Dahlander, L. & Gann, D. (2010). How Open is Innovation? *Research Policy*, Vol. 39, No. 6,

Diaz-Diaz, N.L.; Aguiar-Diaz, I. & De Saa-Perez, P. (2006). Technological knowledge assets

Dilk, C.; Gleich, R.; Wald, A. & Motwani, J. (2008). State and development of innovation

Enkel, E., Gassmann, O. and Chesbrough, H. (2009), "Open R&D and Open Innovation: Exploring the Phenomenon", *R&D Management*, Vol. 39, No. 4, pp. 311-316. Gassmann, O.; Stahl, M. & Wolff, T. (2004). The Cross Industry Innovation Process: Opening

networks. Evidence from the European vehicle sector. *Management Decision*, Vol. 46,

up R&D in the Automotive Industry. *Proceedings of the R&D Management Conference*

in industrial firms. *R&D Management*, Vol. 36, No.2, pp. 189-203.

Autoblog. Available from: http://www.autoblog.it/post/28620/mitsubishi-

Conceptual Framework and Application in the Automotive Industry. *Conference*

development- systematic identification of lead users harnessing interactive and collaborative online-tools. *International Journal of Innovation Management*, Vol. 12,

*turbochargers.* Stuggart: Bosch Presse. Available from: http://www.bosch-

of quantitative tools of data analysis should be also recommended.

contro-pininfarina-in-tribunale-per-la-colt-czc.

*Proceedings R&D*, Manchester, 30 June – 2 July.

presse.de/TBWebDB/en-US/PressText.cfm?id=3455.

*Technology*. Boston: Harvard Business School Press.

perspective. *R&D Management*, Vol. 28, pp. 199-212.

*(Lissabon).* 12 July 2004. Oxford: Blackwell.

Gladbach: FHDW Center of Automotive.

Boston: Harvard Business School Press.

**6. References**

No. 3, pp. 419-458.

No. 3, pp. 35-41.

pp. 699-709.

No. 5, pp. 691-701.

the final product - the car) and technology intensity seem to force companies to search for external sources of knowledge. Main declared goals for collaboration by the respondents are the enlargement of company's competence base, the stimulation of creativity and capability of generating new ideas, the reduction and sharing of risks related to innovation activities and costs of innovation process.

The second paper objective was understanding how Open Innovation approaches could be set-up. In this regard, the typology of partners has been firstly investigated. As result, the partner variety is considerable high. Company A probably presents the highest openness degree by collaborating with the highest number of different partners. In particular, the company relies on the collaborations with a lot of suppliers, customers, firms operating in other industries (cross industry innovation, i.e. CII) and universities' research centres.

With regard to the other respondents, they cooperate with a minor number of partners. As suppliers of automotive companies, Pininfarina and Bosch keep the main objective of satisfying the customer that, in this case, is represented by the automotive company itself. For this reason, they establish the highest number of agreements with them. However, Pininfarina deeply relies on CII, as its prototypes are aimed at showing the highest concentration of technology in order to impress automotive companies and induce them in introducing those innovations in their vehicles. In particular, Pininfarina retains that collaborating with partners from other industries is a daily activity, rather than a rare experience. To be noted is the almost total absence of competitors among the possible partners identified by the respondents. This is in contrast with the cited findings of Ili et al. (2010), who show competitors as the second source of ideas (after customers) for automotive companies. Probably the respondents have not yet overcome the strong aversion to collaborate with competitors as they perceive a very high risk of imitation.

However, the management of intellectual property is rapidly getting better and, although an active exploitation of IP (i.e. outbound open innovation) is not yet adopted, it is perhaps only a matter of time. With adequate protection mechanisms of IP, also collaborations with competitors will not seem so risky.

As concerns the openness of the innovation process phases, all the respondents agree with the fact that all the stages can be opened according to the project and the kind of agreement among partners. In general, the phase of idea generation always takes information from external sources, ranging from the analysis of customer needs to the adaptation of solutions from other industries. The other phases can be conducted internally, as well as in cooperation with other partners: for example, Company A specifies that it tends to exclude partners when phase activities are dealing with perceived distinctive functionalities.

Also the adopted organizational modes are various: they range from contractual agreements to more integrated forms as alliances and joint ventures, these chosen only for high-relevant long-term projects because of the high amount of required resources.

Finally, both hierarchical and participative kinds of governance are used by the respondents, with the tendency towards stronger delegation when partner competencies are highly distinctive, not standardized and therefore difficult to be replaced.

Lastly, despite the identification of some obstacles, partnerships are described as successful by proving Open Innovation is an appropriate approach to be adopted in the automotive industry.

Our work obviously has severe limitations because it is focus on a limited set of companies. The fact that the investigated companies have different roles in the same value chain allowed at least to provide a broader view on the industry. However, future researches could deal with validating these findings by interviewing other actors of the same dimension of the respondents. In this way, it would be possible to provide more significant evidence on the industry. Moreover, similar analysis could be conducted on small-medium companies operating in the automotive industry, as well as on emerging automotive companies in the Eastern countries. More extended investigations, i.e. surveys, with the use of quantitative tools of data analysis should be also recommended.

#### **6. References**

234 Management of Technological Innovation in Developing and Developed Countries

the final product - the car) and technology intensity seem to force companies to search for external sources of knowledge. Main declared goals for collaboration by the respondents are the enlargement of company's competence base, the stimulation of creativity and capability of generating new ideas, the reduction and sharing of risks related to innovation activities

The second paper objective was understanding how Open Innovation approaches could be set-up. In this regard, the typology of partners has been firstly investigated. As result, the partner variety is considerable high. Company A probably presents the highest openness degree by collaborating with the highest number of different partners. In particular, the company relies on the collaborations with a lot of suppliers, customers, firms operating in other industries (cross industry innovation, i.e. CII) and universities' research centres.

With regard to the other respondents, they cooperate with a minor number of partners. As suppliers of automotive companies, Pininfarina and Bosch keep the main objective of satisfying the customer that, in this case, is represented by the automotive company itself. For this reason, they establish the highest number of agreements with them. However, Pininfarina deeply relies on CII, as its prototypes are aimed at showing the highest concentration of technology in order to impress automotive companies and induce them in introducing those innovations in their vehicles. In particular, Pininfarina retains that collaborating with partners from other industries is a daily activity, rather than a rare experience. To be noted is the almost total absence of competitors among the possible partners identified by the respondents. This is in contrast with the cited findings of Ili et al. (2010), who show competitors as the second source of ideas (after customers) for automotive companies. Probably the respondents have not yet overcome the strong aversion to

However, the management of intellectual property is rapidly getting better and, although an active exploitation of IP (i.e. outbound open innovation) is not yet adopted, it is perhaps only a matter of time. With adequate protection mechanisms of IP, also collaborations with

As concerns the openness of the innovation process phases, all the respondents agree with the fact that all the stages can be opened according to the project and the kind of agreement among partners. In general, the phase of idea generation always takes information from external sources, ranging from the analysis of customer needs to the adaptation of solutions from other industries. The other phases can be conducted internally, as well as in cooperation with other partners: for example, Company A specifies that it tends to exclude

Also the adopted organizational modes are various: they range from contractual agreements to more integrated forms as alliances and joint ventures, these chosen only for high-relevant

Finally, both hierarchical and participative kinds of governance are used by the respondents, with the tendency towards stronger delegation when partner competencies are

Lastly, despite the identification of some obstacles, partnerships are described as successful by proving Open Innovation is an appropriate approach to be adopted in the automotive industry.

partners when phase activities are dealing with perceived distinctive functionalities.

long-term projects because of the high amount of required resources.

highly distinctive, not standardized and therefore difficult to be replaced.

collaborate with competitors as they perceive a very high risk of imitation.

and costs of innovation process.

competitors will not seem so risky.

**11**

*Colombia*

**The Impact of Company Relationship**

*Industrial Engineering Department, National University of Colombia-Manizales*

In today's competitive environment, the activities of a business cannot be imagined in an isolated manner, nor can they be exclusively associated with commercial-type relations (purchasing and sales of goods and services) as they were in the 1980s and 1990s (Buhman, et al., 2005; Más Ruíz, 2000). We are currently living in a networking society where strong interdependencies among actors are created through networks and inter-organizational alliances (Tikkanen & Parvinen, 2006), where today's companies are network-centric enterprises (Buhman et al., 2005), and where the external environment affects company behavior. At the same time, the companies, their culture and their learning processes influence the external stakeholders with whom they have relations (Minguzzi & Passaro, 2000). Therefore, companies must take into account not only their stage of development but also the development of the industry, in order to maximize their learning capability (Benson-Rea & Wilson, 2003) based on their relations with the other stakeholders located in the same region or country, and thus contribute to the development of competitive advantages at both business and regional levels (Bell & Albu, 1999; Carbonara, 2002; Dohse

Section 1 presents some common proposals and perspectives based on the study of intercompany relations and of company-institution relations using the following theoretical approaches: agglomeration economics, clusters and industrial districts, and networking. Section 2 addresses theoretical aspects regarding R&D activities and innovation in a business network environment and proposes the hypotheses for this article. Section 3 explains the research methodology; based on the literature on the topic, it indicates the main methodologies employed in this article and defines analysis variables. Section 4 presents the general theoretical and empirical findings. And finally suggests future lines of research.

**2. The study of inter-company relations and of company-institution relations**  Inter-company relations as well as company-institution relations (public and private institutions) are determining factors for business competitiveness and contribute to the

**1. Introduction**

& Soltwedel, 2006; Feldman et al., 2005).

**and Institution Technology on**

*Department of Business Administration, Manizales University*

**R&D Activity and Innovation**

Fredy Becerra-Rodríguez

## **The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation**

Fredy Becerra-Rodríguez

*Industrial Engineering Department, National University of Colombia-Manizales Department of Business Administration, Manizales University Colombia*

#### **1. Introduction**

236 Management of Technological Innovation in Developing and Developed Countries

Gassmann, O. & Henkel, E. (2004). Towards a theory of open innovation: three core process

Gassmann, O. (2006). Opening up the innovation process: towards an agenda. *R&D*

Heneric, O.; Licht, G. & Sofka, W. (2005). *Europe's Automotive Industry on the Move*

Keupp, M.M. and Gassmann, O. (2009). Determinants and Archetype Users of Open

Herrler, M. (2001). *User-Interface-Gestaltung: Cockpit der Zukunft – kunftige Richtungen in der Anzeigen- und Bedienelement-Gestaltung im Automobil*. Munchen: BMW AG. Ili, S., Alberts, A. and Miller, S. (2010). Open Innovation in the automotive industry*. R&D*

Laursen, K. and Salter, A. (2004), Searching High and Low: What Type of Firms Use

Lichtenthaler, U. (2008), Open Innovation in Practice: an Analysis of Strategic Approaches to

McKinsey & Company (2003). *HAWK 2015 – Knowledge-Based Changes in the Automotive Value*

Pisano, G. P. and Verganti, R. (2008). Which Kind of Collaboration Is Right for You? *Harvard*

Raasch C, C.; Herstatt, C. & Lock, P. (2008). The dynamics of user innovation: Drivers and

Saunders, M.; Lewis, P. & Thornhill, A. (2007). *Research methods for business students.* 4th ed.

Terporten, M. (1999). *Wettbewerb in der Automobilindustrie*. In: Heneric, O.; Licht, G. & Sofka,

van de Vrande, V., Lemmens, C. & Vanhaverbeke, W. (2006). Choosing Governance Modes for External Technology Sourcing, *R&D Management*, Vol. 36, No. 3, pp. 347-363.

Womack, J. P. & Jones, D. T. (1991). *Die zweite Revolution in der Automobilindustrie.*

von Hippel, E. (1988). *The Sources of Innovation*, Oxford University Press: New York.

*impediments* of innovation activities. *International Journal of Innovation Management*,

W. (2005). *Europe's Automotive Industry on the Move Competitiveness in a Changing*

Konsequenzen aus der weltweiten Studie aus dem Massachusetts Institute of Technology. In: Heneric, O.; Licht, G. & Sofka, W. (2005). *Europe's Automotive Industry on the Move Competitiveness in a Changing world*. Mannheim, Germany:

Pininfarina (2010a). *I Clienti, Auto.* Cambiano, Italy: Pininfarina. Available from: http://www.pininfarina.it/index/servizi/clienti/clientiAuto . Pininfarina (2010b). *Magazine.* Cambiano, Italy: Pininfarina. Available from:

Roland Berger & Partners (2000). *Automotives Supplier Trend Study.* Detroit.

*world*. Mannheim, Germany: Physica-Verlag Heidelberg.

http://sintesi.pininfarina.com/docs/magazine.pdf.

*Business Review*, December, pp. 1-9.

Vol.12, No. 3, pp. 377-398.

Harlow: Pearson Education.

Physica-Verlag Heidelberg.

Innovation. *R&D Management*, Vol. 39, No. 4, pp. 331-341.

*Management*, Vol. 36, No. 3, pp. 223-228.

*Management*, 40(3), pp. 246-255.

Heidelberg.

1, pp. 148-157.

*Chain.* Frankfurt.

archetypes. *Proceedings of the R&D Management Conference*, Lisbon, Portugal, July 6–9.

*Competitiveness in a Changing world*. Mannheim, Germany: Physica-Verlag

Universities as a Source of Innovation? *Research Policy*, Vol. 33, No. 8, pp. 1201-1215.

Technology Transactions. *IEEE Transactions on Engineering Management*, Vol. 55, No.

In today's competitive environment, the activities of a business cannot be imagined in an isolated manner, nor can they be exclusively associated with commercial-type relations (purchasing and sales of goods and services) as they were in the 1980s and 1990s (Buhman, et al., 2005; Más Ruíz, 2000). We are currently living in a networking society where strong interdependencies among actors are created through networks and inter-organizational alliances (Tikkanen & Parvinen, 2006), where today's companies are network-centric enterprises (Buhman et al., 2005), and where the external environment affects company behavior. At the same time, the companies, their culture and their learning processes influence the external stakeholders with whom they have relations (Minguzzi & Passaro, 2000). Therefore, companies must take into account not only their stage of development but also the development of the industry, in order to maximize their learning capability (Benson-Rea & Wilson, 2003) based on their relations with the other stakeholders located in the same region or country, and thus contribute to the development of competitive advantages at both business and regional levels (Bell & Albu, 1999; Carbonara, 2002; Dohse & Soltwedel, 2006; Feldman et al., 2005).

Section 1 presents some common proposals and perspectives based on the study of intercompany relations and of company-institution relations using the following theoretical approaches: agglomeration economics, clusters and industrial districts, and networking. Section 2 addresses theoretical aspects regarding R&D activities and innovation in a business network environment and proposes the hypotheses for this article. Section 3 explains the research methodology; based on the literature on the topic, it indicates the main methodologies employed in this article and defines analysis variables. Section 4 presents the general theoretical and empirical findings. And finally suggests future lines of research.

#### **2. The study of inter-company relations and of company-institution relations**

Inter-company relations as well as company-institution relations (public and private institutions) are determining factors for business competitiveness and contribute to the

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 239

inventing a new method of doing something, upon entering a new market or finding a new source of provisioning, or upon creating a new organization (Schumpeter, 1997), which basically implies understanding innovation as a result. Along the same lines, Damanpour (1987) differentiates technological innovations and administrative innovations, and classifies innovations as radical innovations and as incremental innovations (Gopalakrishnan &

The Oslo Manual states that innovation "is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method, in business practices, workplace organization or external relations," (OECD/EUROSTAT, 2005, pg. 46). This definition comprises the different ideas discussed in the literature on the topic and highlights the external relations of a company, meaning that it alludes to the relational capital (Capello, 2002; Capello & Faggian, 2005) that can be found in business networks, which is the object of this study although the above is based on a

Innovation as a process, not as a result, implies understanding the activities that take place in order for new ideas, objects and practices to be created, developed or reinvented (Slappendel, 1996). In that sense, the literature on the topic refers to stages that occur from the time when an invention is created to the time when it is commercialized. Such stages include research (basic and applied), R&D, the development of prototypes and models, the acquisition of technology, and some project engineering stages (OECD, 2002, OECD/EUROSTAT, 2005; Rammer & Schmiele, 2009). Nevertheless, the stages are recursive rather than sequential, in which knowledge is developed, communicated and transferred (Robertson et al., 1997 as cited in Edwards, 2003), resulting in an "interactive process" that is common in the field of innovation and that has been used to describe intra-company and inter-company innovation activities (Rothwell et al., 1990 as cited in Edwards, 2003). Innovation activities that use that process are the main input for obtaining innovations (results) and they are also essential in building

Damanpour, 1997), according to the degree of innovation.

knowledge and technological capabilities in the company.

in R&D projects are also included in this category**2**.

conduct basic or applied research (or if they do, it is solely residual). 2 (See OECD, 2002, pg. 30-33).

**4. Innovation activities (R&D and TKT) as determinants of business**

According to the Oslo Manual, "innovation activities are all those scientific, technological, organizational, financial and commercial steps, including investment in new knowledge, which actually lead to, or are intended to lead to, the implementation of innovations," (OECD/EUROSTAT, 2005, pg. 91), which implicitly includes R&D. According to the Frascati Manual, R&D includes basic research, applied research, and experimental development**1**. Other activities that are not technically R&D activities but that are carried out

1 This article focuses on experimental development because the characteristics proper to the Colombian economy and more specifically to the Provincial Department of Caldas, mostly comprising traditional manufacturing industry micro-companies and small and medium-sized companies (SME), do not allow assuming that the companies on their own or in association with other companies or institutions

perspective of innovation as a result.

**innovation and R&D activities**

socioeconomical development of the area where they are located. They especially influence the companies' innovation and R&D capabilities. The study of these relations is based on the concepts of agglomeration economics, clusters and industrial districts, and networking.

These different concepts have theoretical aspects in common, such as:

And from a practical view, the empirical research conducted in the context of the above concepts has the following perspectives well worth mentioning.

All of the aspects mentioned above prove the importance of inter-company relations as well as of company-institution relations because of their positive influence not only on the economic and social development of specific areas but also on each company's development, especially in the development of technological capabilities for innovation. Likewise, a conclusion to be drawn is that the studies on inter-company relations are based on the idea of networks, which may allow inferring that "the business network" (Becerra, 2008) is the common unit of analysis, independently of the proposals presented in each approach.

#### **3. Innovation as a process: Innovation activities**

The idea of innovation stems from Schumpeter's ground-breaking work that proposes that innovation is achieved upon introducing a new product or a modified product, upon inventing a new method of doing something, upon entering a new market or finding a new source of provisioning, or upon creating a new organization (Schumpeter, 1997), which basically implies understanding innovation as a result. Along the same lines, Damanpour (1987) differentiates technological innovations and administrative innovations, and classifies innovations as radical innovations and as incremental innovations (Gopalakrishnan & Damanpour, 1997), according to the degree of innovation.

The Oslo Manual states that innovation "is the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method, in business practices, workplace organization or external relations," (OECD/EUROSTAT, 2005, pg. 46). This definition comprises the different ideas discussed in the literature on the topic and highlights the external relations of a company, meaning that it alludes to the relational capital (Capello, 2002; Capello & Faggian, 2005) that can be found in business networks, which is the object of this study although the above is based on a perspective of innovation as a result.

Innovation as a process, not as a result, implies understanding the activities that take place in order for new ideas, objects and practices to be created, developed or reinvented (Slappendel, 1996). In that sense, the literature on the topic refers to stages that occur from the time when an invention is created to the time when it is commercialized. Such stages include research (basic and applied), R&D, the development of prototypes and models, the acquisition of technology, and some project engineering stages (OECD, 2002, OECD/EUROSTAT, 2005; Rammer & Schmiele, 2009). Nevertheless, the stages are recursive rather than sequential, in which knowledge is developed, communicated and transferred (Robertson et al., 1997 as cited in Edwards, 2003), resulting in an "interactive process" that is common in the field of innovation and that has been used to describe intra-company and inter-company innovation activities (Rothwell et al., 1990 as cited in Edwards, 2003). Innovation activities that use that process are the main input for obtaining innovations (results) and they are also essential in building knowledge and technological capabilities in the company.

#### **4. Innovation activities (R&D and TKT) as determinants of business innovation and R&D activities**

According to the Oslo Manual, "innovation activities are all those scientific, technological, organizational, financial and commercial steps, including investment in new knowledge, which actually lead to, or are intended to lead to, the implementation of innovations," (OECD/EUROSTAT, 2005, pg. 91), which implicitly includes R&D. According to the Frascati Manual, R&D includes basic research, applied research, and experimental development**1**. Other activities that are not technically R&D activities but that are carried out in R&D projects are also included in this category**2**.

238 Management of Technological Innovation in Developing and Developed Countries

socioeconomical development of the area where they are located. They especially influence the companies' innovation and R&D capabilities. The study of these relations is based on the concepts of agglomeration economics, clusters and industrial districts, and networking.

 The existence of a company cluster in a clearly delimited territory (concentrated in one space) through which the companies take advantage of externalities resulting from their proximity (Alonso-Villar et al., 2004; Bell, 2005; Feser & Bergman, 2000; Mella et al.,

 High levels of specialized goods and services, knowledge flows and innovations, and intense, constant technological change (Antonelli, 2006; Beesley, 2004; Callois, 2008; Carbonara, 2002; Groenewegen & Van Der Steen, 2006; Hagedoorn & Duysters, 2002;

 Building and sustaining vertical and horizontal relations of inter-company trust and of trust between companies and other stakeholders in and outside the territory (Brenner & Greif, 2006; Dohse & Soltwedel, 2006; Eraydin & Armatli-Köroglu, 2005; Hotz-Hart,

And from a practical view, the empirical research conducted in the context of the above

 Local business networks and their role in the development of knowledge, of innovation processes, and in innovation results (Baptista, 1996; Baptista & Swam, 1998; Beaudry & Breschi, 2003; Beesley, 2004; Bell, 2005; Brenner & Greif, 2006; Hagedoorn & Duysters, 2002; Hervás & Dalmau, 2006; Muscio, 2006; Wolfe & Gertler, 2002; Yogel et al., 2000; Zhang, 2007) The importance of business networks in business strategy, productivity and competitiveness, and business infrastructure (Carbonara, 2002; Carrie, 1999; Feldman et

 Business networks and their incidence *vis à vis* the job market, human resources and company development (Blasio & Di Addario, 2005; Hervás & Dalmau, 2006; Hu et al.,

 Business networks and public policies for regional economic development at State, regional, city or location level (Altengurg & Meyer-Stamer, 1999; Dohse & Soltwedel, 2006, Gibb, 2006; Jensen, 1996; Koch et al., 2006; Lai et al., 2005; McDonald et al., 2006;

All of the aspects mentioned above prove the importance of inter-company relations as well as of company-institution relations because of their positive influence not only on the economic and social development of specific areas but also on each company's development, especially in the development of technological capabilities for innovation. Likewise, a conclusion to be drawn is that the studies on inter-company relations are based on the idea of networks, which may allow inferring that "the business network" (Becerra, 2008) is the common unit of

The idea of innovation stems from Schumpeter's ground-breaking work that proposes that innovation is achieved upon introducing a new product or a modified product, upon

These different concepts have theoretical aspects in common, such as:

2002; McCann, 1995; Tracey & Clark, 2003; Walker et al., 1997)

concepts has the following perspectives well worth mentioning.

al., 2005; Hervás et al., 2007; Lechner & Dowling, 2003)

2005; Power & Lundmark, 2004; Pöyhönen & Smedlund, 2004)

Oyelaran-Oyeyinka & Lal, 2006; Viladecans-Marsal, 2004).

analysis, independently of the proposals presented in each approach.

**3. Innovation as a process: Innovation activities**

Hervás & Dalmau, 2006; OECD, 1999 a, 1999b)

2007; Nassimbeni, 2003)

<sup>1</sup> This article focuses on experimental development because the characteristics proper to the Colombian economy and more specifically to the Provincial Department of Caldas, mostly comprising traditional manufacturing industry micro-companies and small and medium-sized companies (SME), do not allow assuming that the companies on their own or in association with other companies or institutions conduct basic or applied research (or if they do, it is solely residual). 2 (See OECD, 2002, pg. 30-33).

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 241

*H2: In a localized business network environment, as concerns R&D activities inter-company relations and company-institution relations have a positive impact on each company's R&D activities*

In business network exchanges, TKT is fundamental to disseminating and absorbing innovations (Banyte & Salickaite, 2008); therefore, it is fundamental to a company's innovation performance (Arvanitis et al., 2007; Capello, 2002; Evangelista et al, 1997; Lin & Chen, 2006). Furthermore, it contributes to each company increasing its R&D activities, creating its own innovations, and decreasing its dependence on ideas and technologies developed by others (Rammer & Schmiele, 2007). The literature on TKT refers to formal and informal exchanges (Allen et al., 2007) that take place among the personnel of the companies and institutions that are part of the network. That aspect constitutes relational capital

TKT has been studied as technology acquisition (Bin, 2008) or as the absorptive capability for technology (Fabrizio, 2009). Nevertheless, it is worth considering that TKT is not always the product of strictly commercial transactions; TKT occurs by employing diverse strategies in the form of joint research projects (or technology development projects), joint training projects (Arvanitis et al., 2007) or mutual support contracts for innovation in companies where public institutions play an important role. TKT also occurs in the joint use of technical infrastructure and laboratories, among others (Arvanitis et al., 2007; Filatochev et al., 2003). Some empirical papers illustrate how inter-company TKT in localized networks influences company innovation and R&D activities. Capello (2002) found that scientific knowledge spillovers generated by universities and R&D centers influence business innovation activities. Small firms in non-urban areas use that knowledge in particular to the best advantage given such companies' production specialization. Lin & Chen (2006) evidenced that industry network knowledge integration has a positive effect on the process of

By studying two Spanish clusters, Martínez & Céspedes (2006) found that companies used their relation with regional associations to obtain knowledge that contributes to their capabilities, even though there was no significant relation with innovation per se. Arvanitis et al. (2007) found that business innovation development indicators had a positive relation with TKT although at different levels of significance; process innovation and product innovation presented the best results in terms of the relation considering public research organizations. Knudsen (2007) found that inter-company relations had an effect on the success of new product development in the form of more frequent customer participation. He also found that companies had a tendency to make alliances with other companies in the same industrial sector and mentioned the danger that that involves. Indeed, he argued that the knowledge contributed was similar; therefore it hindered the possibility of developing radical product innovations. He further found that customer, university and competitor participation had a significant negative influence on innovation development, which he defined as an apparent paradox3. Finally, he concluded that the set of supplementary and

3 There are two explanations for this apparent paradox: 1) average customers are incapable of articulating their needs regarding advanced technology products and 2) average customers are

incapable of conceiving ideas beyond their own experience (Knudsen, 2007, pg.117).

*(input for innovation).*

(Capello, 2002; Capello & Faggian, 2005).

developing new products.

Among the activities related to knowledge flow and technology (Oslo Manual, 2002 and Bogotá Manual, 2001) that are carried out in a business network environment, the activities related to machinery and equipment, specialized software, technical and technological information, and the dissemination of R&D and innovation results (Arvanitis et al., 2007) are included under technology and knowledge transfer (TKT) as determinants of the innovation and R&D proper to a company.

In summary, this article analyses the innovation activities (R&D and TKT) that stem from business network links and that are incentives for company R&D as well as for business innovation (product, process, and administrative innovations).

The influence of business network R&D on company innovation and R&D activities has been proposed in the empirical literature on the topic. By observing the systemic interactions that can favor or hinder innovation activities in the four regional innovation systems in Italy, Evangelista et al. (2002) found that there were differences in the level of importance given to R&D activities and to non-R&D activities at a company level. By studying the impact of relational capital on innovation in urban areas and non-urban areas and in the industrial districts of the Emilia Romagna, Capello (2002) found that relational capital had an impact on company innovation activities, which mostly benefited the large production companies in the district. Capello also found that relational capital had a positive impact on product innovation in small companies that operate in specialized sectors as they achieve synergies and cooperation with one another. The above was later reinforced by Capello & Faggian (2005) who established a positive relation between relational capital and company innovation activity.

Taking the R&D expenses of a company in a network context as a reference, Filatochev et al. (2003) found a positive significant relation between the intensity of R&D activities in firms and the industry concentration. Capello & Faggian (2005) affirm that the "physical proximity" between firms plays a crucial role in the increase of a firm's innovation capability, especially thanks to knowledge spillovers. Cassiman & Veugelers (2006) suggest that internal R&D activities and knowledge acquisition and innovation activities are complementary; regarding basic R&D, they suggest the importance of universities and research centers as sources of information for innovation processes.

Analyzing the determinants of R&D cooperation between innovative firms (foreign and domestic) and universities and public knowledge institutions, Van Beers et al. (2008) studied small economies (The Netherlands and Finland) and found that in The Netherlands foreign firms were less involved in cooperating with public knowledge institutions than domestic firms were whereas the opposite occurred in Finland. Both countries proved that spillovers have a positive effect on the probability of cooperating with universities and public knowledge institutions. That aspect was highlighted as having a positive impact on company innovation and R&D.

Taking the above into account, the following hypotheses are proposed:

*H1: In a localized business network environment, as concerns R&D activities inter-company relations and company-institution relations have a positive impact on each company's innovation (product, process and administrative innovation).*

Among the activities related to knowledge flow and technology (Oslo Manual, 2002 and Bogotá Manual, 2001) that are carried out in a business network environment, the activities related to machinery and equipment, specialized software, technical and technological information, and the dissemination of R&D and innovation results (Arvanitis et al., 2007) are included under technology and knowledge transfer (TKT) as determinants of the innovation

In summary, this article analyses the innovation activities (R&D and TKT) that stem from business network links and that are incentives for company R&D as well as for business

The influence of business network R&D on company innovation and R&D activities has been proposed in the empirical literature on the topic. By observing the systemic interactions that can favor or hinder innovation activities in the four regional innovation systems in Italy, Evangelista et al. (2002) found that there were differences in the level of importance given to R&D activities and to non-R&D activities at a company level. By studying the impact of relational capital on innovation in urban areas and non-urban areas and in the industrial districts of the Emilia Romagna, Capello (2002) found that relational capital had an impact on company innovation activities, which mostly benefited the large production companies in the district. Capello also found that relational capital had a positive impact on product innovation in small companies that operate in specialized sectors as they achieve synergies and cooperation with one another. The above was later reinforced by Capello & Faggian (2005) who established a positive relation between relational capital

Taking the R&D expenses of a company in a network context as a reference, Filatochev et al. (2003) found a positive significant relation between the intensity of R&D activities in firms and the industry concentration. Capello & Faggian (2005) affirm that the "physical proximity" between firms plays a crucial role in the increase of a firm's innovation capability, especially thanks to knowledge spillovers. Cassiman & Veugelers (2006) suggest that internal R&D activities and knowledge acquisition and innovation activities are complementary; regarding basic R&D, they suggest the importance of universities and

Analyzing the determinants of R&D cooperation between innovative firms (foreign and domestic) and universities and public knowledge institutions, Van Beers et al. (2008) studied small economies (The Netherlands and Finland) and found that in The Netherlands foreign firms were less involved in cooperating with public knowledge institutions than domestic firms were whereas the opposite occurred in Finland. Both countries proved that spillovers have a positive effect on the probability of cooperating with universities and public knowledge institutions. That aspect was highlighted as having a positive impact on

*H1: In a localized business network environment, as concerns R&D activities inter-company relations and company-institution relations have a positive impact on each company's innovation*

innovation (product, process, and administrative innovations).

research centers as sources of information for innovation processes.

Taking the above into account, the following hypotheses are proposed:

and R&D proper to a company.

and company innovation activity.

company innovation and R&D.

*(product, process and administrative innovation).*

*H2: In a localized business network environment, as concerns R&D activities inter-company relations and company-institution relations have a positive impact on each company's R&D activities (input for innovation).*

In business network exchanges, TKT is fundamental to disseminating and absorbing innovations (Banyte & Salickaite, 2008); therefore, it is fundamental to a company's innovation performance (Arvanitis et al., 2007; Capello, 2002; Evangelista et al, 1997; Lin & Chen, 2006). Furthermore, it contributes to each company increasing its R&D activities, creating its own innovations, and decreasing its dependence on ideas and technologies developed by others (Rammer & Schmiele, 2007). The literature on TKT refers to formal and informal exchanges (Allen et al., 2007) that take place among the personnel of the companies and institutions that are part of the network. That aspect constitutes relational capital (Capello, 2002; Capello & Faggian, 2005).

TKT has been studied as technology acquisition (Bin, 2008) or as the absorptive capability for technology (Fabrizio, 2009). Nevertheless, it is worth considering that TKT is not always the product of strictly commercial transactions; TKT occurs by employing diverse strategies in the form of joint research projects (or technology development projects), joint training projects (Arvanitis et al., 2007) or mutual support contracts for innovation in companies where public institutions play an important role. TKT also occurs in the joint use of technical infrastructure and laboratories, among others (Arvanitis et al., 2007; Filatochev et al., 2003).

Some empirical papers illustrate how inter-company TKT in localized networks influences company innovation and R&D activities. Capello (2002) found that scientific knowledge spillovers generated by universities and R&D centers influence business innovation activities. Small firms in non-urban areas use that knowledge in particular to the best advantage given such companies' production specialization. Lin & Chen (2006) evidenced that industry network knowledge integration has a positive effect on the process of developing new products.

By studying two Spanish clusters, Martínez & Céspedes (2006) found that companies used their relation with regional associations to obtain knowledge that contributes to their capabilities, even though there was no significant relation with innovation per se. Arvanitis et al. (2007) found that business innovation development indicators had a positive relation with TKT although at different levels of significance; process innovation and product innovation presented the best results in terms of the relation considering public research organizations. Knudsen (2007) found that inter-company relations had an effect on the success of new product development in the form of more frequent customer participation. He also found that companies had a tendency to make alliances with other companies in the same industrial sector and mentioned the danger that that involves. Indeed, he argued that the knowledge contributed was similar; therefore it hindered the possibility of developing radical product innovations. He further found that customer, university and competitor participation had a significant negative influence on innovation development, which he defined as an apparent paradox3. Finally, he concluded that the set of supplementary and

<sup>3</sup> There are two explanations for this apparent paradox: 1) average customers are incapable of articulating their needs regarding advanced technology products and 2) average customers are incapable of conceiving ideas beyond their own experience (Knudsen, 2007, pg.117).

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 243

in this article combines case studies with quantitative techniques to understand gathered

**CLUSTER AND INDUSTRIAL DISTRICTS**

**# AUTHORS # AUTHORS # AUTHORS**

**2006**

**2005; Nassimbeni & Sartor, 2005; Perdomo & Malaver,**

**2003;**

**2006**

**OTHERS 1 Beesley, 2004 1 1,5 I/O – GT 1 Hauknes, 1999 1 1,5**

**2007**

**GT – CA 1 Giuliani, 2007 1 1,5**

**TOTAL 18 26 20 64 100**

I/P: Input – output; GT: Graph theory; CA: Correspondence analysis; y CE: Case studies

Table 1. Summary of the Methodologies Used, by Theoretical Approach

**6 Hervás et al., 2007; Hu et al., 2005; Lai et al., 2005; Mezquita & Lazzarini, 2006; Nassimbeni, 2003; Oyelaran-Oyeyinka & Lal, 2006**

 **2 Johannisson &**

**GT – CE 2 Macías, 2002;**

**Power & Lundmark, 20004; Vega-Rosado,**

**Chaminade, 1999; Oliveira & Fensterseifer, 2003 ; Roelandt et al., 1999;**

**3 Baptista & Swann, 1998; Beaudry & Breschi, 2003; McDonald et al.,**

> **Köroglu, 2005; Feldman et al., 2005; Heath, 1999; Khan & Ghani, 2004; Lagendijk & Charles, 1999; Nadvi, 1999; Nadvi & Halder,**

**THEORETICAL APPROACHES IN THE ANALYSIS OF BUSINESS NETWORKS TOT. %**

**NETWORKING**

**2 Hagedoorn & Duysters, 2002; Minguzzi & Passaro,**

**8 Benson-Rea & Wilson, 2003; Biggiero, 2001; Carbonara, 2002; Huggins, 2000; Lechner y Dowling, 2003; Pöyhönen y Smedlund, 2004; Steinle et al., 2007; Yogel et al., 2000**

 **2 3,1**

**1 Marceu, 1999 5 7,8**

**Carrasco & Albertos,**

**Malewicki, 2005; Mas-Ruíz, 2000**

**Ramirez-Pasillas,**

**2006**

**3 Bell, 2005;**

**2002; Reid et al., 2007**  **13 20,3**

**20 31,2**

**2 3,1**

**13 20,3**

**6 9,3**

**2000**

data.

**USED**

**METHODOLOGY**

**AGGLOMERATION ECONOMICS**

> **Alonso-Villar et al.,2004; Blasio & Di Addario, 2005; Callejón, 1998; Ciccone, 2001; Feldman & Audretsch, 1999; Le Bass & Miribel, 2005; Viladecans-Marsal, 2004**

**ONLY CE 1 Mun y Hutchinson, 1995 11 Eraydin & Amartli-**

**I/O – CA 1 Trueba y Lozano, 2001 1 Vom Hofe & Bhatta,**

**I/O – CE 4 Bishop et al., 2000;**

**O'Donoghue y Gleave,**

**Tuan & Ng, 2001; Zheng, 1998**

**Frenken et al., 2005; Rosenthal y Strange, 2003**

**4 Black et al., 2004; Davis & Weinstein,**

**CA – CE 4 Gordon & McCann, 2000;**

**OTHER COMBINATIONS**  **2004;**

**1999;**

**ONLY CA 8 Alecke et al., 2006;**

complementary knowledge with external partners for new product development has a positive effect on innovative performance. Based on the above, the following hypotheses are proposed:

*H3: In a localized company network environment, inter-company and company-institution technology and knowledge transfer has a positive effect on company innovation (product, process, administrative innovation).*

*H4: In a localized company network environment, inter-company and company-institution technology and knowledge transfer has a positive effect on company R&D activities (input for innovation).*

#### **5. Methodology**

The empirical studies reviewed above regarding the topic of business networks show the wide variety of technologies and techniques available for use (Vom Hofe & Chen, 2006 and Wolfe & Gertler, 2004). Dhose and Soltwedel (2006) drew a similar conclusion upon reviewing the papers presented in the Workshop on Spatial Econometrics in April 2005. Those authors stated that the topics of the most important papers presented – which analyzed what they called "innovation clusters" – had very different points of view and employed very different methodologies.

Nevertheless, there are papers that classify research into certain typologies of techniques. One of them is the classification established by the OECD team specialized in industrial clusters; it identifies five categories of analysis techniques: input–output tables, the innovation interaction matrix, the graph theory (or network analysis), the correspondence analysis (quantitative studies that use statistics and econometrics techniques), and case studies (OECD, 1999b). Wolfe and Gertler (2004) proposed three methodology perspectives for studying clusters: statistic analysis tools with different levels of sophistication for measuring the degree of grouping in a local or regional economy, case studies for an comparative analysis of an individual cluster or a comparative analysis of a group of clusters, and the analysis of public policy and strategies specifically designed for promoting the establishment and growth of a cluster or of a group of clusters in a location or region. This last methodology is frequently combined with quantitative studies and case studies.

Based on the above, to establish the methodology to be used in this work, the author analyzed 64 empirical studies and found that the various authors had used one of the three methodologies mentioned above. Table 1 presents a summary of the methodologies most commonly used in the mentioned analysis.

The table above shows a general tendency towards using combined methodologies, seen in 47% of the studies reviewed, 20% of which used the combination CA– CS that implies combining the data (qualitative and quantitative) obtained through field work. Combining techniques can lead to a more in-depth analysis of the set of events and phenomena that take place in business networks and that cannot be successfully explained by data gathered from secondary sources, which is common in studies that solely use correspondence analysis (quantitative studies). To be consistent with the literature on the topic, the analysis

complementary knowledge with external partners for new product development has a positive effect on innovative performance. Based on the above, the following hypotheses are

*H3: In a localized company network environment, inter-company and company-institution technology and knowledge transfer has a positive effect on company innovation (product, process,*

*H4: In a localized company network environment, inter-company and company-institution technology and knowledge transfer has a positive effect on company R&D activities (input for*

The empirical studies reviewed above regarding the topic of business networks show the wide variety of technologies and techniques available for use (Vom Hofe & Chen, 2006 and Wolfe & Gertler, 2004). Dhose and Soltwedel (2006) drew a similar conclusion upon reviewing the papers presented in the Workshop on Spatial Econometrics in April 2005. Those authors stated that the topics of the most important papers presented – which analyzed what they called "innovation clusters" – had very different points of view and

Nevertheless, there are papers that classify research into certain typologies of techniques. One of them is the classification established by the OECD team specialized in industrial clusters; it identifies five categories of analysis techniques: input–output tables, the innovation interaction matrix, the graph theory (or network analysis), the correspondence analysis (quantitative studies that use statistics and econometrics techniques), and case studies (OECD, 1999b). Wolfe and Gertler (2004) proposed three methodology perspectives for studying clusters: statistic analysis tools with different levels of sophistication for measuring the degree of grouping in a local or regional economy, case studies for an comparative analysis of an individual cluster or a comparative analysis of a group of clusters, and the analysis of public policy and strategies specifically designed for promoting the establishment and growth of a cluster or of a group of clusters in a location or region. This last methodology is frequently combined with quantitative studies and

Based on the above, to establish the methodology to be used in this work, the author analyzed 64 empirical studies and found that the various authors had used one of the three methodologies mentioned above. Table 1 presents a summary of the methodologies most

The table above shows a general tendency towards using combined methodologies, seen in 47% of the studies reviewed, 20% of which used the combination CA– CS that implies combining the data (qualitative and quantitative) obtained through field work. Combining techniques can lead to a more in-depth analysis of the set of events and phenomena that take place in business networks and that cannot be successfully explained by data gathered from secondary sources, which is common in studies that solely use correspondence analysis (quantitative studies). To be consistent with the literature on the topic, the analysis

proposed:

*innovation).*

case studies.

**5. Methodology**

employed very different methodologies.

commonly used in the mentioned analysis.

*administrative innovation).*

in this article combines case studies with quantitative techniques to understand gathered data.

I/P: Input – output; GT: Graph theory; CA: Correspondence analysis; y CE: Case studies

Table 1. Summary of the Methodologies Used, by Theoretical Approach

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 245

R&D in the company The company carries out R & D (experimental development, innovation projects, use of infrastructure).

Links for R&D The company partners or partnering with other local actors to

Inter-company TKT The company has or has had technology and knowledge transfer

reliability of the instrument, the following analyses were made: content validity by experts on the topic and a pilot test involving ten companies; Cronbach's alpha, to evaluate instrument consistency and proposed dimension consistency, obtaining α>0.7; and a correlation analysis, to establish convergent validity, obtaining significant correlations (*ρ<0.05*) and a concordant theoretical value (ρ>0.7) (Nunally, 1978). The above enabled establishing that the dimensions proposed for measuring inter-company business innovation, R&D activity links, and technology and knowledge transfer

First, a descriptive analysis was made in order to make an exploratory identification of the aspects related to the variables studied in the tool cluster. Also, a cluster analysis made with the maximum verisimilitude method using the criteria of "closest neighbor" and "unit of measure *lambda*, to identify the percentage of companies that develop innovations, by type of innovation (product, process and administrative). Based on the cluster analysis, a contingency analysis was made, to establish the realization indicator for the links defined in

For comparing the hypotheses, a logistic regression analysis was made using the stepwise logistic regression method, considered appropriate for analyzing dichotomic variables, measuring impact, and adjusting variables. The Hosmer-Lemeshow test was run, to prove the specificity of the regression model. A linktest was run, to prove that the logit models are a linear combination of the dependent variables and independent variables. An analysis of estimated correlations was made, to determine that there was no correlation among the explanatory variables. And a marginal effect analysis was made, to establish incidence in terms of the probability of the independent variables / dependent

(product, process, administrative) in the last five years.

perform R & D (experimental development, innovation projects, use

(machinery and equipment, specialized software and technical information and dissemination of research results) with other local.

VARIABLE OPERACIONALIZATION

Business innovation The company has made innovations

Yes 1, No 0

Yes 1, No 0

Yes 1, No 0

Company size Number of employees

Chart 1. Variable Operacionalization

presented homogeneous measurements.

the independent variables, according to the cluster.

**5.4 Comparison of hypotheses**

variables.

of infrastructure). Yes 1, No 0

#### **5.1 Population and sample**

The study consisted of 101 companies in the tools manufacturing sector (ISIC4); they represent the total population in the Provincial Department of Caldas in Colombia. The information was obtained from the Manizales Chamber of Commerce (CCM is the Colombian acronym), the Colombian National Industrialists Association (Asociación Nacional de Industriales - ANDI), the Colombian Small Industrialists Association (Asociación Colombiana de Pequeños industrials - ACOPI) and the Manizales telephone directories. Those databases were compared, to obtain the population under study. The population is distributed in three links of the tool cluster value chain: suppliers, core companies (tool manufacturers), and customers. Among those companies, 90% are micro-companies or small companies and 97% of them are part of forward and backward linkages, which enabled analyzing the networks and identifying their impact on the link nucleus (see Table 2).

Table 2. Tool Cluster General Data

#### **5.2 Measuring**

The study involved four company factors: innovation (product, process and administrative innovations), R&D activities (experimental development, innovation projects, use of infrastructure), links for inter-company and company-institution R&D activities (experimental development, innovation projects, use of infrastructure), and technology and knowledge transfer (machinery and equipment, specialized software and technical information, and dissemination of research results). To adjust the models, the control variable *company size* (number of employees) was included. The dependent variables as well as the independent variables are dichotomic variables (Jensen, et. al. 2007; Knudsen, 2007; Rammer & Schmiele, 2009). They are defined and operationalized in Chart 1.

#### **5.3 Validity and reliability**

The instruments used in this research have been adapted from prior research papers (Capello, 2002; Capello & Faggian, 2005; Cassiman & Veugelers, 2002; Earydin & Amartli-Köroglu, 2005; Filatochev et. al., 2003; Jensen et. al. 2007; Johansson & Karlsson, 2007; OCDE/EUROSTAT, 2005; OCDE; 2002). However, to establish the validity and

 4 International Standard Industrial Code. This group manufactures knifes, hand tools and hardware store items.

#### Chart 1. Variable Operacionalization

244 Management of Technological Innovation in Developing and Developed Countries

The study consisted of 101 companies in the tools manufacturing sector (ISIC4); they represent the total population in the Provincial Department of Caldas in Colombia. The information was obtained from the Manizales Chamber of Commerce (CCM is the Colombian acronym), the Colombian National Industrialists Association (Asociación Nacional de Industriales - ANDI), the Colombian Small Industrialists Association (Asociación Colombiana de Pequeños industrials - ACOPI) and the Manizales telephone directories. Those databases were compared, to obtain the population under study. The population is distributed in three links of the tool cluster value chain: suppliers, core companies (tool manufacturers), and customers. Among those companies, 90% are micro-companies or small companies and 97% of them are part of forward and backward linkages, which enabled analyzing the networks and

> **FEATURES** (%) **Type of link** 100 Suppliers 36 Main companies 3 Customers 61 **Company size** 100 Micro 64 Small 26 Medium 4 Large 6

The study involved four company factors: innovation (product, process and administrative innovations), R&D activities (experimental development, innovation projects, use of infrastructure), links for inter-company and company-institution R&D activities (experimental development, innovation projects, use of infrastructure), and technology and knowledge transfer (machinery and equipment, specialized software and technical information, and dissemination of research results). To adjust the models, the control variable *company size* (number of employees) was included. The dependent variables as well as the independent variables are dichotomic variables (Jensen, et. al. 2007; Knudsen, 2007;

The instruments used in this research have been adapted from prior research papers (Capello, 2002; Capello & Faggian, 2005; Cassiman & Veugelers, 2002; Earydin & Amartli-Köroglu, 2005; Filatochev et. al., 2003; Jensen et. al. 2007; Johansson & Karlsson, 2007; OCDE/EUROSTAT, 2005; OCDE; 2002). However, to establish the validity and

4 International Standard Industrial Code. This group manufactures knifes, hand tools and hardware

Rammer & Schmiele, 2009). They are defined and operationalized in Chart 1.

**5.1 Population and sample**

Table 2. Tool Cluster General Data

**5.3 Validity and reliability**

store items.

**5.2 Measuring**

identifying their impact on the link nucleus (see Table 2).

reliability of the instrument, the following analyses were made: content validity by experts on the topic and a pilot test involving ten companies; Cronbach's alpha, to evaluate instrument consistency and proposed dimension consistency, obtaining α>0.7; and a correlation analysis, to establish convergent validity, obtaining significant correlations (*ρ<0.05*) and a concordant theoretical value (ρ>0.7) (Nunally, 1978). The above enabled establishing that the dimensions proposed for measuring inter-company business innovation, R&D activity links, and technology and knowledge transfer presented homogeneous measurements.

#### **5.4 Comparison of hypotheses**

First, a descriptive analysis was made in order to make an exploratory identification of the aspects related to the variables studied in the tool cluster. Also, a cluster analysis made with the maximum verisimilitude method using the criteria of "closest neighbor" and "unit of measure *lambda*, to identify the percentage of companies that develop innovations, by type of innovation (product, process and administrative). Based on the cluster analysis, a contingency analysis was made, to establish the realization indicator for the links defined in the independent variables, according to the cluster.

For comparing the hypotheses, a logistic regression analysis was made using the stepwise logistic regression method, considered appropriate for analyzing dichotomic variables, measuring impact, and adjusting variables. The Hosmer-Lemeshow test was run, to prove the specificity of the regression model. A linktest was run, to prove that the logit models are a linear combination of the dependent variables and independent variables. An analysis of estimated correlations was made, to determine that there was no correlation among the explanatory variables. And a marginal effect analysis was made, to establish incidence in terms of the probability of the independent variables / dependent variables.

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 247

Using the data above and through a contingency analysis, the high innovation level conglomerate (Conglomerate 2) presents a higher inter-company association index for R&D activities (86%) and a higher TKT index (95.5%) than the low innovation level cluster

INDEPENDENT VARIABLES CONGLOMERATE 1 CONGLOMERATE 2

To compare the hypotheses, a logistic analysis was made. For the first two hypotheses, the author studied the incidence of inter-company and company-institution cooperation on carrying out R&D activities for business innovation (H1) and on each company's R&D activities (H2). For the third and fourth hypothesis, the author studied the effect of intercompany and company-institution technology and knowledge transfer (TKT) on business

The results showed that both H1 and H2 have a positive significant effect (*p value < 0.001*); such findings validate accepting both of those hypotheses. Likewise, both H3 and H4 have a positive significant effect (*p value < 0.001*), which means that they can be accepted (see Table

Links for R&D 7.6% 86.4% Inter-company TKT 21.5% 95.5% Table 6. Contingency Table of Innovation conglomerates and Links for R&D and inter-

innovation (H3) and on each company's R&D activities (H4).

Fig. 1. Business Innovation Conglomerates

(Conglomerate 1) (see Table 6).

company TKT

7 and 8).

#### **6. Findings**

First, the article presents a general profile of the sample companies according to study variables. The results indicate that 36% of the companies had developed innovations (product, process and administrative) and that 26% had carried out R&D activities; only 25% had links for the R&D activities and 38% had participated in technology and knowledge transfer (see Tables 3 and 4).

Table 3. Contingency Analysis among Business Innovation, Inter-company TKT, and Links for R&D

Table 4. Contingency Analysis among R&D in the company, Inter-company TKT, and Links for R&D

For the purpose of determining company behavior regarding the variable "innovation" considering the three types of innovation studied, a hierarchical cluster analysis was made, using as a reference the "closest neighbor" categories and *Goodman and Kruskal's lambda*5 as measurement interval, the latter commonly used for dichotomic variable analysis. That led to identifying two types of business conglomerates in the population, according to the innovation.

The first type was comprised of 79 companies with low levels of process innovation and administrative innovation and with no product innovation; it was called low innovation level conglomerate. The second type was comprised of 22 companies with a high level of process innovation (77%), administrative innovation (50%), and product innovation seen in all of the companies; it was called high innovation level conglomerate (see Figure 1 and table 5).

Table 5. Business Innovation Conglomerates

 5 This type of interval is used taking into account that the analysis variables are dichotomic.

Fig. 1. Business Innovation Conglomerates

246 Management of Technological Innovation in Developing and Developed Countries

First, the article presents a general profile of the sample companies according to study variables. The results indicate that 36% of the companies had developed innovations (product, process and administrative) and that 26% had carried out R&D activities; only 25% had links for the R&D activities and 38% had participated in technology and knowledge

NO 54% 10% NO 62% 2% YES 8% 28% YES 13% 23% Table 3. Contingency Analysis among Business Innovation, Inter-company TKT, and Links

NO 56% 18% NO 68% 6% YES 6% 20% YES 7% 19% Table 4. Contingency Analysis among R&D in the company, Inter-company TKT, and Links

For the purpose of determining company behavior regarding the variable "innovation" considering the three types of innovation studied, a hierarchical cluster analysis was made, using as a reference the "closest neighbor" categories and *Goodman and Kruskal's lambda*5 as measurement interval, the latter commonly used for dichotomic variable analysis. That led to identifying two types of business conglomerates in the population, according to the

The first type was comprised of 79 companies with low levels of process innovation and administrative innovation and with no product innovation; it was called low innovation level conglomerate. The second type was comprised of 22 companies with a high level of process innovation (77%), administrative innovation (50%), and product innovation seen in all of the companies; it was called high innovation level conglomerate (see Figure 1 and

**INNOVATION CONGLOMERATE 1 CONGLOMERATE 2**

process Innovation 0,1 0,77 product innovation 0 1 organizational Innovation 0,08 0,5

5 This type of interval is used taking into account that the analysis variables are dichotomic.

innovation

NO YES NO YES

company

NO YES NO YES

Links for R&D

Links for R&D

Inter-company TKT Business

Inter-company TKT R&D in the

**6. Findings**

Business innovation

for R&D

for R&D

innovation.

table 5).

**TYPES OF BUSINESS**

Table 5. Business Innovation Conglomerates

R&D in the company

transfer (see Tables 3 and 4).

Using the data above and through a contingency analysis, the high innovation level conglomerate (Conglomerate 2) presents a higher inter-company association index for R&D activities (86%) and a higher TKT index (95.5%) than the low innovation level cluster (Conglomerate 1) (see Table 6).

Table 6. Contingency Table of Innovation conglomerates and Links for R&D and intercompany TKT

To compare the hypotheses, a logistic analysis was made. For the first two hypotheses, the author studied the incidence of inter-company and company-institution cooperation on carrying out R&D activities for business innovation (H1) and on each company's R&D activities (H2). For the third and fourth hypothesis, the author studied the effect of intercompany and company-institution technology and knowledge transfer (TKT) on business innovation (H3) and on each company's R&D activities (H4).

The results showed that both H1 and H2 have a positive significant effect (*p value < 0.001*); such findings validate accepting both of those hypotheses. Likewise, both H3 and H4 have a positive significant effect (*p value < 0.001*), which means that they can be accepted (see Table 7 and 8).

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 249

generates an increase of 0.68% in innovation and an increase of 0.66% in each company's R&D activities. On the other hand, a percentile increase in inter-company and companyinstitution TKT generates an increase of 0.53% in innovation and an increase of 0.35% in each company's R&D activities. Upon comparing those results with the pseudo-coefficient of determination, the conclusion may be drawn that the models in which there is intercompany and company-institution cooperation for carrying out R&D activities present a greater fit than the models in which the variable inter-company and company-institution

VARIABLES Business innovation R&D in the company

Table 9. Marginal Effects of the Independent Variables on Company R&D Activities and on

The results obtained from applying the logit model as discussed above were tested, to verify the goodness of fit. To do so, the level of significance6 was identified for the model and Wald's linearity analysis parameters7 were applied, plus an error term distribution analysis8 was made. Likewise, the non-existence of multicolinearity and heteroskedacity was

Upon reviewing the literature that studies inter-company and company-institution relations, three main theoretical approaches were studied (agglomeration economies, clusters and industrial districts, and networking), which have guided the research on such relations and that, given the convergence in the theoretical orientation of such approaches, as well as the perspective employed in each research paper, the notion of "business networks" can be understood as the common unit of analysis. In that sense, the conclusion can be drawn that there is a consensus in the literature on the topic regarding the positive effect of intercompany and company-institution relations in a network environment on business performance, especially because they result in greater possibilities of knowledge development, broadened company's innovation process capabilities, and better results in

There was evidence in the tool cluster in the Provincial Department of Caldas in Colombia that the percentage of companies that have developed innovations and carried out R&D

8 The Roc Curve Graph was prepared and the sensitivity and specificity graphic analysis was made, as well as the Hosmer-Lemeshow test (*p value>0.001)* that proves the non-existence of stochastic

Links for R&D 0.68% \*\*\* 0.66% \*\*\* Inter-company TKT 0.53% \*\*\* 0.35% \*\*

δy/δx Sig. δy/δx Sig.

technology and knowledge transfer (TKT) is present (see Table 9).

\*\* *p value* <0,01; \*\*\* *p value* <0,001

**7. Discussion and conclusions**

process, product, and administrative innovations.

7 The parameters were identified as being linear and consistent (*p value>0.05).*

6 The Lr χ<sup>2</sup> *(p value < 0.001)* test was run.

Business Innovation

identified.

perturbations.

Table 7. Logistic Regression Analysis for the Tool Business Network – Business innovation

Table 8. Logistic Regression Analysis for the Tool Business Network – R&D in the company

To complement the analyses above, a marginal effect analysis was made, to establish to what degree association for R&D and TKT activities affects business innovation and company R&D activities. On one hand, the analysis showed that a percentile increase in inter-company and company-institution cooperation for carrying out R&D activities generates an increase of 0.68% in innovation and an increase of 0.66% in each company's R&D activities. On the other hand, a percentile increase in inter-company and companyinstitution TKT generates an increase of 0.53% in innovation and an increase of 0.35% in each company's R&D activities. Upon comparing those results with the pseudo-coefficient of determination, the conclusion may be drawn that the models in which there is intercompany and company-institution cooperation for carrying out R&D activities present a greater fit than the models in which the variable inter-company and company-institution technology and knowledge transfer (TKT) is present (see Table 9).

\*\* *p value* <0,01; \*\*\* *p value* <0,001

248 Management of Technological Innovation in Developing and Developed Countries

Constant -1,440441 -4,36 \*\*\* -1,990311 -5,31 \*\*\* -2,167 -5,09 \*\*\* Size 0,0611753 2,53 0,025925 1,48 0,03373 1,38

TKT 2,39628 4,28 \*\*\* Lr χ2 26,5 \*\*\* 58,77 \*\*\* 47,05 \*\*\*

Table 7. Logistic Regression Analysis for the Tool Business Network – Business innovation

Constant -1,852675 -5,52 \*\*\* -2,847551 -5,63 \*\*\* -2,4315 -5,46 \*\*\* Size 0,0401128 2,42 \* 0,020963 1,5 0,02583 1,71 …

TKT 1,64242 2,77 \*\*

Lr χ2 27,31 \*\*\* 55,41 \*\*\* 35,33 \*\*\*

Table 8. Logistic Regression Analysis for the Tool Business Network – R&D in the company

To complement the analyses above, a marginal effect analysis was made, to establish to what degree association for R&D and TKT activities affects business innovation and company R&D activities. On one hand, the analysis showed that a percentile increase in inter-company and company-institution cooperation for carrying out R&D activities

Pseudo R2 0,2371 0,481 0,3067 Goodnes fit 46,18 41,78

**R&D IN THE COMPANY Model 1 Model 2 Model 3 β Z Sig. β Z Sig. Β Z Sig.**

Pseudo R2 0,2014 0,4467 0,3576

fit 57,4 76,86

R&D 3,748522 4,54 \*\*\*

R&D 3,318111 4,85 \*\*\*

**BUSINESS INNOVATION Model 1 Model 2 Model 3 β Z Sig. β Z Sig. Β Z Sig.**

**Variables**

*Dependent Variable*  Links for

Intercompany

Goodnes

**Variables**

*Control Var.*

*Dependent Variable*  Links for

Intercompany

*Control Var.*

> Table 9. Marginal Effects of the Independent Variables on Company R&D Activities and on Business Innovation

The results obtained from applying the logit model as discussed above were tested, to verify the goodness of fit. To do so, the level of significance6 was identified for the model and Wald's linearity analysis parameters7 were applied, plus an error term distribution analysis8 was made. Likewise, the non-existence of multicolinearity and heteroskedacity was identified.

#### **7. Discussion and conclusions**

Upon reviewing the literature that studies inter-company and company-institution relations, three main theoretical approaches were studied (agglomeration economies, clusters and industrial districts, and networking), which have guided the research on such relations and that, given the convergence in the theoretical orientation of such approaches, as well as the perspective employed in each research paper, the notion of "business networks" can be understood as the common unit of analysis. In that sense, the conclusion can be drawn that there is a consensus in the literature on the topic regarding the positive effect of intercompany and company-institution relations in a network environment on business performance, especially because they result in greater possibilities of knowledge development, broadened company's innovation process capabilities, and better results in process, product, and administrative innovations.

There was evidence in the tool cluster in the Provincial Department of Caldas in Colombia that the percentage of companies that have developed innovations and carried out R&D

<sup>6</sup> The Lr χ<sup>2</sup> *(p value < 0.001)* test was run.

<sup>7</sup> The parameters were identified as being linear and consistent (*p value>0.05).*

<sup>8</sup> The Roc Curve Graph was prepared and the sensitivity and specificity graphic analysis was made, as well as the Hosmer-Lemeshow test (*p value>0.001)* that proves the non-existence of stochastic perturbations.

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 251

Allen, J.; James, A. & Gamlen, P. (2007). Formal versus informal knowledge networks in

Alonso-Villar, O.; Chamorro, J. & Gonzáles, X. (2004). Agglomeration economies in

Altenburg, T. & Meyer-Stamer, J. (1999). How to promote clusters: Policy experiences from

Antonelli, C. (2006). The bussines governance of localized knowledge: An information

Arvanitis, S.; Kubli, U.; Sydow, N. & Woerter, M. (2007). Knowledge and technology

Banyte, J. & Salickaite, R. (2008). Successful diffusion and adoption of innovation as a

Baptista, R. (1996). Research round up: industrial cluster and technological innovation,

Baptista, R. & Swann, P. (1998). Do firms in clusters innovate more?, *Research Policy*, Vol.27,

Barge-gil, A. (2009). *Cooperation-based innovators and peripheral cooperators: an empirical analysis*

Beaudry, C. & Breschi, S. (2003). Are firms in clusters really more innovative?, *Economy*

Becerra, F. (2008). Las redes empresariales y la dinámica de la empresa: aproximación

Beesley, L. (2004). Multi-level complexity management of knowledge networks, *Journal of*

Bell, M. & Albu, M. (1999). Knowledge systems and technological dynamism in

Bell, G. (2005). Clusters, networks, and firm innovativeness, *Strategic Management Journal*,

Benson-Rea, M. & Wilson, H. (2003). Networks, learning and the lifecycle, *European*

Biggiero, L. (2001). Self-organizing processes in building entrepreneurial networks: A

Bishop, P.; Brand, S. & McVittie, E. (2000). The use of input – output models in local impact

industrial clusters in developing countries, *World Development*, Vol.27, No.9, pp.

theoretical and empirical investigation, *Human Systems Management*, Vol.20, No.3,

Latin America, *World Development*, Vol.27, No.9, pp. 1693-1713.

pp. 179 – 196.

pp. 48 – 56.

pp. 525–540.

1715-1734.

pp. 209 – 222.

Vol.26, pp. 287 – 295.

Vol.13, No. 3, pp. 227 – 261.

*Management*, Vol.5, No.6, pp. 17 – 75.

*Business Strategy Review*, Vol.7, No.2, pp. 59 – 64.

Research Unit for Industrial Dynamics – DRUID. Denmark.

*Innovation New Technology*, Vol.12, No.4, pp. 325–342.

teórica. *Innovar*, Vol.18, No.32, pp. 27 – 46.

*Knowledge Management*, Vol.8, No.3, pp. 71 – 88.

*Management Journal*, Vol.21, No.5), pp. 588–597.

analysis, *Local Economy*, Vol.15, No.3, pp. 238 – 250.

2116*.*

R&D: a case study using social network analysis. *R&D Management*, Vol.37, No3,

manufacturing industries: The case of Spain, *Applied Economics,* Vol.36, pp. 2103–

economics approach for the economics of knowledge, *Industry and Innovation,*

transfer (KTT) activities between universities and firms in Switzerland – the main facts: an empirical analysis based on firm-level data. *The Icfai Journal of Knowledge*

means to increase competitiveness of enterprises. *Engineering Economics*, Vol.1,

*of their characteristics and behavior.* Paper presented at the conference Danish

activities is relatively low and, therefore, the author observes that the percentage for establishing relations for R&D and TKT is also low. That fact can be explained by the composition of the companies. Indeed, most fall into the category of SMEs with limited capabilities for carrying out those types of activities, as well as for establishing contacts with other regional stakeholders. Mahemba & De Bruijn (2003) found a similar situation in Tanzania; their explanation was that SMEs are not aware of opportunities in their midst, such as collaboration with research institutes, universities, technology centers, and the government. Nevertheless, that contradicts the findings of Barge-Gil (2009) who proposes that small firms and firms in low, medium-low, and medium sized sectors are more prone to innovation-based cooperation; he further highlights the role that suppliers play in innovation development.

The companies that stated that they had relations with other stakeholders enabled comparing the proposed hypotheses and, hence, enabled proving that inter-company relations and relations between companies and other regional stakeholders for R&D and TKT have a positive effect on the company's R&D and innovation. Therefore, the proposed hypotheses may be accepted. That fact corroborates other findings in the literature on the topic.

In general, the results obtained are consistent with the idea of "relational capital" (Capello, 2002; Capello and Faggian, 2005); nevertheless, they leave unanswered questions that may be addressed in future research. One practical suggestion for future research would be to study, in the business network under study herein, the determinants that influence the companies' low propensity to establish relations with other regional stakeholders in order to improve business innovation and even to observe other variables associated with the innovation process (Arvanitis *et al*., 2007) that may affect business innovation. Another future line of research may involve conducting similar inquiries in other business networks and in other geographical environments and then carrying out comparative analyses. It would be particularly interesting to study the relation between companies and support institutions (governmental and non-governmental) as that is a fundamental aspect for public policy regarding business competitiveness and regional social and economic development in countries such as Colombia.

Finally, the author proposes some other future lines of research to complement this article. Studies may be conducted inquiring what variables are the most determinant for the integral development of business networks, for example, employment is often used to evaluate productive specialization, competences or technological innovation. Yet another suggestion would be to identify the geographical contexts and sectors in which the research is focused and carry out comparative analyses.

#### **8. References**

Alecke, B.; Alsleben, C.; Scharr, F. & Untiedt, G. (2006). Are there really high-tech clusters? The geographic concentration of German manufacturing industries and its determinants, *Annals Regional Science*, Vol.40, pp. 19 – 42.

activities is relatively low and, therefore, the author observes that the percentage for establishing relations for R&D and TKT is also low. That fact can be explained by the composition of the companies. Indeed, most fall into the category of SMEs with limited capabilities for carrying out those types of activities, as well as for establishing contacts with other regional stakeholders. Mahemba & De Bruijn (2003) found a similar situation in Tanzania; their explanation was that SMEs are not aware of opportunities in their midst, such as collaboration with research institutes, universities, technology centers, and the government. Nevertheless, that contradicts the findings of Barge-Gil (2009) who proposes that small firms and firms in low, medium-low, and medium sized sectors are more prone to innovation-based cooperation; he further highlights the role that suppliers play in

The companies that stated that they had relations with other stakeholders enabled comparing the proposed hypotheses and, hence, enabled proving that inter-company relations and relations between companies and other regional stakeholders for R&D and TKT have a positive effect on the company's R&D and innovation. Therefore, the proposed hypotheses may be accepted. That fact corroborates other findings in the literature on the

In general, the results obtained are consistent with the idea of "relational capital" (Capello, 2002; Capello and Faggian, 2005); nevertheless, they leave unanswered questions that may be addressed in future research. One practical suggestion for future research would be to study, in the business network under study herein, the determinants that influence the companies' low propensity to establish relations with other regional stakeholders in order to improve business innovation and even to observe other variables associated with the innovation process (Arvanitis *et al*., 2007) that may affect business innovation. Another future line of research may involve conducting similar inquiries in other business networks and in other geographical environments and then carrying out comparative analyses. It would be particularly interesting to study the relation between companies and support institutions (governmental and non-governmental) as that is a fundamental aspect for public policy regarding business competitiveness and regional social and economic development

Finally, the author proposes some other future lines of research to complement this article. Studies may be conducted inquiring what variables are the most determinant for the integral development of business networks, for example, employment is often used to evaluate productive specialization, competences or technological innovation. Yet another suggestion would be to identify the geographical contexts and sectors in which the research

Alecke, B.; Alsleben, C.; Scharr, F. & Untiedt, G. (2006). Are there really high-tech clusters?

determinants, *Annals Regional Science*, Vol.40, pp. 19 – 42.

The geographic concentration of German manufacturing industries and its

innovation development.

in countries such as Colombia.

**8. References**

is focused and carry out comparative analyses.

topic.

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 253

Eraydin, A. & Armatli-Köroglu, B. (2005). Innovation, networking and the new industrial

Evangelista, R.; Perani, G.; Rapiti, F. & Archibugi, D. (1997). Nature and impact of

Evangelista, Rinaldo; Iammarino, Simona; Mastrostefano, Valeria & Silvani, Alberto (2002).

Fabrizio, K. (2009). Absorptive capacity and the search for innovation. *Research Policy*,

Feldman, J. & Audretsch, D. (1999). Innovation in cities: Science-based diversity,

Feldman, J.; Francis, J. & Bercovitz, J. (2005). Creating a cluster while building a firm:

Feser, E. & Bergman, E. (2000). National industry cluster templates: A framework for applied regional cluster analysis, *Regional Studies*, Vol.34, No.1, pp. 1 – 19. Filatochev, I.; Piga, C. & Dyomina, N. (2003). Network positioning and R&D activity: a study of Italian groups. *R&D Managemente*, Vol.33, No.1, pp. 37 – 48. Frenken, K.; Van Oort, F. & Verburg, T. (2005). *Variety and regional economic growth in the*

Gibb, A. (2006). Making markets in business development services for SMEs: Taking up the

Giuliani, E. (2007). Towards an understanding of knowledge spillovers in industrial clusters,

Gopalakrishnan, S. & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. *Omega*, Vol.25, No.1, pp. 15 – 28. Gordon, R. & McCann, P. (2000). Industrial Clusters: Complexes, agglomeration and/or

Groenewegen, J. & Van Der Steen, M. (2006). The evolution of national innovation systems,

Hagedoorn, J. & Duysters, G. (2002). Learning in dynamic inter-firm networks: The efficacy of multiple contacts, *Organization Studies*, Vol.23, No.4, pp. 525 – 548. Hauknes, J. (1999). Norwegian input-output clusters and innovation patterns. En OECD (Edit.), *Boosting innovation: The cluster approach* (pp. 61 – 90). París: OECD. Heath, R. (1999). The Ottawa high-tech cluster: Policy or luck? En OECD (Edit.), *Boosting*

Growth Agenda's Aalborg, Aalborg, Denmark, may 28 – 31 .

social networks? *Urban Studies*, Vol.37, No.3, pp. 513 – 532.

*innovation:The cluster approach* (pp. 175 – 192). París: OECD.

*Journal of Economic Issues*, Vol.40, No.2, pp. 277 – 285.

*Applied Economics Letters*, Vol.14, pp. 87–90.

survey. *Research Policy*, Vol.26, pp. 521-536.

survey. *Regional Studies,* Vol.36, No.2, pp. 173–186.

– 266.

– 429.

263-283.

Vol.38, pp. 255 – 267.

No.1, pp. 129 – 141.

clusters: The characteristics of networks and local innovation capabilities in the Turkish industrial clusters, *Entrepreneurship and regional development*, Vol.17, pp. 237

innovation in manufacturing industry: some evidence from the Italian innovation

Looking for regional systems of innovation: evidence from the Italian innovation

specialization and localized competition, *European Economic Review*, Vol.43, pp. 409

Entrepreneurs and the formation of industrial clusters, *Regional Studies*, Vol.39,

*Netherlands*. Paper presented to the Regional Studies Conference on Regional

Chinese challenge of entrepreneurial networking and stakeholder relationship management, *Journal of Small Business and Enterprise Development*, Vol.13, No.2, pp.

Black, G.; Chucrch, H. & Holley, D. (2004). Empirical estimation of agglomeration economies

Blasio, G. & Di Addario, S. (2005). Do workers benefit from industrial agglomeration?,

Brenner, T. & Greif, S. (2006). The dependence of innovativeness on the local firm

Buhman, C.; Kekre, S. & Singhal, J. (2005). Interdisciplinary and interorganizational

Callejón, M. (1998). *Concentración geográfica de la industria y economías de aglomeración*.

Callois, J. (2008). The two sides of proximity in industrial clusters: The trade-off between process and product innovation, *Journal of Urban Economics*, Vol.63, pp. 146–162. Capello, R. (2002). Spatial and Sectoral Characteristics of Relational Capital in Innovation

Capello, R. & Faggian, A. (2005). Collective Learning and Relational Capital in Local

Carbonara, N. (2002). New models of inter-firm networks within industrial districts,

Carrasco, J. & Albertos, J. (2006). Redes institucionales y servicios a las empresas en el

Carrie, A. (1999). Integrated clusters – the future basis of competition, *International Journal of*

Cassiman, B. & Veugelers, R. (2006). In search of complementarity in innovation strategy:

Chaminade, C. (1999). Innovation processes and knowledge flows in the information and

Cicconee, A. (2001). Efectos de aglomeración en Europa y en EE.UU. *Els Opuscles del Crei*,

Damanpour, F. (1987). The adoption of technological, administrative, and ancillary

Davis, D. & Weinstein, D. (1999). Economic geography and regional production structure: An empirical investigation, *European Economic Review*, Vol.43, pp. 379 – 407. Dohse, D. & Soltwedel, R. (2006). Recent developments in the research on innovative cluster,

Edwards, T. (2003). Innovation and organizational change: developments towards an

internal R&D and external knowledge acquisition. *Management Science*, Vol.52,

communication technologies (ICT) cluster in Spain. En OECD (Edit.), *Boosting*

innovations: impact of organizational factors. *Journal of Management,* Vol.13, No.4,

interactive process perspective. *Technology Analysis & Strategic Management*, Vol.12,

Activity. *European Planning Studies*, Vol.10, No.2, pp. 177 – 200.

Innovation Processes. *Regional Studies*, Vol.39, No.1, pp. 75–87.

*Entrepreneurship & Regional Development*, Vol.14, pp. 229 – 246.

*Innovation:The Cluster Approach* (pp. 219 – 242). París: OECD.

cluster cerámico de Castellón, *Scripta Nova*, X(213).

*Agile Management Systems*, Vol.1, No.1, pp. 45 – 50.

*European Planning Studies*, Vol. 14, No.9, editorial.

*Journal of Regional Science*, Vol.45, No.4, pp. 797–827.

*Management*, Vol.14, No.4, pp. 493–513.

Empresariales. Free Press.

328.

No.1, pp. 21–39.

No.1, pp. 68-82.

Vol.9, pp. 1 – 28.

No.4, pp. 445 – 464.

pp. 675-688.

associated with research facilities, *Atlantic Economic Journal*, Vol.32, No.4, pp. 320 –

population: An empirical study of German patents, *Industry and Innovation*, Vol.13,

Research: Establishing the science of enterprise networks, *Production and Operations*

Barcelona: Universidad de Barcelona Facultad de Ciencias Económicas y

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 255

Le Bas, C. & Miribel, F. (2005). The agglomeration economies associated with information

Lechner, C. & Dowling, M. (2003). Firm networks: External relationships as sources for de

Lin, B-W. & Chen, C-J. (2006). Fostering product innovation in industry networks: the

Macías, A. (2002). Redes sociales y clusters empresariales, *Revista Hispana para el Análisis de*

Mahemba, C. & De Bruijn, E. (2003). Innovation Activities by Small and Medium-sized

Malewicki, D. (2005). Member involvement in entrepreneur network organizations: The role

Marceu, J. (1999). The disappearing trick: Clusters in the Australian economy. En OECD (Edit.), *Boosting innovation: The cluster approach* (pp. 155 – 174). París: OECD. Martínez, J.& Céspedes, J. (2006). *Generación y difusión de la innovación en distritos industriales.*

Más-Ruíz, F. (2000). The supplier-retailer relationship in the context of strategic groups,

McCann, P. (1995). Rethinking the economics of location and agglomeration, *Urban Studies*,

McDonald, F.; Tsagdis, D. & Huang, Q. (2006). The development of industrial clusters and public policy, *Entrepreneurship & Regional Development*, Vol.18, pp. 525–542. Mella, J.; López, A. & Yrigoyen, C. (2005). Crecimiento económico y convergencia urbana en

Mesquita, L. & Lazzarini, S. (2006). Vertical and horizontal relationships in an industrial

Minguzzi, A. & Passaro, R. (2000). The network of relationships between the economic

Mun, S-I. & Hutchinson, B. (1995). Empyrical analysis of office rent and agglomeration

Muscio, A. (2006). Patterns of innovation in industrial districts: An empirical analysis,

Nadvi, K. (1999). Collective efficiency and collective failure: The response of the Sialkot

cluster: Implications for firms access to global markets. Paper presented at the

environment and the entrepreneurial culture in small firms, *Journal of Business*

economies: A case study of Toronto, *Journal of Regional Science*, Vol.35, No.3, pp. 437

surgical instrument cluster to global quality pressures, *World Development*, Vol.27,

españa. Investigación, nº 6, España: Instituto de Estudios Fiscales.

*Academy of Management Proceedings*, Atlanta, U.S., augost 11 – 16.

*Industry and Innovation*, Vol.13, No.3, pp. 291 – 312.

*Corporate Change*, Vol.14, No.2, pp. 343–363.

*Management*, Vol.17, No.1, pp. 155–173.

*Redes Sociales*, Vol.1, No.6, pp. 1 – 20.

*Development*, Vol.15, pp. 1- 26.

Vol.12, No.3, pp. 162 – 172.

Vol.32, No.3, pp. 563 – 577.

*Venturing*, Vol.16, pp. 181–207.

No.9, pp. 1605 – 1626.

141 – 166.

106.

– 455.

*Mi+d*, Free press.

technology activities: An empirical study of the US economy, *Industrial and*

growth and competitiveness of entrepreneurial firms, *Entrepreneurship & Regional*

mediating role of knowledge integration. *International Journal of Human Resource*

Manufacturing Enterprises in Tanzania. *Creativity and Innovation Management*,

of commitment and trust, *Journal of Development Entrepreneurship*, Vol.10, No,2, pp.

*International Journal of Retail & Distribution Management*, Vol.28, No.2, pp. 93-

 http://www4.usc.es/Lugo-XIII-Hispano-Lusas/pdf/01\_ESTRATEGIA/30\_hervas \_dalmau\_canales.pdf.

Hervás, J. & Dalmau, J. (2006). How to measure IC in clusters: Empirical evidence, *Journal of*

Hervás, J.; Dalmau, J. & Canales, C. (2007). Localización y estrategia empresarial: Contraste

http://www4.usc.es/Lugo-XIII-Hispano-Lusas/pdf/01\_ESTRATEGIA/30\_hervas

Hotz-Hart, B. (2000). Innovation networks, regions and globalization. En Clark, G.; Feldman,

Hu, T-S.; Lin, C-Y. & Chang, S-L. (2005). Role of interaction between technological

Huggins, R. (2000). The success and failure of policy-implanted inter-firm network

Jensen, R. (1996). Social issues in spatial economics, *International Journal of Social Economics*,

Jensen, M.; Johnson B.; Lorenz, E. & Lundvall, B. (2007). Forms of knowledge and modes of

Jhojannisson, B. & Ramirez-Pasillas, M. (2002). The institutional embeddedness of local

Johansson, S, & Karlsson, C. (2007). R&D accessibility and regional export diversity. *Annals*

Khan, J. & Ghani, J. (2004). Clusters and entrepreneurship: Implications for innovation in a

Knudsen, M. (2007). The relative importance of interfirm relationships and knowledge

Koch, T.; Kautonen, T. & Grünhagen, M. (2006). Development of cooperation in new venture

Lagendijk, A. & Charles, D (1999). Clustering as a new growth strategy for regional

Lai, H-C.; Chiu, Y-C. & Leu, H-D. (2005). Innovation capacity comparison of China's

district, Taiwan, *Urban Studies*, Vol.42, No.7, pp. 1139–1160.

empírico de un modelo de indicadores de competitividad en un sector industrial.

M. & Gertler, M. (Eds.), *The Oxford Handbook of Economic Geography* (pp. 432 – 450).

communities and industrial clustering in innovative activity: The case of Hsinchu

initiatives: Motivations, processes and structure, *Entrepreneurship & Rregional*

inter-firm networks: A leverage for business creation, *Entrepreneurship & Regional*

developing economy, *Journal of Developmental Entrepreneurship*, Vol.9, No.3, pp. 221

transfer for new product development success. *Journal of Product Innovation*

support networks: The role of key actors, *Journal of Small Business and Enterprise*

economies? A discussion of new forms of regional industrial policy in the United Kingdom. En OECD (Edit.), *Boosting innovation: The cluster approach* (pp. 127 – 154).

information technology industrial clusters: The case of Shanghai, Kunshan, Shenzhen and Dongguan, *Technology Analysis & Strategic Management*, Vol.17, No.3,

*Intellectual Capital*, Vol.7, No.3, pp. 354 – 380.

Oxford: Oxford University Press.

*Development*, Vol.12, pp. 111-135.

Vol.23, Nos.4/5/6, pp. 297 – 309.

*Development*, Vol.14, pp. 297 – 365.

*Management*, Vol.24, pp. 117 – 138.

*Development*, Vol.13, No.1, pp. 62 – 72.

*Regional Science*, Vol.41, pp. 501 – 523.

innovation, *Research Policy*, Vol.36, pp. 680–693.

Retrived from

\_dalmau\_canales.pdf.

– 238.

París: OECD.

pp. 293–315.

The Impact of Company Relationship and Institution Technology on R&D Activity and Innovation 257

Roelandt, T.; Den Hertog, P.; Van Sinderen, J. & Van Den Hove, N. (1999). Cluster analysis

Rosenthal, S. & Strange, W. (2003). Geography, industrial organization, and agglomeration,

Shumpeter, J. (1997) *Teoría del desenvolvimiento económico.* México: Fondo de Cultura

Slappendel, C. (1996). Perspectives on innovation in organizations. *Organization Studies*,

Steinle, S.; Holger, S. & Mietzner, K. (2007). Merging a firm-centred and a regional policy

Tikkanen, H. & Parvinen, P. (2006). Planned and spontaneous orders in the emerging

Tracey, P. & Clark, G. (2003). Alliances, networks and competitive strategy: Rethinking

Trueba, M. & Lozano, P. (2001). Las pautas de localización industrial en el ámbito

Tuan, C. & Ng, L. (2001). Regional division of labor from agglomeration economies' perspective: Some evidence, *Journal of Asian Economies*, Vol.12, pp. 65 – 85. Van Beers*,* C.; Berghäll, E. & Poot, T. (2008). R&D internationalization, R&D collaboration

Vega-Rosado, L. (2006). The international competitiveness of Puerto Rico using the Porter's

Viladecans-Marsal, E. (2004). Agglomeration economies and industrial location: City-level

Vom Hofe, R. & Chen, K. (2006). Whither or not industrial cluster: Conclusions or

Vom Hofe, R. & Bhatta, D. (2007). Method for identifying local and domestic industrial

Walker, G.; Kogut, B. & Shan, W. (1997). Social capital, structural holes and the formation of an industry network, *Organization Science*, Vol.8, No.2, pp. 109 – 125. Wolfe, D. & Gertler, M. (2004). Clusters from the inside and out: Local dynamics and global

Yoguel, G.; Novick, M. & Marin, A. (2000). Production networks: Linkages, innovation

clusters using interregional commodity trade data, *The Industrial Geographer*, Vol.4,

processes and social management technologies. A methodological approach applied to the volkswagen case in Argentina, *Danish Research Unit for Industrial*

model, *Journal of Global Competitiveness*, Vol.14, No,2, pp. 95 – 111.

evidence, *Journal of Economic Geography*, Vol.4, pp. 565–582.

confusions? *The Industrial Geographer*, Vol.4, No.1, pp. 2 – 28.

linkages, *Urban Studies*, Vol.41, Nos.5/6, pp. 1071–1093.

*Dynamics – DRUID working paper*, Vol.11, pp. 1 – 36.

cluster of innovation, *Grow and Change*, Vol.34, No.1, pp. 1 – 16.

the Netherlands. *Research Policy*, Vol.37, pp. 294–308.

perspective for the assessment of regional clusters: Concept and application of a "dual" approach to a medical technology cluster, *European Planning Studies*, Vol.15,

network society, *Journal of Business & Industrial Marketing*, Vol.21, No.1, pp. 38–

municipal: Relevancia de las economías de aglomeración, *Economía Industrial*,

and public knowledge institutions in small economies: evidence from Finland and

*The Review of Economics and Statistics*, Vol.85, No.2, pp. 377–393.

*cluster approach* (pp. 315 – 338). París: OECD.

Económica.

49.

Vol.17, No.1, pp. 107 – 129.

No.2, pp. 235 – 251.

Vol.337, pp. 177 – 188.

No.2, pp. 1 – 27.

and cluster policy in the Netherlands. En OECD (Edit.), *Boosting innovation: The*

Nadvi, K. & Halder, G. (2005). Local cluster in global value chains: Exploring linkages

Nassimbeni, G. (2003). Local manufacturing systems and global economy: Are they

Nassimbeni, G. & Sartor, M. (2005). The internationalization of local manufacturing systems:

OECD/EUROSTAT (2005). *Oslo manual*: Guidelines for collecting and interpreting

OECD (2002). *Frascati Manual: Proposed Standard Practice for Surveys on Research and*

O'Donoghue, D. & Gleave, B. (2004). A note on methods for measuring industrial

Oliveira, E. & Fensterseifer, J. (2003). Use of resource-based view in industrial cluster

Oyelaran-Oyeyinka, B. & Lal, K. (2006). Institutional support for collective learning: Cluster

Perdomo, J. & Malaver, F. (2003). *Metodología para la referenciación competitiva de clusters*

Power, D. & Lundmark, M. (2004). Working through knowledge pools: Labour market

Pöyhönen, A. & Smedlund, A. (2004). Assessing intellectual capital creation in regional

Rammer, C. & Schmiele, A. (2009). Drivers and effects of internationalizing innovation by

Red de Indicadores de Ciencia & Tecnología (RICyT), Organización de los Estados

Reid, N.; Carroll, M. & Smith, B. (2007). Critical steps in the cluster building process,

clusters, *Journal of Intellectual Capital*, Vol.5, No.3, pp. 351 – 365.

*Economic Development Journal*, Vol.6, No.4, pp. 44 – 52.

strategic analyisis, *International Journal of Operations & Production management*,

development in Kenya and Ghana, *African Development Review*, Vol.18, No.2, pp.

*estratégicos regionales*. Bogotá: Centro de Investigaciones para el Desarrollo – CID,

dynamics, the transference of knowledge and ideas, and industrial clusters, *Urban*

SMEs. *The Icfai University Journal of Knowledge Management*, Vol.7, No.2, pp. 18 –

Americanos (OEA), Programa de Ciencia y Tecnología para el Desarrollo (Cyted) de Colciencias y Observatorio Colombiano de Ciencia y Tecnología (OCyT), (2001). *Manual de Bogotá: normalización de indicadores de innovación tecnológica en América*

agglomeration, *Regional Studies*, Vol.38, No.4, pp. 419 – 427.

pp. 339 – 363.

pp. 470–478.

258 – 278.

61.

*Management*, Vol.21, No.2, pp. 151–171.

innovation data. París: OECD.

Vol.23, No.9, pp. 995 – 1009.

Universidad Nacional de Colombia.

*Studies*, Vol.41, Nos.5/6, pp. 1025–1044.

*Latina y el Caribe*. Bogotá: autores

*Experimental Development*. París: OECD.

Nunally, J. C. (1978). *Psychometric theory*. New York: McGraw Hill. OECD (1999a). *Boosting Innovation:The Cluster Approach*. París: OECD. OECD (1999b). *Managing National Innovation Systems.*París: OECD.

between Germany and Pakistan, *Entreprenerurship & Regional Development*, Vol.17,

compatible? The case of the Italian eyewear district, *Journal of Operations*

evidence from the Italian chair district, *Production Planning & Control*, Vol.16, No.5,

**12**

*Spain*

*University of Zaragoza*

**The Impact of ICT on Productivity:**

Technological innovation has generated considerable interest among academics and practitioners in recent years. In recent decades, Information and Communication Technologies (ICT) such as computer terminals, e-mail and the Internet and their applications have become the major drivers of innovation, growth and social change. Moreover, as the OECD points out (OECD, 2010), in times of crisis there must be a focus on the contribution of ICT to innovation and growth. However, although interest in this subject has grown substantially, research on the importance of the combination of organizational change together with technological innovation has been less common. Some studies suggest that technological innovation is not an isolated source of improvement, but part of a system

Then, the simply availability of new ICT does not necessarily lead to success. On the contrary, it requires that firms accompany innovation with the development of best organizational practices (Brynjolfsson et al., 2002; Huerta et al., 2008), and it is therefore necessary to study the influence of this question in more detail, as we explore in this chapter. Our main goal is to examine whether technological innovation and organizational change are complementary, and whether they are associated with better performance.

In order to reach this objective, survey data from more than a thousand Spanish firms has been used. In particular, we analyze the impact of technological innovation and diverse complementary elements on organizational productivity. The main questions to answer are:

These questions are especially important in today's highly competitive environment. Over the last decade, competition has intensified and companies have found the need to restructure and improve their business practices in order to find new and more efficient

We undertake diverse descriptive analyses, which allow us to observe the characteristics of more efficient firms. Following prior studies, we use a standard Cobb-Douglas production

*Are organizational characteristics complementary to ICT in improving firm performance?*

ways to obtain competitive advantage as a condition of survival.

or cluster of mutually-reinforcing organizational approaches.

*Is firm performance improved through ICT?*

**1. Introduction**

**The Moderating Role of Worker**

Ana Gargallo-Castel and Carmen Galve-Górriz

**Quality and Quality Strategy**

## **The Impact of ICT on Productivity: The Moderating Role of Worker Quality and Quality Strategy**

Ana Gargallo-Castel and Carmen Galve-Górriz *University of Zaragoza Spain*

#### **1. Introduction**

258 Management of Technological Innovation in Developing and Developed Countries

Zhang, Y. (2007). Inter-firm networks and innovation: The difference between the horizontal

Zheng, X-P. (1998). Measuring optimal population distribution by agglomeration

Philadelphia, U. S. , august 3 – 8.

pp. 95 – 112.

and vertical type. Paper presented at the *Academy of Management Proceedings*,

economies and diseconomies: A case study of Tokio, *Urban Studies*, Vol.35, No.1,

Technological innovation has generated considerable interest among academics and practitioners in recent years. In recent decades, Information and Communication Technologies (ICT) such as computer terminals, e-mail and the Internet and their applications have become the major drivers of innovation, growth and social change. Moreover, as the OECD points out (OECD, 2010), in times of crisis there must be a focus on the contribution of ICT to innovation and growth. However, although interest in this subject has grown substantially, research on the importance of the combination of organizational change together with technological innovation has been less common. Some studies suggest that technological innovation is not an isolated source of improvement, but part of a system or cluster of mutually-reinforcing organizational approaches.

Then, the simply availability of new ICT does not necessarily lead to success. On the contrary, it requires that firms accompany innovation with the development of best organizational practices (Brynjolfsson et al., 2002; Huerta et al., 2008), and it is therefore necessary to study the influence of this question in more detail, as we explore in this chapter. Our main goal is to examine whether technological innovation and organizational change are complementary, and whether they are associated with better performance.

In order to reach this objective, survey data from more than a thousand Spanish firms has been used. In particular, we analyze the impact of technological innovation and diverse complementary elements on organizational productivity. The main questions to answer are:

#### *Is firm performance improved through ICT?*

#### *Are organizational characteristics complementary to ICT in improving firm performance?*

These questions are especially important in today's highly competitive environment. Over the last decade, competition has intensified and companies have found the need to restructure and improve their business practices in order to find new and more efficient ways to obtain competitive advantage as a condition of survival.

We undertake diverse descriptive analyses, which allow us to observe the characteristics of more efficient firms. Following prior studies, we use a standard Cobb-Douglas production

The Impact of ICT on Productivity: The Moderating Role of Worker Quality and Quality Strategy 261

Strassmann, 1985, 1990; Weill, 1992; Wilson, 1993, 1995). This empirical evidence led to the so-called Productivity Paradox, well summarized by Nobel Prize Robert Solow (1987), who

According to Brynjolfsson (1993) and Brynjolfsson and Yang, (1996) the various explanations that have been proposed for this apparent lack of relationship can be grouped into four

However, moving beyond the productivity paradox, there is growing new evidence that ICT generates large positive returns that are even in excess of the returns from other types of investments (Dewan and Min, 1997). Authors such as Lichtenberg (1995) and Brynjolfsson and Hitt (1995, 1996) offer empirical evidence of the positive impact of ICT on a variety of

In any case, the notion that ICT per se does not generate sustainable competitive advantage has received important support in recent research (Kettinger et al., 1994; Powell and Dent-Micallef, 1997), giving rise to what has become known as the "strategic necessity hypothesis". According to the Resource Based View, also known as RBV (Rumelt, 1984; Wernerfelt, 1984; Peteraf, 1993; Barney, 1991) a resource must possess certain characteristics to be qualified as strategic. Specifically, in order to both generate and sustain competitive advantage over a period of time, a resource must be "valuable", "scarce", "unable to

Today, ICT is so widely available that it can hardly be described as being scarce, or difficult to imitate, and thus it does not satisfy the necessary criteria to be considered critical. Conversely, critical value may reside in the complementary or synergic effects of ICT with

Recently, the empirical literature has begun to re-assess the association between ICT and a wide variety of complementary factors (Arvanitis and Loukis, 2009; Giuri et al., 2008; Aral and Weill 2007), with a consensus emerging that, in order for ICT to be properly utilised, it must be used in conjunction with complementary resources such as organisational structure, human resources or organisational resources (Walton, 1989; Bélanger and Collins, 1998; Bresnahan et al., 2002; Mata et al., 1995; Ramírez et al., 2001; Peppard and Ward ,2004; Aral

Focusing on the retail industry in the US, Powell and Dent-Micallef (1997) find that ICT alone has not produced sustainable performance advantages, while some firms have gained competitive advantages by using these technologies with complementary human and organisational resources. Also for the US, Bresnahan et al. (2002) offer empirical evidence about the positive effects of combining ICT and organisational design on increasing firm productivity. The same results are offered by Black and Lynch (2001), who examine the

impact of ICT, human resource practices, and firm reorganization on productivity.

imitate" and "complementary with other elements or resources of the firm.

said, "You can see the computer age everywhere but in the productivity statistics."

categories:

et al., 2010).

1. Mis-measurement of outputs and inputs 2. Lags due to learning and adjustment 3. Redistribution and dissipation of profits

internal resources and capabilities of the firm.

measures of firm performance.

4. Mismanagement of information and technology

function to model the production process, considering ICT as a factor of production (Brynjolfsson et al., 2011; Hitt and Brynjolfsson, 1996; Dewan and Min, 1997). With this analysis, we will asses the impact of these technologies on company efficiency and productivity.

To test organizational complementarities, we use a methodology that allows us to model the joint effects of ICT and organizational and human resources on performance. The results show the importance of organizational human capital in order to increase the benefits of the technological innovation.

Our results contribute to the international body of research and offer a new conceptualization and empirical evidence of the technology innovation performance.

We have structured the work into five parts, including this introduction. In the next section we review the main literature. We then discuss the theoretical relationship between ICT and competitive advantage, stating our hypothesis using the complementarities perspective as the main theoretical framework. Next, we present the research design, sample and definition of variables and we discuss the main results. In the final section, we present our conclusions, discuss certain limitations of our study, and propose several directions in which to continue our research.

#### **2. Literature review**

There have been many challenges and variations in the forces for globalization during the last decade. One that has garnered substantial attention over the past few years is concerned with the impact of information and communication technology on economic growth and on firm performance (OECD, 2004). The widespread diffusion of the Internet, the mobile phone and the broadband networks shows how pervasive these technologies have become.

According to much theoretical and empirical evidence, ICT offers benefits for a wide range of business processes and improves information and knowledge management within the firm, leading to better performance. Firms can manage their processes more efficiently and, as a consequence, they increase their operational efficiency. Moreover, ICT reduces the coordination costs of the firm because of lower procurement and inventory costs and closer coordination with suppliers (Tachiki et al., 2004; OECD, 2003, 2004). In addition, communication based on ICT and the Internet can also improve external communication, reducing the inefficiencies resulting from lack of co-ordination between firms, and increasing the speed and reliability of information processing and transfer. In general, ICT reduces transaction and coordination costs, maximizing the value of the transactions (OECD, 2004).

However, according to the literature review on the impact of ICT at the firm level, we can confirm the diversity of theoretical approaches and empirical evidence on the role of ICT in the improvement of the firm performance.

Much of the early literature on ICT, mainly in the 1980s and early 1990s, theoretically justified the advantages of information technologies, but they obtained contradictory empirical evidence, especially weak or with no link between ICT and firm performance (Brynjolfsson, 1993; Davenport, 1994; Kettinger et al., 1994; Loveman, 1994; Roach, 1987; Strassmann, 1985, 1990; Weill, 1992; Wilson, 1993, 1995). This empirical evidence led to the so-called Productivity Paradox, well summarized by Nobel Prize Robert Solow (1987), who said, "You can see the computer age everywhere but in the productivity statistics."

According to Brynjolfsson (1993) and Brynjolfsson and Yang, (1996) the various explanations that have been proposed for this apparent lack of relationship can be grouped into four categories:

260 Management of Technological Innovation in Developing and Developed Countries

function to model the production process, considering ICT as a factor of production (Brynjolfsson et al., 2011; Hitt and Brynjolfsson, 1996; Dewan and Min, 1997). With this analysis, we will asses the impact of these technologies on company efficiency and

To test organizational complementarities, we use a methodology that allows us to model the joint effects of ICT and organizational and human resources on performance. The results show the importance of organizational human capital in order to increase the benefits of the

Our results contribute to the international body of research and offer a new

We have structured the work into five parts, including this introduction. In the next section we review the main literature. We then discuss the theoretical relationship between ICT and competitive advantage, stating our hypothesis using the complementarities perspective as the main theoretical framework. Next, we present the research design, sample and definition of variables and we discuss the main results. In the final section, we present our conclusions, discuss certain limitations of our study, and propose several directions in

There have been many challenges and variations in the forces for globalization during the last decade. One that has garnered substantial attention over the past few years is concerned with the impact of information and communication technology on economic growth and on firm performance (OECD, 2004). The widespread diffusion of the Internet, the mobile phone

According to much theoretical and empirical evidence, ICT offers benefits for a wide range of business processes and improves information and knowledge management within the firm, leading to better performance. Firms can manage their processes more efficiently and, as a consequence, they increase their operational efficiency. Moreover, ICT reduces the coordination costs of the firm because of lower procurement and inventory costs and closer coordination with suppliers (Tachiki et al., 2004; OECD, 2003, 2004). In addition, communication based on ICT and the Internet can also improve external communication, reducing the inefficiencies resulting from lack of co-ordination between firms, and increasing the speed and reliability of information processing and transfer. In general, ICT reduces transaction and coordination costs, maximizing the value of the transactions

However, according to the literature review on the impact of ICT at the firm level, we can confirm the diversity of theoretical approaches and empirical evidence on the role of ICT in

Much of the early literature on ICT, mainly in the 1980s and early 1990s, theoretically justified the advantages of information technologies, but they obtained contradictory empirical evidence, especially weak or with no link between ICT and firm performance (Brynjolfsson, 1993; Davenport, 1994; Kettinger et al., 1994; Loveman, 1994; Roach, 1987;

and the broadband networks shows how pervasive these technologies have become.

conceptualization and empirical evidence of the technology innovation performance.

productivity.

technological innovation.

which to continue our research.

**2. Literature review**

(OECD, 2004).

the improvement of the firm performance.

However, moving beyond the productivity paradox, there is growing new evidence that ICT generates large positive returns that are even in excess of the returns from other types of investments (Dewan and Min, 1997). Authors such as Lichtenberg (1995) and Brynjolfsson and Hitt (1995, 1996) offer empirical evidence of the positive impact of ICT on a variety of measures of firm performance.

In any case, the notion that ICT per se does not generate sustainable competitive advantage has received important support in recent research (Kettinger et al., 1994; Powell and Dent-Micallef, 1997), giving rise to what has become known as the "strategic necessity hypothesis". According to the Resource Based View, also known as RBV (Rumelt, 1984; Wernerfelt, 1984; Peteraf, 1993; Barney, 1991) a resource must possess certain characteristics to be qualified as strategic. Specifically, in order to both generate and sustain competitive advantage over a period of time, a resource must be "valuable", "scarce", "unable to imitate" and "complementary with other elements or resources of the firm.

Today, ICT is so widely available that it can hardly be described as being scarce, or difficult to imitate, and thus it does not satisfy the necessary criteria to be considered critical. Conversely, critical value may reside in the complementary or synergic effects of ICT with internal resources and capabilities of the firm.

Recently, the empirical literature has begun to re-assess the association between ICT and a wide variety of complementary factors (Arvanitis and Loukis, 2009; Giuri et al., 2008; Aral and Weill 2007), with a consensus emerging that, in order for ICT to be properly utilised, it must be used in conjunction with complementary resources such as organisational structure, human resources or organisational resources (Walton, 1989; Bélanger and Collins, 1998; Bresnahan et al., 2002; Mata et al., 1995; Ramírez et al., 2001; Peppard and Ward ,2004; Aral et al., 2010).

Focusing on the retail industry in the US, Powell and Dent-Micallef (1997) find that ICT alone has not produced sustainable performance advantages, while some firms have gained competitive advantages by using these technologies with complementary human and organisational resources. Also for the US, Bresnahan et al. (2002) offer empirical evidence about the positive effects of combining ICT and organisational design on increasing firm productivity. The same results are offered by Black and Lynch (2001), who examine the impact of ICT, human resource practices, and firm reorganization on productivity.

The Impact of ICT on Productivity: The Moderating Role of Worker Quality and Quality Strategy 263

As we have seen, although some research concludes that ICT has negative or irrelevant effects, since the last decade there has been a growing consensus that there is a strong positive link (Atzeni and Carboni, 2006). ICT can help improve productivity and the efficiency of all stages of the production process by reducing set-up time, run time and inspection time, which will help efficient firms to improve their market position.

*Hypothesis 1: "The impact of information and communication technologies on productivity will be*

However, within the theoretical framework of RBV and the complementarities discussed above, we can argue that the firm-level impacts of ICT may be higher in those companies that have been able to reorganise. In order to implement effectively and to reap benefits

Investment in human resources has been one of the factors most commonly explored, because of its possibilities for enhancing the effectiveness of organizational practice. Furthermore, human resources management is considered to be not only a determining factor of productivity, but also a complementary element of ICT (OECD, 2001a). According to 'skill-biased' technological change, we can argue that ICT is associated with a greater demand for highly educated workers (Acemoglu, 1998; Black and Lynch, 2001; Bresnahan et al., 2002; Caroli and Van Reenen, 2002). Skilled employees allow firms to integrate ICT more effectively in the planning process of the business (Bharadwaj, 2000). Available empirical evidence suggests that improvements in the composition of the labour factor have directly contributed to labour productivity growth in many countries (OECD, 2005). At the firm level, various authors have shown that ICT use can have positive effects on performance, but those positive effects may be smaller if the necessary complementary investments in skills have not occurred to a sufficient degree (Arvanitis, 2005; Powell and Dent-Micallef, 1997; Pinsonneault and Kraemer, 1997; Francalanci and Galal, 1998 and Bresnahan et al., 2002). As these authors show, ICT has substantial impacts on firm performance when it is combined with a higher level of better-qualified personnel. Therefore, we define the next

*Hypothesis 2: "The impact of ICT on results will be greater for organisations that combine ICT with a*

Research has also highlighted the importance of strategic alignment, defined in terms of ICT support for the business strategy (Tallon and Kraemer, 2008). Specifically, ICT adoption has been positively associated with those firms that attach greater importance to the quality of their products (Lal, 1999). ICT investments provide significant opportunities to decrease the costs of overseeing production and distribution. Insofar as quality systems are closely related to the availability of flows and information processing, ICT will offer new approaches to the implementation of quality assurance systems. In this context, implementation of ICT is most effective when introduced in conjunction with quality systems (Bruque and Moyano, 2007). Moreover, as Ramírez et al. (2001) stress, the impact of ICT on results is positively influenced by the adoption of TQM and re-engineering. Thus, we

from ICT investments, complementary resources and capabilities will be needed.

**3. Hypothesis development**

*positive"*

hypothesis as follows:

*high level of worker qualification".*

propose the following hypothesis:

Accordingly, we propose our first hypothesis:

Crespi et al. (2007) examine the relationships between productivity growth, ICT investment and organisational change in UK firms, and their results support the idea that gains from IT need re-organisation to produce measured productivity growth.