to overcome the challenges of technological evolution and the requirements of more developed markets (Kakuta & Luz, 2001).

On the other hand, the availability of techno­logical services favors directly the achievement of systemic competitiveness, helping in the definition of more industrially advanced areas. Qualified human resources such as specialized researchers, whose expertise can be used by these institutes, and the availability of laboratory infrastructure, machinery and equipment, as well as training courses related to the activities of the TISC, are elements consistent with the aforementioned theories.

The institutional conditions in which the supply of these services takes place are highly varied. In this context, one must keep in mind that the environment of technological research in developing countries has always been linked, one way or another, to government institutions. One of the consequences of this institutional design, which is particularly true in the case of Brazil, is that few centers and institutes have achieved the economic autonomy that would render them independent of public sources. This situation is due to the fact that the technological solutions ofthese centers have often failed to be market-oriented, or even because company demands showed little technological dynamism, focusing mainly on the acquisition of foreign technology (Kakuta & Luz,

2001) . Nevertheless, according to these authors, knowledge about the forces that drive the demand and supply for technological services in Brazil is still limited, indicating the need for more in-depth studies on this issue.

METHODOLOGICAL PROCEDURES General Methodological Aspects of this Research

Multicriteria analysis was used in two distinct phases of this study. The first phase consisted of an extensive review of the literature and searches on specialized websites to find the quantitative variables most commonly used for the identi­fication of demand and supply of technological services. The variables thus identified were then collected and compiled in tables for an initial multicriteria analysis. The purpose ofthis analysis was to select, among the 19 states in the Central West, North, and Northeast regions of Brazil, the ones that would be the object of a detailed field research. This initial analysis was necessary due to the limited time and resources to carry out the study. Several states therefore were discarded in this initial stage of the study.

The quantitative information was garnered from various sources, such as the Brazilian Insti­tute of Geography and Statistics (IBGE), Ministry of Labor and Employment (MTE), Secretariat of Foreign Trade (SECEX), Council for Scientific and Technological Development (CNPq), and Brazilian Technology Network of the Ministry of Science and Technology (MCT).

The second multicriteria analysis, which provided the final guidance for the best loca­tions for TISCs, required gathering qualitative information in the cities and states selected in the initial analysis. In each city, interviews were held with industrial federations, research foun­dations, universities, and research centers. The characteristics of the method used here and the procedure for selecting the most suitable states for the creation of Materials Technology Centers (MTC) are explained below.

The Method of Multicriteria Analysis

The use of methods of multicriteria analysis al­lows for the inclusion of criteria of individual values designated by specialists in order to solve a number of problems simultaneously. These methods include the Analytic Hierarchy Process (AHP), which aims to associate the list of pref­erences (subjective) with the various criteria in the decision-making process, considering both quantitative and qualitative variables (Saaty, 1990; Granemann, Tedesco & Candal, 2008). The determining factor for the choice of the AHP method was the possibility of treating qualitative variables based on intangible criteria, which are therefore more difficult to evaluate.

The method is structured on a sequence of steps that encompass the delimitation of the objective and the relevant criteria for problem-solving and decision-making, the determination of alterna­tives, evaluation of the relative importance of each criterion and of the alternatives in relation to the criteria, and determination of the global valuation of each alternative.

After considering the relative importance of the criteria, an entirely analogous process is used to establish the level of preference of the alterna­tives. Lastly, the global valuation is considered according to the weighted sum method, using the following equation:

V(?) = E Px ' ax (q), E Px = 1 and 0^x^

x=i x=i

(1)

where:

V(q): global valuation of the alternative px: weight of criterion x ax: level of preference of the alternatives ana­lyzed in the criterion

Thus, the aforementioned model was applied to identify the optimal location oftechnology centers, considering that the criteria would be the factors most closely related to the demand and supply for technological services and that would determine the attractiveness of the various regions.

The Process of Location Choice

First Multicriteria Analysis

The purpose of this section is the selection, among the 19 states in the Central West, North, and Northeast regions of Brazil, of the ones with the best conditions for the creation of Materials Technology Centers. The project’s goal was to reduce technological disparities between Brazilian states. In general, Brazil’s Central West, North, and Northeast regions have less advanced technology centers than the South and Southeast regions.

Table 2 identifies the criteria selected for the first multicriteria analysis and described their characteristics and importance for this type of decision making.

Eight critical variables were used here to ex­plain the demand and supply of innovation-relat­ed technological services, three of them related to supply and five to demand. Note that the human resources indicator (HRI) and the publication indicator (PI) are composite indicators obtained by combining two or more variables. Table 3 lists the values of these indicators for all the states considered in the analysis.

Table 2. Variables used in the first multicriteria analysis

Analysis

Criteria (vari­ables)

Indicator source and creation

Importance

HRI (Human re­sources indicator)

Assignment of weights to the sum of research­ers with a master’s degree (weight 1), with a doctoral degree in the field of materials or related area (weight 2). Indicator created based on the Tabular Plan of the Council for Scientific and Technological Development (CNPq) (2004).

Indicator of the level of training of human resourc­es qualified to work for a technology center

Supply

PI (Publication indicator)

Assignment of weights to the sum of the num­ber of national (weight 4) and international (weight 8) publications, published book chap­ters (weight 4), and complete works published in the proceedings of events in materials or correlated areas (weight 2). Indicator created based on the Tabular Plan of CNPq (2004).

Indicator of the presence of knowledge and intellec­tual qualifications that can favor the sustainability of the TISC

Research institutes and universities

Research Institutes and universities acting in materials or correlated areas* registered with CNPq (2004) and in the Brazilian Network of Technology - MCT (2007)

Indicator of the number of institutes (supply). The creation of technology centers in states that already have research institutes and universities may favor joint collaborations.

Exports

Secretariat of Foreign Trade (2006)

In addition to indicating the potential demand for a technology center resulting from stimuli from foreign markets, it demonstrates the technological content of the exports of the states.

Number of em­ployees

Annual Record of Social Information - Minis­try of Labor and Employment of Brazil (2004).

Quantifies the workforce formally employed

Demand

Number of em­ployees with higher education

Annual Record of Social Information - Minis­try of Labor and Employment of Brazil (2004).

Quantifies the workforce with higher education.

The demand of technologically dynamic companies represents potential clients to be served

Number of local units

Annual Industrial Survey - Brazilian Institute of Geography and Statistics (2005)

Industrial companies with 5 or more employees. Allows for quantification of the potential market to be served by the TISC

Industrial transfor­mation value

Annual Industrial Survey - Brazilian Institute of Geography and Statistics (2005).

Difference between the gross value of industrial production and industrial operating costs. Indicator of productivity that shows the added value of each segment to the state’s production.

Source: Developed by the authors.

* The fields refer to materials-related activities such as chemistry, chemical engineering, mechanics and physics.

The research team prioritized the criteria in decreasing order of importance, which resulted in the following hierarchy: research institutes and universities, Human Resources Indicator (HRI), Publication Indicator (PI), number of employees, exports, number of local units, and industrial transformation value.

Greater importance was given to the presence of research institutes and universities acting in materials and/or related fields, since their presence indicates initiatives and efforts already made in this area. The second most important factor was the Human Resources Indicator (HRI), since it is understood that TISCs need researchers with expertise in the areas of materials.

Alternatives

(States)

Human

re­

sources

indica­

tor

(2004)

Publi­

cation

Indica­

tor

(2004)

Research institutes and uni­versities (2004)

Exports US$ (2006)

Number of

employees

(2004)

Num­ber of employ­ees with higher educa­tion (2004)

Num­ber of local units (2005)

Industrial transforma­tion value in R$

(2005)

Central West

Federal District

1,723

47,984

9

65,749,524

18,588

1,352

919

1,140,079

Goias

1,031

26,144

8

2,092,027,930

135,717

4,119

4,513

8,501,887

Mato Grosso

493

6,094

3

4,333,376,419

72,248

1,385

2,403

6,295,994

Mato Grosso Sul

743

15,716

5

1,004,204,248

48,391

1,408

1,314

2,766,885

North

Acre

77

1,266

3

17,795,969

3,644

50

184

95,688

Amapa

20

82

2

127,980,007

2,779

67

107

259,972

Amazonas

1,033

18,740

11

1,522,851,015

86,236

5,093

918

19,769,522

Para

1,088

24,046

10

6,707,603,218

90,479

2,323

2,019

8,165,561

Rondonia

40

638

1

308,018,812

25,945

217

1,073

1,126,990

Roraima

161

2,608

2

15,358,447

1,523

21

96

30,665

Tocantins

243

4,888

4

203,886,580

8,702

119

392

276,118

Northeast

Alagoas

419

8,422

3

692,543,376

94,916

1,435

667

2,075,454

Bahia

1,973

43,474

13

6,771,981,469

148,102

6,576

4,160

24,184,645

Ceara

1,559

43,512

12

957,045,076

176,854

3,826

3,805

5,392,342

Maranhao

354

10,636

3

1,712,701,103

23,190

560

764

2,235,720

Paraiba

1,510

48,312

8

208,589,087

51,153

1,447

1,302

1,852,656

Pernambuco

2,237

62,774

11

780,340,072

147,209

5,573

3,786

5,675,220

Piaui

249

3,416

3

47,127,095

20,777

555

819

702,169

Rio Grande Norte

880

24,110

7

371,503,239

55,095

1,753

1,349

2,904,517

Sergipe

435

9,002

3

78,939,173

29,116

1,121

825

2,638,893

Total

16,268

401,864

121

28,019,621,859

1,240,664

39,000

31,415

96,090,977

Source: Brazilian Institute of Geography and Statistics (IBGE), Ministry of Labor and Employment (MTE), Secretariat of Foreign Trade (SECEX), National Council for Scientific and Technological Development (CNPq), and Brazilian Network of Technology of the Ministry of Science and Technology (MCT)

A relatively lower degree of importance was given to the publication indicator (PI), which re­fers to studies published in the area of materials and/or related fields and indicates the presence of knowledge and intellectual capability that can favor the operations of a TISC. The lesser importance of HRI and PI compared to the pres­ence of research institutes and universities is due to the fact that human resources and knowledge
can be transferred to nearby regions to meet the requirements of the TISC.

Ranking fourth and fifth in terms of priority are, respectively, the number of people with a higher education working in the state’s industrial companies and the overall number of employees. The number of employees with a higher educa­tion indicates the qualifications ofthe companies’ human resources. For a TISC, the demand of technologically dynamic companies represents potential clients to be served. Precisely because the technological dynamism of these companies is relevant to a center, a lower priority was given to the overall number of employees.

The two lowest priority levels were assigned to exports and to the number of local units. An export analysis can indicate the degree of insertion of companies in the export market. However, this insertion may occur mainly through goods with low added value, as is typically the case of com­modity exporter states. Hence, even if the export rate may serve to indicate the size ofthe market to be served by a TISC, the technologically dynamic export markets in the Central West, North, and Northeast regions to be served by this TISC would be relatively small, since these Brazilian regions are known historically as exporters of low added value goods. Similarly, although the number of local companies allows for quantification of the potential market to be served by a TISC, it does not guarantee that these industrial companies actually require technological services. Therefore, less importance was given to these criteria than to the previous ones.

Lastly, an even lower priority was assigned to the industrial transformation value, which is defined as the difference between gross indus­trial production and industrial operating costs (intermediate consumption). This is a productivity indicator that indicates each sector’s added value to the production of the state under analysis.

The lower priority given to the industrial transformation value compared to exports is due to the fact that the intermediate consumption of the sectors that require technology in these states depends, to a large extent, on imports, which contributed to diminish the role to be played by the TISC.

Therefore, the parity comparisons resulted in the following weights for the criteria: research in­stitutes and universities (34.3%), human resources indicator (HRI) (24.4%), publication indicator (PI) (17.3%), number of employees with higher educa­tion (8.9%), overall number of employees (6.1%), exports (4.1%), number oflocal units (2.9%), and industrial transformation value (2.1%).

Table 4 presents the global valuation of the alternatives, which was obtained from their com­parison to each other based on each criterion. The global value indicates the attractiveness of the 19 states of the CW, N, and NE regions, considering that 7 states would be strong candidates to receive a technology support center. Note that the in CW region, the Federal District (Brasilia) and Goias are competitors, since they present attractiveness indices of 0.537 and 0.282, respectively.

On the other hand, the highest scoring states in the North are Para (0.362) and Amazonas (0.352), In the Northeast, Pernambuco, Bahia, Ceara and Paraiba presented the highest scores,

i. e., 0.246, 0.241, 0.146 and 0.141, respectively.

Second Multicriteria Analysis

The objective ofthis analysis is the choice, among the seven states and the Federal District selected in the first analysis, of the three states that would receive technology centers, one in each region of the country. In this stage, qualitative information garnered in the field research was combined with previously available quantitative data. Thus, seven variables were selected for a second multicriteria analysis, three of them related to supply and four to demand for technological services.

The variables of exports and industrial trans­formation value had already served as the basis for the first analysis. Five new variables that had been the objects of the field research were now included, namely, institutional environment, exist­ing infrastructure, technology service demanding sectors, private projects in the area of action of the TISC, and expanded human resources indi­cator. The following priorities were assigned to the criteria in decreasing order: demanding sec­tors, private projects, institutional environment, expanded HRI, industrial transformation value, existing infrastructure, and exports.

The first level of priority or importance was assigned to the existence of sectors that demand the TISCs’ services, which would indicate the current demand for materials-related technological services in the regions under analysis. The second

Alternatives (States)

Level of attractiveness

Central West

Federal District

0.537

Goias

0.282

Mato Grosso

0.081

Mato Grosso Sul

0.100

North

Para

0.362

Amazonas

0.352

Tocantins

0.109

Rondonia

0.054

Acre

0.051

Amapa

0.037

Roraima

0.035

Northeast

Pernambuco

0.246

Bahia

0.241

Ceara

0.146

Paraiba

0.141

Rio Grande Norte

0.084

Sergipe

0.050

Maranhao

0.036

Alagoas

0.030

Piarn

0.026

Source: Developed by the authors.

level of priority was assigned to the existence of large investment projects. This variable, which would indicate the existence of current and future demand for technological services on the part of large industrial projects, was assigned a lower priority than the existence of demanding sectors due to the uncertain nature of many projects, which could render them unviable.

The third and fourth levels of priority were assigned to the presence of an institutional environ­ment and the expanded human resources indicator. The presence of a favorable institutional environ­ment indicates the commitment of institutional support agencies to the creation of the TISC. The expanded human resources indicator denotes the level ofmaterials-related qualifications ofthe hu­man resources, as well as the research efforts that have been published in materials-related areas. The latter is a combination (average) ofthe HRI and PI indicators used in the first multicriteria analysis. The presence of a favorable institutional environ­ment was considered a more important requisite for the creation of a TISC than the presence of human resources, since people can be transferred to and from the regions near the TISC.

The fifth level of priority was assigned to the industrial transformation value of the states. In the first multicriteria analysis, a higher priority was assigned to exports than to the industrial transformation value, due to the characteristics of intermediate goods consumption in the CW, N, and NE regions, based, to a large extent, on imported technology. However, the interviews were conducted with the main representatives of the states investigated, such as presidents and directors of industry associations and unions, presidents of universities and professors of uni­versity departments. The interviews revealed that this priority could be inverted because the inter­mediate consumption of the sectors that require technological services depends little on imports and the demand can be supplied partially by local technology centers.

The sixth level of priority was assigned to the existence of infrastructure in the states, such as laboratories and equipment that could support the TISC structure. The lowest level of priority was assigned to exports, since exports in the regions under analysis consist of low added value goods, limiting the action of the TISC.

Thus, the resulting weights for the different criteria were: demanding sectors (36.8%), pri­vate projects (22.7%), institutional environment (17.1%), human resources indicator (9.9%), indus­trial transformation value (6.4%), infrastructure (4.5%), and exports (2.6%). Table 5 describes the evaluation of the alternatives according to different criteria.

In the Central West region, the state of Goias presented the highest scores in all the criteria with the exception of human resources, for which the

Table 5. Attractiveness of the alternatives according to different criteria

Alternatives

(States)

Criteria (variables)

Total

DEMSEC

PRIPROJ

INSTENV

HRIEXP

ITV

INFRA

EXP

Central West

Goias

0.315

0.114

0.086

0.017

0.057

0.038

0.023

0.650

Federal District

0.053

0.113

0.085

0.082

0.007

0.007

0.003

0.350

North

Amazonas

0.307

0.057

0.137

0.025

0.053

0.034

0.006

0.619

Para

0.061

0.170

0.034

0.075

0.011

0.011

0.020

0.382

Northeast

Pernambuco

0.099

0.145

0.095

0.057

0.010

0.018

0.003

0.427

Bahia

0.215

0.023

0.022

0.012

0.042

0.018

0.017

0.349

Ceara

0.038

0.048

0.046

0.012

0.009

0.003

0.005

0.161

Paraiba

0.016

0.011

0.008

0.018

0.003

0.006

0.001

0.063

Legend: DEMSEC: Demanding sectors; PRIPROJ: Private projects; INSTENV: Institutional environment; HRI EXP: Expanded human resources indicator; ITV: Industrial transformation value; INFRA: Available infrastructure; EXP: Exports

Federal District showed a better classification. In 2004, the number of publications by researchers with master’s and doctoral degrees, as well as the number of theses and dissertations concluded in the Federal District, was relatively higher than in the state of Goias. Nevertheless, the total sum of the variables (0.650 for Goias and 0.350 for the Federal District) indicated that Goias would be the most suitable location for a materials technol­ogy center in the Central Western region.

In the North, the state of Amazonas reached higher scores than the state ofPara for the following criteria: demanding sectors (0.307), institutional environment (0.137), industrial transformation value (0.053), and infrastructure (0.034), while Para showed a better performance in human re­sources, exports, and private projects. The sum of all the variables was 0.619 for the state of Ama­zonas and 0.382 for Para, which would justify the choice of the former state over the latter.

The state of Pernambuco showed the best performance in the Northeast region, with higher scores than the other states in three criteria: private projects, institutional environment, and human resources, Ceara scored higher than Pernambuco only in the exports criterion, and received the sec­ond highest score in the institutional environment criterion (0.046). The state of Paraiba presented the second best performance in human resources (0.018), Bahia scored higher than the other states in the following criteria: demanding sectors, indus­trial transformation value, and exports. However, the final sum of the variables favored the choice of Pernambuco as the most suitable state for a technology center in the Northeast.

The global valuation ofthe multicriteria analy­sis for the state of Pernambuco was 0.427, while the other states showed the following scores: Bahia 0.349, Ceara 0.161, and Paraiba 0.063.

RESULTS AND DISCUSSION

The evaluation of the process of location based on multicriteria analysis using the AHP method demonstrated that Pernambuco (NE), Amazonas (N), and Goias (CW) are the states with the best conditions for the creation of Materials Technology Centers (MTC). The two multicriteria analyses showed an inconsistency index of 0.07, indicating
that the results fall within standards acceptable to the AHP (maximum inconsistency of 0.1).

The use of the AHP model allowed for a better understanding ofthe different factors that affect the location of technology support centers. Analyses of the relative importance of all the factors used in the two multicriteria approaches led to the conclusion that there is a good proportionality in terms of the impact of demand and supply on the location of technology centers.

Infrastructure

----------- 1

I

Expanded human resouces indicator

& Institutional environment

О Publication indicator

I

Research institutes and universities

Total

Number of local unities

Exports

Industrial transformation value

-o Number of employees

л

E Number of employees w ith higher education

Q Private projects

Demanding sectors

Total

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

Figure 1. Relative importance ofthe factors that influence the choice of locations for technology support centers. (Source: Developed by the authors)

Considering jointly the weights obtained for the criteria in the first and second analyses, and determining an average value for the criteria that are common to both of them, it was possible to obtain the respective sums for demand and supply. Figure 1 illustrates the different attributes used in the first and second multicriteria analyses. The total sum of supply-related factors was 0.83, while that of demand was 0.85, indicating that factors of technology demand and supply exerted the same influence on decision-making about the most suitable choice for the implementation of TISCs.

The analysis corroborated the relevance of many aspects of contemporary location theory. On the one hand, the presence of demand sectors, private projects, research institutes, institutional environments, and qualified labor constitute a set of complex and specialized factors that are poorly explained by the market rationality which theoreticians use as the basis for this approach. On the other hand, it should be kept in mind that some factors considered important in classical theory, such as the industrial transformation value and the number of local units in a region, also appear to justify and influence the standards of technology center location.

An in-depth analysis of Figure 1 leads to the conclusion that the high weight of research insti­tutes and universities denotes not only the presence of institutes that favor joint collaborations with technology centers but also reflects the historical connection of these centers to local public uni­versities. Most technology centers in Brazil are located on universities campuses proper, and cases of academic spin-offs that are technically
and financially self-sustainable are rare. The high risks of acting in the market and the relatively low demand for technology services appear to be some of the obstacles that technology centers in Brazil face. Notwithstanding the high correlation that Wren and Storey (2002) identified between technology services and the number of employees in a company, demand appears to depend less on company size and more on the innovative char­acteristics and competitive conditions to which the sectors requiring these services are exposed. This fact would also support the high weight of this criterion in the TISC location process.

CONCLUSION

The existence oftechnology centers is an important source of motivation for regions and countries to overcome technology and innovation disadvan­tages. The location of these centers is a relevant theme within the sphere of Strategic Innovation Management. This field of research contributes to the innovation of companies and countries through technology management and decision-making support. The mobilization oftechnological knowl­edge and competencies in order to favor the best location choice is not a simple task and should be managed and planned strategically so as to encour­age the creation of innovative environments. In this context, the design of robust methodologies that augment the chances of success of enterprises is entirely justifiable. The analysis presented here attempted to advance in this direction by allowing for the identification and combined weighting of the elements pertinent to the location choice of such centers. In countries where the choice of a site for the implementation of enterprises of this type is strongly influenced by political criteria alien to technical and scientific logic, the application of research multicriteria methods of this nature offers unquestionable advantages.

Government incentives can be vital when it comes to establishing a TISC. Moreover, the role

of the State in innovation stimulates the creation of environments that are more favorable for the development of innovations, inducing the elabora­tion of innovation-related business strategies and decisions (Salerno & Kubota, 2008). However, although the public sector can act as an articula­tor of the outcomes of technological innovation, the technical inconsistency of its decisions may sometimes conceal particular orientations not aligned with the productive characteristics of a given region. Reality shows a plethora of situations in which regions and technology proj ects were the recipients of ventures that were justified more by political will than by an adequate technical basis. To a large extent, this fact is also the result of im­porting models that are firmly established in well industrialized countrie s but that contribute little to the development of the local industrial structure, since the competitive conditions of this structure differ from those of the regions from which these development models were imported.

Therefore, this location model can be very useful for decision makers seeking to dissociate the allocation of technological services from any less technical classification criterion. This model could undoubtedly represent an advance in reducing the technological imbalances to which industrialized regions are subject. Many imbal­ances in these regions have long been known; however, despite the mechanisms that have been applied to reverse them, further efforts to mitigate them are still required.

Studies involving the location of technology centers are strongly dependent on the identification of the determining factors of supply and demand for technology. Future research should focus on the identification of cause and effect relationships between the various factors that affect the sup­ply and demand for technological services. This type of research would undoubtedly contribute to improve analyses concerning the location of technology centers.

The themes of technological innovation, entrepreneurship, and organizing

About the Contributors

Farley S. Nobre (PhD, MSc, BSc) is Professor at the School of Management of Federal University of Parana, Brazil. His research interests include organizations, knowledge management systems, innova­tion and sustainability. …

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Jason G. Caudill Carson-Newman College, USA ABSTRACT The purpose of this chapter is to examine computer technology as a tool to support innovation and innovative processes. The primary problem that …

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