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Measuring innovation: clusters and competitiveness in Jalisco, Mexico.

INTRODUCTION

Since the 1990s, Porter's (Porter, 1998a,b,c) cluster definition as "a geographically proximate group of interconnected companies and associated institutions in a particular field, linked by commonalities and complementarities," remains a major source of innovation that drives regional and national competitiveness. It is more applied to developed countries, but what happens in emerging countries? Mexico currently has in several states various clusters, including textiles, clothing, logistics and food (Aguascalientes), automobile (Guanajuato, Coahuila), footwear (Guanajuato), wine (Baja California), etc. But, according to the report of the WEF (2011), Mexico is ranked 66/139 with an immediate drop of 6 positions and a loss of 14 places since 2006.

CONTEXTUAL REFERENCE

Innovation, and clusters are two concepts that today are considered by governments, educational institutions, and companies to gain competitiveness in the new global economy. In recent years, the state of Jalisco, Mexico, has installed and developed various clusters such as that of information and communications technologies (ICT, Guadalajara, and Ciudad Guzman), footwear (Guadalajara), furniture (Ocotlan), and, more recently, multimedia (Chapala), medical tourism, textiles, clothing, leather and jewelry (Guadalajara and its metropolitan area), however, there are no reports of the extent to which they are related within the cluster respective innovation and competitiveness; such as the Mexican Institute for Competitiveness (IMCO, 2010), showing loss of Jalisco three places mainly by efficient and effective government indicators. This raises the following research question (RQ):

What is the conceptual model of measuring innovation for competitiveness (IFC) ex post, based on the cluster design variables (CDV), which allows the managers of these companies to recognize, assess, decide, and implement actions that transform these organizations to be competitive?.

THEORETICAL FRAMEWORK

Innovation

This comes from the Latin innovate, meaning act or effect; to innovate, become new or renewing. It depends on creativity from the individuals involved. Some actors in the innovation clusters are individuals working on their own, but the absolute majority work are in an organizational context (Lagnevick et al., 2004). How can the organization stimulate and encourage creativity? Robinson and Stern (1997) define the creative company as "a company is a creative when its employees do something new and potentially useful without being directly shown or taught." Some tools to stimulate creativity are: alignment, self-initiated activity, unofficial activity, serendipity, diverse stimuli, and within-company communication. All of them require driving by innovation management and strategy.

Innovation Management

This refers to the importance of companies that constitute the cluster, to recognize and apply techniques to encourage creativity and innovation in a systematic way, based on creating added value to the company and the customer. Does the company use a tool to manage creativity and innovation? Is systematic? (Lagnevick et al.. 2004)

Innovation Strategy

There are three principal features of regional clusters that influence firm strategy (Enright, 1994). The first is that the resources and capabilities vital for firm to succeed can often be found within a region rather than within a single firm. The second is that the regional clusters often involve activities that are shared between firms within the cluster. The third feature is that a firm's choice of strategy can be influenced by the strategic interdependencies, rapid information flows, and the unique mixture of competition and cooperation often found in regional clusters.

Cluster

This is defined in the Concise Oxford Dictionary as a "group of similar things growing together." In a cluster, we'll find companies producing products and services for consumers, suppliers of specialized inputs, components, machinery, and services located up or downstream in the value added chain but also in related industries (Lagnevick et al., 2004). If we want to analyze the strength and potential of a cluster, we can use the analysis dimensions developed by Michel J. Enright (2000), as follows: Geographic Scope; Density; Activity Base; Depth; Growth Potential; Innovative Capability; Industrial Organization; Co-ordination Mechanisms.

Type of Knowledge

There are two principal types by codification: explicit and tacit (Nonaka and Takeuchi, 1995). Both kinds of knowledge are necessary in the innovation process, and this is one of the main reason why geography matters. According to Lundvall (1992) and Johnson and Lundvall (1994) we can distinguish a further four types of knowledge: know-what (knowledge of facts and transfer of codified knowledge); know-why (scientific knowledge about basic principles, rules and ideas); know-who (knowledge about specific and selective social relations); building of trust in relations. These four types of knowledge differ in regard to knowledge creation and knowledge transfer.

Policies for Development

The role of the government is important in innovation clusters, especially the ability to make anticipatory institutional changes at higher subsystem levels. When the government anticipates changes in the innovation landscape, it can undertake actions that lead to institutional change (Lagnevik et al., 2004). Cluster dynamics in creating innovation by government is also summarized by Porter (Porter, 1998c). Government should establish a stable and predictable economic environment; improve the ability, quality, and efficiency of general-purpose input and institution; establish overall rules and incentives governing competition that encourage productivity growth; facilitate cluster development and upgrading; develop and implement a positive, distinctive, and establish a long-term economic upgrading program which mobilizes relations between government-business-institutions and citizens. According to PECyTI (2008-2012), we have incentives for diffusion and intellectual protection; strengthening state and national systems of science and technology; increasing the scientific infrastructure, technological innovation, and physical and human regional/national resources.

Competitiveness

According OECD (2009), competitiveness is defined as "an multidimensional issue, with different perspectives about use" (qtd. In Ambastha & Momaya, 2004). Porter (1998c) notes that international competitiveness is described from a macroeconomic analysis of certain factors such as available and affordable labor, abundant natural resources budget deficit, exchange rates, interest rates, low unit labor costs, management practices, the competitive advantages derived of differencet, a positive trade balance, and a high and increasing industry productivity. Flanagan et al. (2005) affirms that the main objective derived from the competitiveness of a nation is human development, as well as improving quality of life of its inhabitants. Schuller and Lidbom (2009) affirm that competitiveness depends on market performance where an elevated efficiency could be considered the key to success. Kay (1993) described four factors: the capacity to innovate, key internal and external relationships referring to both strategic relations, reputation, and strategic assets. It is necessary to understand competitiveness not exclusively as productivity, but rather the ability of a company to design, produce and/or market products superior to those offered by competitors, considering the perceived value for customers (Vilanova et al., 2009).

To make the proposed conceptual model, it is necessary to discover the CDV. So we used the following works from the literature:

* Cluster and Innovation: Lagnevik et al. (2004); OECD (2009); Diaz, C. and Arechavala, R. (2007); Asheim et al. (2006); PECyTI (2008-2012)

* Competitiveness: Porter, M. (1998b,1998c, 2005); Sanchez, J. (2010); Sanchez, J. et al. (2011); Azua, J. (2008); IMCO (2010)

The next stage was to ask the order of importance of CDV to 6 managers (3 BO.-Back Office/3 fo.-Front Office) from ICT cluster, using Saaty's theorem (Analysis Hierarchical Process, AHP; Saaty, 1997). They were questioned about the importance of each of the CDV using the criteria of: firm (F), political environment (E), and institutions of higher education (A). The four main results, were: Strategy (S: 10.73 percent); Value Added (V: 9.91); Creativity (C: 8.55 percent) and National Government Policies (P: 7.58 percent).

The IFC levels proposed in this research are taken from previous work by Lugones et al. (2004) who, based on results of their study in product companies in Colombia, provide innovative activity classification: innovative companies in the strict sense; companies with minor innovations; potentially innovative firms; non-innovative companies.

RESEARCH QUESTIONS AND HYPOTHESES

To solve the RQ, we proposed:

Q1: What are the dimensions and indicators of CDV variables in the generation of IFC?

Q2: What is the relationship between CDV to the generation of IFC?

Q3: Which of the CDV and its dimension is the most influential in the creation of IFC?

The approach of general hypotheses to answer Q2:

H1: To greater value added (V), greater levels of IFC

H2: To greater government policies (P), greater levels of IFC

H3: To greater strategy, greater levels (S) of IFC

H4: To greater creativity, greater levels (C) IFC

HG: The relationship between cluster firms and the creation of the IFC, depends on the CDV: Value Added (V), National Government Policies (P), Strategy (S), and Creativity (C

METHODOLOGY

After determining the four CDVs: (S),(V),(C),(P) from the theoretical framework, this research was carried out by applying 44 questionnaires (11BO/11FO managers from software developers companies and idem to telecommunications firms) with 12 dimensions, 36 indicators, and 36 questions distributed in: 60 interviews, 20 telephone calls, and 50 e-mails to 22 companies in the ICT cluster. The reliability was tested by halves method with consulting experts from the ICT cluster (3BO/3FO).We evaluated the responses using the Likert scale in order to determine the degree of agreement or disagreement with each item. To probe the hypotheses, we analyzed the results using simple linear regression (SLR) and multiple linear regression (MLR). After this, it was necessary to code the information and then tabulate by capturing the data from each of the questionnaires that we considered as valid.

ANALYSIS OF RESULTS

In order to operationalize the CDV, methodological matrices were created as evidence of validity, based on theoretical framework to explain the origin of variables, dimensions, and indicators for measurement. Q1 was reached at 100 percent. On the other hand, it was initially proposed by the conceptual model detailed ex ante, generating the questionnaire design and performed a pilot reliability test by the method of the 2 halves with a Pearson (r) Correlation: 0.97812 and degree of adjustment ([r.sup.2]) of: 0.9567. Descriptive statistics were applied to the dependent variable IFC and independent variables: (V), (P), (S), (C) obtaining by simple linear regression (RLS) to CDV and IFC for bivariate behavior separately. The positive correlations of variables were: S (r = 0923) V (r = 0846) C (r = 0706), while (P) with inverse (r = -0.199). In testing hypotheses: H1, H2, H3, H4 were reached at 100 percent; only H2 was rejected; so Q2 was reached at 100 percent. By statistical inference by multiple linear regression (MLR) is determined by the behavior of independent CDV: strategy (S) and its dimension: implementation of the strategy as the most influential. So Q3 was reached at 100 percent. See Tables: 1 and 2.

In this way, we obtained the ex post conceptual model answering the RQ at 100 percent. See scheme 1

[ILLUSTRATION OMITTED]

Because (P) we also obtained 6 high dispersion indicators explaining the low level of IFC and hence competitiveness which were: business-institutes of higher education and government linkage; value-added product-service policies; incentives policies for the diffusion of intellectual property protection; incentive policies for creativity; and creation policies clusters at regional and national levels. Low levels of IFC (4,590 points; 58 percent of total design), with CVD participation were: S = 42 percent; V = 43 percent; C = 50 percent; P = 37 percent.

CONCLUSIONS AND RECOMMENDATIONS:

To determine the conceptual model of measuring innovation for competitiveness (ex post) based on the design of cluster variables (CDV), which allows the managers of these companies to recognize, evaluate, decide, and implement actions that transform such organizations to be competitive, the study concluded:

1. The remarkable inclination of the ICT cluster managers to generate IFC as more important to the business relationship; a situation that shows how waste of resources and opportunities for a relationship with the government policy environment and institutions of higher education to contribute to IFC.

2. The determination of CDV: S, V, C, P to be those with more references and in order of importance of experts in the ICT cluster.

3. The findings of CVD: S and its dimension, strategy implementation as the most influential to generate IFC.

4. Positive correlation for the generation of CVD IFC: S, V, C, although not with P; the latter circumstance as a result of low or no coordination of the innovation plans at the state and federal levels.

5. The ex post conceptual model and measurement of levels of IFC with a total of 12 dimensions, 36 indicators, 36 questions which are considered useful for its comprehensiveness and depth.

6.- The discovery of 5 dimensions (from CDV: P) urgent to correct: business-institutes of higher education and government linkage , value-added product-service policies, incentives policies for the diffusion of intellectual property protection, incentive policies for creativity, and creation policies clusters at regional and national levels .

7. Low levels of IFC (4,590 points; 58 percent of design), showing that the command staff, has average shares of IFC, but insufficient in the CVD participation: S = 42 percent ; V = 43 percent ;C = 50 percent; P = 37 percent.

REFERENCES

Ambastha, A., & Momaya, K. (2004). Competitiveness of firms: Review of theory, frameworks and models. Singapore Management Review, 26(1), 45-61.

Asheim, B., Cooke, P., & Martin , R. (2006). Clusters and regional development. Critical reflections and explorations. NY: Routledge.

Azua, J. (2008). Clusterizar y glokalizar la economia. Colombia: Ed. Oveja Negra.

Diario Oficial Mexico 16-Diciembre (2008). Programa Especial de Ciencia y Tecnologia e Innovacion (PECyTI) 2008-2012 (2008). Retrieved 10 July 2011, from http://www.foroconsultivo.org.mx/documentos/academicos/pecyt_2008_2012_dof_ 161208.pdf

Diaz, C., & Arechavala, R. (Coordinadores) (2007) Innovacion y desarrollo tecnologico. Politicas, acciones y casos. Mexico: Centro Universitario de Ciencias Economico Administrativas; Universidad de Guadalajara.

Enright, M. (1994). Organization and co-ordination of geographically concentrated industries. In D. Raff and N. Lamoreaux (Eds.), Co-ordination and information: Historical perspectives on the organization of enterprise. Chicago: Chicago University Press.

Enright, M. (2000), The globalization of competition and the localization of competitive advantage: Policies towards regional clustering. In N. Hood and S. Young (Eds.), The globalisation of multinational enterprise activity and economic development. London: Macmillan.

Flanagan, R., Jewell, C., Ericsson, S., & Henricsson, P. (2005). Measuring competitiveness in selected countries. UK: The University of Reading.

Instituto Mexicano dela Competitividad. Reporte IMCO 2010.R etrieved 11July2011, from http://contorno.org.mx/contorno/pdfs_reporte/oct10/PCG_Competitividad_Estatal_ IMCO.pdf

Johnson, B., & Lundvall, B-A. (1994). The learning economy. Journal of Industrial Studies, 1(2).

Kay, J. (1993). Foundations of corporate success. Oxford: Oxford University Press.

Lagnevik, M., SjOlholm, I., Lareke, A., & Osterberg, J. (2004). The dynamics of inovation cluster. A study of the food industry. A new horizons in the economics of innovation. Nothampnton, MA: Edgar Elgar Publishing.

Lundvall, B. (1992). National systems of innovation: Towards a theory of innovation and interactive learning. London: Pinter.

Lugones, G., Malaver, R., & Vargas, M. (2004). El desarrollo del manual de Bogota. Algunas contribuciones desde la experiencia Colombiana. Bogota: RICyT.

Nonaka, I., & Takeuchi, H. (1995). The knowledge creating company: How Japanese companies create the dynamics of innovation (pp.8-9). USA: Oxford University Press.

OECD. (2009), Clusters, innovation and entrepeneurship. Paris: OECD

Porter, M.E (1998a). Jon Azua panel, SMS Conference Orlando, Florida, Nov. 4

Porter, M.E. (1998b). On competition. NY: Harvard Business Review Books.

Porter, M.E (1998c). Clusters and the new economics of competition. Harvard Business Review, 76(6), 77-90.

Porter, M.E. (2005). Ventaja competitiva. Creacion y sostenimiento de un desempeno superior.4a. ed. Mexico: Compania Editorial Continental, S.A.

Robinson, A.G., & Stern, S. (1997). Corporate creativity: How innovation and improvement actually happen. San Francisco: Berret-Koehler Publishers, Inc.

Saaty, T. (1997). Decision making for leaders: The analytical hierarchy process for decisions in a complex world. Pittsburgh, PA: RWS.

Sanchez, J. (Coordinador) (2010). La Competitividad como estrategia en epoca de crisis. Mexico: Centro Universitario de Ciencias Economico Administrativas; Universidad de Guadalajara.

Sanchez, J., Gaytan, J., & Vazquez, M. (Coordinadores) (2011). La competitividad como factor de exito. Mexico: Centro Universitario de Ciencias Economico Administrativas; Universidad de Guadalajara.

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Juan Mejia Trejo, University of Guadalajara, Mexico

Jose Sanchez Gutierrez, University of Guadalajara, Mexico

Gabriel Fregoso Jasso, University of Guadalajara, Mexico
TABLE 1
ANOVA Dependent CDV: IFC

               Suma de            Media
Model         Cuadrados    Gl   Cuadratica        F         Sig.

1 Regresion   124816,644    1   124816,644       243,236   ,000(a)
  Residual     21552,356   42      513,151
  Total       146369,000   43
2 Regresion   143107,348    2    71553,674       899,452   ,000(b)
  Residual      3261,652   41       79,552
  Total       146369,000   43
3 Regresion   145595,863    3    48531,954      2510,911   ,000(c)
  Residual       773,137   40       19,328
  Total       146369,000   43
4 Regresion   146369,000    4    36592,250    2349340157   ,000(d)
                                             4615660,000
  Residual          ,000   39         ,000
  Total       146369,000   43

(a) Predictive Variables: (Constant), Strategy;

(b) Predictive Variables: (Constant), Strategy, Government Policies;

(c) Predictive Variables: (Constant), Strategy, Creativity, National
Government Policies;

(d) Predictive Variables: (Constant), Strategy, Value Added,
Creativity, National Government Policies;

(e.) Dependant Variable: IFC;

TABLE 2
ANOVA Independent CDV: Strategy

                Suma de              Media
Modelo         cuadrados    GL    cuadratica         F          Sig.

1 Regresion   105666,337     1    105666,337        344,413    ,000(a)
  Residual     12885,663    42       306,801
  Total       118552,000    43
2 Regresion   114682,486     2     57341,243        607,568    ,000(b)
  Residual      3869,514    41        94,378
  Total       118552,000    43
3 Regresion   117262,829     3     39087,610       1212,798    ,000(c)
  Residual      1289,171    40        32,229
  Total       118552,000    43
4 Regresion   117912,956     4     29478,239       1799,017    ,000(d)
  Residual       639,044    39        16,386
  Total       118552,000    43
5 Regresion   118552,000     5     23710,400     23303732539   ,000(e)
                                                  925620,000
  Residual          ,000    38          ,000
  Total       118552,000    43

(a) Predictive Variables: (Constant), Implementation;

(b) Predictive Variables: (Constant), Implementation, Planning;

(c) Predictive Variables: (Constant), Implementation, Planning,
Coordination Mechanisms, Industrial Organization;

(d) Predictive Variables: (Constant), Implementation, Planning,
Coordination Mechanisms, Industrial Organization;

(e) Dependent Variable (CDV): Strategy
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Author:Trejo, Juan Mejia; Gutierrez, Jose Sanchez; Jasso, Gabriel Fregoso
Publication:Competition Forum
Article Type:Report
Date:Jan 1, 2011
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