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The evolution of the network structure in the ICT sector
ARTICLE IN PRESS Int. J. Production Economics 115 (2008) 296– 304
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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe
The evolution of the network structure in the ICT sector Jukka Hallikas , Jari Varis, Heli Sissonen, Veli-Matti Virolainen School of Business, Lappeenranta University of Technology, Box 20, FI-53851 Lappeenranta, Finland
a r t i c l e i n f o
abstract
Article history: Received 5 October 2006 Accepted 5 November 2007 Available online 21 June 2008
This study examines the development of collaborative relationships in the Information and Communications Technology (ICT) sector. Companies form close relationships with other companies to access complementary resources, and such links create networks that promote value delivery. This paper provides a theoretical outline of the structural network and alliance research. We also analyze the evolution of the ICT network in terms of position and power relations at different time spans, and provide an overview of the theoretical basis of interfirm networking. The network dynamics and evolution based on structural analysis of the network is clarified by describing the alliance evolution of different actors in a case ICT sector network. We also analyze the actors’ structural position in the industry network by adapting the algorithms for structural network analysis. Finally, we compare the companies’ structural position in the network and their R&D input. Our results indicate that value networks emerge and develop to a large extent through their structure. A strong position in these networks seems to indicate external resource orientation. Furthermore, the results of this paper provide indications about the circumstances and effects of a company’s structural position in an ICT industry network. & 2008 Elsevier B.V. All rights reserved.
Keywords: Supplier networks Supply-chain management Risk management
1. Introduction Interest in industrial networks has increased since various companies and industries have become more dependent on each other. Organizations with complementary strategies, resources and capabilities are joining forces in order to meet the needs of customers in a manner that benefits all participants in the value network. Thus, we have been witnessing the emergence of entire value networks. Our aim in this paper is to examine the theoretical and empirical foundations of the analysis of value-network dynamics in the Information and Communications Technology (ICT) industry. According to Steinbock (2002), the industry has changed from one of closed specifications, central innova-
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E-mail address: hallikas@lut.fi (J. Hallikas). 0925-5273/$ - see front matter & 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2007.11.014
tion and domestic marketing to one in which open specifications, distributed innovation and global networking prevail. An example of this kind of networking is the standardization organization, whose role has changed over the years. The reasons for this trend include the high speed of technological change, which increases standardization activities but also makes them non-transparent and complex: there have been institutional changes in standardization, driven by the short development and product life cycles. Whereas traditional standard-setting organizations used to develop standards for the ICT industry, due to the rapid pace of innovation informal consortia have now taken more of a hold on these activities. This phenomenon has been explained through game-theoretical models, for example. It has been found that formal processes are of a higher quality and legitimacy, but are slower to develop than informal processes. On the other hand, the traditional, formal standard-setting organizations may adapt well to meeting the needs of mature industries operating within stable
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markets. In the context of dynamic industries such as ICT, the emphasis of traditional standard-setting organizations on transparent, inclusive processes and broad consensus is not relevant to the needs of dynamic sectors (Winn, 2005). The institutional view in economic literature is often connected to the organization as an institution (Williamson, 1985). More recently, Nooteboom (2004) provided a more comprehensive definition: institutions both constrain and enable action, they are the result of earlier action, they are relatively stable, and they are relatively binding. Furthermore, external change factors affect different institutional levels—the country, the industry, and the organization driving the change. In this study, we consider the ICT industry to be an institution, and explore the impact of inter-firm alliance formation and R&D investments on it. One recent research field focusing on the emergence and evolution of industry dynamics is related to the value network approach (see e.g., Allee, 2003), the original objective of which was to examine the tangible and intangible dynamic interchange between various actors in the industry. These networks often emerge and develop in line with the collaborative relationships of firms within and across industry value chains and networks. Alliance formation and dynamics are therefore essential factors in determining their development. An important element affecting the value distribution in networks is related to the formation of linkages between organizations in various industries. Powell et al. (1996) found that the position in industry-wide strategic networks and the capability of exploiting alliance learning was the locus of innovation. Other studies have also described the causal linkages between network position and innovation outputs, such as patents (Ahuja, 2000). Companies in industry networks may implement different involvement strategies, such as finding a balance between dependency on internal and collaborative resources. Industry networks have a finite duration and thus their own life-cycle. Given the fact that the network structure is dynamic and constantly changing, it is essential to consider their evolution. Ebers (1999) defines inter-organizational networks as institutionalized, recurring, partner-specific exchange relationships of finite (often based on goal accomplishment) or unspecified duration involving a limited number of actors. It follows that individual collaborative relationships lead to the formation and development of network structures. Our study concerns the development of collaborative relationships in the ICT industry. What makes the ICT industry interesting in the global context is that it is currently in a state of continuous and at least partially unpredictable change. Companies form close relationships in order to access complementary resources, and such links create the network that promotes value delivery. Different actors such as service operators and infrastructure manufacturers adopt different strategies for positioning themselves in the industry. This practical consideration provides a strong argument for exploring
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the dynamics of the alliance and network development. In brief, this paper has the following objectives:
To provide a theoretical outline of structural networks and alliance research.
To analyze the evolution of the ICT network in terms of position and power relations over different time spans. In order to outline the theories and methods used in analyzing the development of collaborative relationships, we first provide an overview of the theoretical basis of interfirm networking. There are several relevant theories and approaches to explaining the development of networks. On the other hand, the strategic choice of positioning seems to be one of the fundamental decisions that companies involved in an industry network have to make. We focus our investigation of network dynamics and evolution on a structural analysis of the case ICT network and the alliance evolution of the different actors involved. We consider the nature of alliances through the SDC Platinum database,1 and analyze the actors’ structural positions in the industry network by adapting the algorithms and UCINET software2 developed for the structural network analysis. Finally, we compare the companies’ structural positions in the network and their R&D input. This is a question of indicating the actors’ strategic positioning in terms of their usage of internal (R&D inputs) and external resources (network position). 2. Theoretical considerations 2.1. The context Institutional theorists have described the nature of institutional pressures to achieve conformity and uniformity. Greenwood and Hinings (1996) emphasized the exogenous nature of change, which emanates from the realm of ideas and legitimacy. In the analysis of value networks it is essential to understand the incidence and pacing of radical organizational change, in particular the differences between organizations as they respond to apparently similar contextual pressures. Dynamics have been defined as patterns of value commitment, dissatisfaction with interests, power dependencies, and capacities for action. In order to further understanding of the radical changes in value delivery we focus on the value-net approach, which is characterized by a high degree of clustering and variable path length between different actors. The term customer-perceived value has been at the heart of business thinking in recent decades. One could argue that the idea of different value systems originally stems from the value-chain concept (Porter, 1985). Value is not an unambiguous element in any case. Every company provides its customers with an amount of value 1 The Alliance and Joint Venture database, called SDC Platinum, is provided by Thomson Financial, and it provides information on over 57,000 worldwide alliances and joint ventures. 2 Ucinet is a Windows-based program (see Borgatti et al., 1992).
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(Hughes et al., 1998). However, in the context of the research on value networks, the phrase is used to describe tangible and intangible benefits that different players in value systems (such as end-users) receive from utilizing the product or service. To be successful, a company has to direct its efforts towards creating value for which the customer is ready to pay. A value network combines the advantages of a traditional supply-chain/network and value-chain activities. While the former emphasizes joint efforts in achieving efficiency, the latter focuses more on the value-creating activities. Bovet and Martha (2000), Parolini (1999) and Mo¨ller et al. (2005) use the term value net, for example, while Allee (2002) and Timmers (1999) use value network. Allee (2002) defines the value network thus: ‘‘A value network is any network or web of relationships that generates tangible and intangible value through complex dynamic exchanges between two or more organizations. Any group of organizations engaged in both tangible and intangible exchanges can be viewed as a value network.’’ The right-hand side of the continuum represents the so-called future-oriented value–production system, with radical innovations opening up new business opportunities. Different types of dynamic capabilities are then required—for visioning, innovation, network orchestration, and relationship management. Value needs to be delivered to customers in a way that sustains the profitability of the business while fully meeting the expectations of the different stakeholders. Achieving this return requires the appropriation of value for these stakeholders. Securing a dominant or preeminent position in the market place facilitates such appropriation (Hughes et al., 1998). Dominance can come from the superiority of the product offering, its pricing, striking differentiation, the control of different dynamic capabilities (see Fig. 1), and the ability to exercise power and influence at each stage of the value system. 2.2. The network structure and dynamics A modern economy based on knowledge favors customization, flexibility, rapid response, and the dis-
• Well-known activities • Well-known players • Well-known technologies • Well-known business process
internalization or deconstruction of the value and supply chains (Contractor and Lorange, 2002). Rapid technological changes and the uncertain environment are motivating firms to form alliances and to rely on network relationships. Powell et al. (2005) combine these motives with network dynamics in their extensive research in the biotechnology industry. They found that older, less-closely linked organizations were more likely to fail. On the other hand, Li and Whalley (2002) claim that the transformation from value chains to value networks is evident through structural analysis. For example, a lot of players from other industries have been drawn into the previously neatly defined telecommunications market. This has brought about changes in market positions, strategies and revenue generation. To sum up, it seems that establishing collaborative relationships early enough and expanding them on a regular basis is a key to survival in today’s business world (Powell et al., 2005). However, supply networks like any other type of network often have one or several companies that orchestrate them. This leads us to the structures and structural positions in networks, which may provide many advantages to the focal firms. For example, the number of indirect ties could be a critical source of information and innovation (Gulati and Garguilo, 1999; Ahuja, 2000). This is linked to the theory of weak ties (Granovetter, 1973), according to which the number of ties influences the innovativeness of the network node due to the firm’s connection with its partners, which also brings indirect connections to their partners. These linkages could be seen as information intermediaries that enhance the knowledge and information base of companies, and thus increase their absorptive capacity, i.e. their ability to absorb and utilize external knowledge (see e.g., Cohen and Levinthal, 1990). Furthermore, the locus of innovation is often in networks of learning rather than in individual firms (Powell et al., 1996). In sum, there are several benefits associated with strong structural positions in a network. It is therefore worth analyzing which companies perform well in the industry network. The importance of structural analysis in study ing network dynamics is described in the following:
• Well-known value systems • Changes through incremental modifications
• Stable
Stable system/net
Incremental improvements
• Emerging value nets • Old and new actors • Radical changes • New business logic • Uncertainty about both value activities and actors
Emerging system/net
Fig. 1. The value nets/systems continuum (adapted from Mo¨ller et al., 2005).
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‘‘This linkage between network dynamics and the evolving structure of fields needs to be made in order to make progress in explaining how the behavior of actors or organizations of one kind influences the actions of organizations of another kind’’ (Powell et al., 2005, p. 1139). Later on they highlight the need to find out what types of actors and relationships are most critical in shaping the evolution of the field at particular points of time. Our study follows the principles set out by Powell et al. (2005) and Gay and Dousset (2005) and Coviello (2005), who studied networks from various perspectives. Gay and Dousset (2005) adopt a dynamic network visualization approach, which means that time must be conceptualized in the networks as either discrete or continuous. The visualization could also incorporate: (i) centrality (out degree, betweenness and closeness), (ii) turn-over of nodes (network growth), (iii) patent data, and (iv) technology flows/activities. Powell et al. (2005) mention the following as applicable to the implementation of structural analysis: (i) the dominant forms of the partner organizations, (ii) activities between companies (commercialization, finance, R&D and licensing), and (iii) degree measures. The authors of this study also collected a data set related to R&D investments in order to analyze the relation between network position and investment in internal assets. According to McGahan (2004), it is necessary in constantly changing and developing business environments such as the technology-intensive ICT market to take care of the core value-creating assets and to evaluate how quickly these assets are depreciating. One way of keeping assets valid is to develop products and processes continuously in response to the changing situations in the market. R&D investments are one indicator of this kind of activity. As Powell et al. noted (1996), the locus of innovation is to be found in networks, and we suggest that there are several benefits related to strong network positions. On the other hand, Cohen and Levinthal (1990) claim that R&D creates a new capacity to assimilate and exploit new knowledge, and they used R&D intensity as a predictive measure of innovation activity. They based their arguments on earlier research in which it was found that technical change within an industry was often closely related to the firm’s ongoing R&D activity. Furthermore, they suggest that the firm’s ability to exploit external knowledge is often generated as a byproduct of its R&D. In our view, therefore, there should be a link between R&D orientation and the network positions of the companies in an ICT value network. We applied this definition in our study in order to show that the preconditions of strategic supply-chain relationships ultimately arise from prior R&D investments made inside the firm. Without these investments the learning and knowledge transfer between the partners would be restricted due to cognitive distance (Nooteboom, 2004). The main argument here is that companies have to develop their absorptive capacity constantly and before engaging in effective knowledge sharing in a supply chain, although it may also increase as a result of their alliance relationships.
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In sum, companies have become nodes of series of inter-twined value chains, essentially becoming part of a complex and rapidly evolving value network. In order to survive in this new environment they all need to understand their positions, and to re-evaluate their strategies and business models. As we have seen, there are several ways in which to approach structural network analysis. Our paper focuses on these changes in the dynamic environment of the ICT industry, which is described in more detail in Section 3 below. 3. Findings from the ICT industry 3.1. Empirical data and methods As mentioned above, one of the premises of this study is the fast-growing and knowledge-intensive ICT industry, in which the number of alliances has been steadily increasing and the network structure has been evolving. ICT includes the Information Technology (IT) and Telecommunications (TLC) sectors, and in recent years the media industry has begun to converge with the ICT industry. When the network is analyzed as a whole there are numerous ways of describing the structure of and relations between nodes. We chose a set of 26 companies from different industries operating in the ICT sector (Table 1). These companies represent a variety of actors such as telecommunication manufacturers and operators, IT companies, media companies, Internet service providers, and component manufacturers. All collaborative relationships, including alliances and joint ventures, were included in the study. This data set provides a rough example of the actor structure of the ICT network, and illustrates the different roles. In order to investigate the development and dynamics of the ICT industry we took three different perspectives: (i) The number of alliances and the shift in the alliance activities. (ii) The shift in structural positions (centrality measures). (iii) The impact of R&D inputs. We chose two time frames (1999–2001 and 2003–2005), the former one representing the hype season of the ICT business, and the latter exemplifying the stable stage of the business. The year 2002 was left out of the Table 1 The focal companies in the study AOL AOL TimeWarner Cisco Systems Deutsche Telekom Ericsson Google Hewlett Packard IBM Intel LG Electronics Microsoft Motorola Nokia
NTT DoCoMo Oracle Qualcomm Samsung Electronics Sanmina-SCI SBC Communications Siemens Sony SonyEricsson Telenor TeliaSonera Vodafone Yahoo
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industry. The analysis is conducted with regard to two separate time frames, 1999–2001 and 2003–2005, in order to indicate changes in the structural positions of the companies. The descriptive statistics are given in Table 2. SDC Platinum database and the UCINET program and method were used in the network-structure analysis. UCINET software was originally developed for the purpose of analyzing social networks, and it includes the advanced features and algorithms of network analysis. The analysis is based on the mapping of the relationships and the linkages between the organizations in a network. The objective is to understand and measure the organization’s position, based on its node (company) centrality and its structural position. There are several approaches to measuring centrality in a network. Basically, it could be seen as an indicator of power. Furthermore, it could characterize the different positional advantages or disadvantages. The advantages
analysis to make the difference between these two time frames easier to identify. 3.2. The development of alliance activities The development of alliance activities is shown in Fig. 2 below. It gives, first, the general development in the number of alliances. As expected, the hype season is clearly connected with the higher number, whereas there were fewer in the more stable stage. The figure also shows the type of alliances entered into by the ICT companies. The number of supply-chain and computer-service alliances increased, while those in the traditional telecommunication services fell in number during the research period. This could have been a consequence of the search for efficiency and new business opportunities, which suggests that the industry is still at an immature stage. Furthermore, since telecommunication services represent such a big proportion of the total number of alliances, we split them into more detailed activities. According to the figure (the lower chart), R&D and software development services seemed to increase, while other activities fell. Consequently, it can be assumed that the companies are searching for new business opportunities.
Table 2 Descriptive statistics
Number of relationships
3.3. Network structure
Number of focal firms Number of nodes in a network
Next we will analyze the alliance network of the 26 companies and the changes of power structure in the ICT
Alliance network 1999–2001
Alliance network 2003–2005 (present)
Dichotomized relationships 26 2016
Dichotomized relationships 26 722
Alliance activities
3500
Amount of alliances
3000 2500 Other services Licensing services Computer services Supply chain services Telecomm services
2000 1500 1000
1834
500 402 0
Telecommunication services
100 % Software Dev services Internet services Communications services R & D services Telecomm services Multi-media services
80 % 60 % 40 % 20 % 0% 1999-2001
2003-2005
Fig. 2. The number and nature of alliance activities between 1999–2001 and 2003–2005.
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could lie in having several direct relationships with different actors, for example, or having ties with actors in several relationships. The position of broker between collected groups or clusters in the network may also offer several advantages. Perhaps the most simplified measure of network centrality is degree centrality. This principally measures the number of connections each node in a network has, and calculates the degree and normalized degree. The implication of degree centrality concerns node activity, and it may also indicate a hub position in the network. Another essential tool for analyzing network structure is betweenness centrality, which is used for measuring the structural position of a focal firm between clusters of nodes. It provides insight into the node’s position and its role as a gatekeeper between two independent network components. Furthermore, companies between clusters of nodes may transform this broker role into a power position. On the basis of earlier research on network analysis (Everett and Borgatti 1999), we used Freemans betweenness centrality measure to analyze the shift in the structural position of the selected 26 focal organizations in the case ICT network between 1999–2001 and 2003–2005. The results are shown in Table 3 below.
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Table 3 summarizes the betweenness centrality measures of the studied networks of companies. Not all of them feature in both analyses since they were not central or did not exist previously. The original 26 focal companies are included in the table if they existed and had an above-zero betweenness centrality measure. Following the UCINET analysis the companies were listed in ranked order. It follows that companies in the table present higher-ranked nodes in the analysis beyond the originally selected 26 focal companies. The analysis shows that the structural positions in the network have not changed much, indicating that companies such as Microsoft, IBM and Nokia, which were previously strong in forming alliance networks, have continued to create new alliances. This implies the adoption of dynamic strategies in the re-creation of the ICT industries. Given their strong positions in the betweenness centrality measure, these companies are also seeking to create collaborative relationships at the industry intersections, and thus to position themselves as gatekeepers between company clusters. Interesting companies emerged in the 2003–2005 alliance network. Some of these were new, such as the Internet search company Google. The Internet recruiting company CareerBuilder and the IP telephony solution
Table 3 Betweenness centrality measures of the 26 focal companies’ network Freeman betweenness centralities in the network Alliance network (1999–2001); number of nodes ¼ 2016
Alliance network (2003–2005); number of nodes ¼ 722
Firm
nBetweenness
Firm
nBetweenness
Microsoft Corp. IBM Corp. America Online Inc. Nokia Oyj Motorola Inc. Sony Corp. Oracle Corp. Cisco Systems Inc. LM Ericsson Telefon AB Siemens AG Intel Corp. Samsung Electronics Co Ltd Yahoo! Inc. NEC Corp. Sony Music Entertainment Hewlett-Packard Co. IBM Japan Ltd. AOL Time Warner Inc. Hitachi Ltd. Yahoo Japan Corp. Toshiba Corp. Compaq Computer Corp. NTT DoCoMo Inc. LG Electronics Inc. Motorola Corp. Fujitsu Ltd. Deutsche Telekom AG Telia AB Sonera Oyj Telenor AS Vodafone Group PLC
19.4 15.0 7.5 6.0 6.0 5.4 5.2 5.0 4.9 3.9 3.6 3.2 2.5 2.4 2.0 2.0 1.9 1.8 1.7 1.7 1.6 1.4 1.2 1.2 1.2 1.2 1.0 0.8 0.6 0.6 0.4
Microsoft Corp. IBM Corp. Motorola Inc. Samsung Electronics Co. Ltd. Intel Corp. Cisco Systems Inc. Nokia Oyj Sony Corp. Openwave Systems Inc. Microsoft Network LLC SAP AG EMC Corp. Oracle Corp. NTT DoCoMo Inc Siemens AG Toshiba Corp. LM Ericsson Telefon AB Fujitsu Ltd. Yahoo! Inc. Hewlett-Packard Co. Comcast Corp. CareerBuilder Inc. AOL Time Warner Inc. Google Inc. Apple Computer Inc. Avaya Inc. LG Electronics Inc. Time Warner Inc. Vodafone Group PLC SBC Communications Inc. Deutsche Telekom AG TeliaSonera AB
16.9 12.9 10.4 9.9 8.6 7.1 5.3 4.8 4.3 4.3 3.7 3.5 3.4 3.1 3.0 2.4 2.2 2.0 1.7 1.7 1.6 1.4 1.3 1.3 1.2 1.2 0.7 0.6 0.5 0.5 0.4 0.2
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Table 4 R&D investments and betweenness R&D av 99–01 AOL Cisco Deutsche Telekom Ericsson Google HP IBM Intel Microsoft Motorola Nokia NTT Siemens Sony Vodafone Group PLC Yahoo!
Betw 99–01
2.65 18.69 1.85 16.94
1.80 5.00 1.00 4.90
5.72 5.70 12.15 16.27 12.49 7.85
2.00 15.00 3.60 19.40 1.20 6.00 1.20 3.90 5.40 0.40 2.50
7.52 5.70 0.72 13.20
Table 5 Correlation between R&D investments and Betweenness in 1991– 2001
Betw 99–01 Pearson correlation Sig. (1-tailed) N
Betw 99–01
1
.333 .123 14
.333 .123 14
0.32 14.80 1.17 15.52 6.94 4.47 5.48 13.89 17.03 11.23 10.65 3.26 6.80 6.60 0.56 11.16
1.30 7.10 0.40 2.20 1.30 1.70 12.90 8.60 16.90 10.40 5.30 3.10 3.00 4.80 0.50 1.70
Correlations
R&D av 99–01
14
Betw 03–05
Table 6 Correlation between R&D investments and betweenness in 2003– 2005
Correlations
R&D av99–01 Pearson correlation Sig. (1-tailed) N
R&D av 03–05
1 15
R&D av 03–05 Pearson correlation Sig. (1-tailed) N Betw 03–05 Pearson correlation Sig. (1-tailed) N
R&D av 03–05
Betw 03–05
1
.573 .010 16
16
.573 .010 16
1 16
Correlation is significant at the 0.05 level (1-tailed).
provider Avaya also represent new and emerging business models within ICT. The software company Openwave Systems, the IP access provider Comcast and the computer manufacturer Apple represent more traditional players in the sector, but their strong positions indicate their attempts to broaden their network roles. There are only a few operators with high structural network positions representing the strong regional scope and relatively small operators. Some focal companies such as Telenor, Sanmina-SCI, and Qualcomm dropped from the centrality list because of their low betweenness centrality measure. Qualcomm, for example, is well known for licensing and aggressively protecting its telecommunications digital property rights. It is therefore not surprising that it is structurally strong in the network of collaborative agreements. 3.4. Network position and R&D investment activity In order to analyze the relation between network position and investment in internal assets we collected data related to the R&D investments of selected companies, based on the Thomson One Banker Database (www.thomson.com/financial). Average R&D investments per unit of sales over 3 years were calculated and compared with the network position.
We conducted a preliminary correlation analysis and scatter-plotted the companies’ values on two variables in order to illustrate the positions of the companies on this scale. The data were divided in two sets according to the earlier time frame (1999–2001 and 2003–2005). Table 4 presents the data, and the correlations between the variables in the corresponding time frames are presented in Tables 5 and 6. The correlations are only indicative as the sample size is very limited. As Tables 6 and 7 show, there is some correlation between the figures in the later time frame, and if we compare the R&D investments during 1999–2001 to the network position indicator during 2003–2005. The correlation in the latter table might indicate that that effect of R&D investments required some time to show in a stronger network position. In further illustration of the relation between these two variables a scatter plot is presented in Fig. 3. There seem to be indications that companies investing a lot in R&D also occupy the central positions in their value networks. The scatter plot could be used as a starting point in analyzing the company strategies related to the variables. Seemingly, Microsoft is the clear number one in R&D investments and, correspondingly, has a central position in the value network. On the other hand,
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Table 7 Correlation between R&D investments in 1999– 2001 and betweenness in 2003– 2005 Correlations
R&D av99–01 Pearson correlation Sig. (1-tailed) N
R&D av99–01
Betw 03–05
1
.470 .045 14
14
Betw 03– 05 Pearson correlation Sig. (1-tailed) N
.470 .045 14
1 16
Correlation is significant at the 0.05 level (1-tailed).
Y 03-05 20.0
Betw 03-05
15.0
10.0
5.0
0.0 0
5
10
15
20
Fig. 3. The connection between R&D investments and betweenness, 2003–2005.
operators occupy the lower left-hand corner of the plot with lower R&D investments and a more peripheral network position. It could thus be assumed that companies that are more service intensive do not invest so much in technology development, but exploit available technologies in their business. On the other hand, companies that could be regarded as hardware producers in the ICT industry are at the center of the plot, having relatively high R&D investments but more variation in their network positions. 4. Discussion and conclusions This study reported a range of approaches through which to study network evolution, taking ICT as an illustrative example. Shifts in the general network structure can be explored in terms of alliance activities, via the change in the number of alliances entered into by the actors (operators, communication equipment providers, etc.), for example. On the other hand, it is essential to understand the actors’ power position: it is a question of studying their structural position in the industry alliance
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by applying algorithms from social network theory. There are a lot of possible metrics for analyzing networks. We used average degree of nodes and betweenness centrality. Some metrics describe the network as a whole and can be used to track macro-level dynamics (degree of nodes). Others, such as the betweenness centrality measure, can also be used on the network level in order to further understanding of the relative positions in relation to direct relevant competitors. We have shown that value networks emerge and develop to a large extent through their structure. As far as the theoretical foundations of the development of collaborative alliance networks are concerned, a strong position seems to indicate an external resource orientation. Consequently, it could be assumed that the strategies of these companies focus strongly on the usage of external resources, and that they seek to adapt to the changing environment by developing their offerings and innovations in cooperation with other actors. The drive for this kind of dependency on the usage of external resources could be attributed to standardization and open technological development, or the creation of complementary innovations that are beyond the resources available to single organizations in the industry. One driver could also be ex-ante investments in internal R&D, which could be used further to absorb knowledge from external sources. The results of this paper provide insight into the circumstances and effects of the companies’ structural positions in an ICT industry network. We also compared the firms’ network positions with their internal R&D efforts. Companies in a value network are able to develop their resource-based strategies in line with these two alternative paths. One limitation of our structural network analysis is the number of companies included in the study. The egocentric research approach taken made it impossible to give an absolutely reliable picture of the structural development of the whole ICT industry. Our study does, however, give an indication of and an insight into network-structure development. Further research should include the set of performance metrics in the analysis and take a larger sample of companies. References Ahuja, G., 2000. Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly 45, 425–455. Allee, V., 2002. The Future of the Knowledge: Increasing Prosperity through Value Networks. Elsevier Science, Burlington. Allee, V., 2003. The Future of Knowledge. Increasing Prosperity through Value Networks. Elsevier, Burlington, MA. Borgatti, S.P., Everett, M.G., Freeman, L.C., 1992. Ucinet-Guide-Ucinet for Windows: Software and Social Network Analysis. Analytic Technologies, Harvard. Bovet, D., Martha, J., 2000. Value nets: Reinventing the rusty supply chain for competitive advantage. Strategy and Leadership 28 (4), 21–26. Cohen, W., Levinthal, D., 1990. Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly 35 (1), 128–152. Contractor, F.J., Lorange, P., 2002. The growth of alliances in the knowledge-based economy. International Business Review 11, 485–502.
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Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe
The evolution of the network structure in the ICT sector Jukka Hallikas , Jari Varis, Heli Sissonen, Veli-Matti Virolainen School of Business, Lappeenranta University of Technology, Box 20, FI-53851 Lappeenranta, Finland
a r t i c l e i n f o
abstract
Article history: Received 5 October 2006 Accepted 5 November 2007 Available online 21 June 2008
This study examines the development of collaborative relationships in the Information and Communications Technology (ICT) sector. Companies form close relationships with other companies to access complementary resources, and such links create networks that promote value delivery. This paper provides a theoretical outline of the structural network and alliance research. We also analyze the evolution of the ICT network in terms of position and power relations at different time spans, and provide an overview of the theoretical basis of interfirm networking. The network dynamics and evolution based on structural analysis of the network is clarified by describing the alliance evolution of different actors in a case ICT sector network. We also analyze the actors’ structural position in the industry network by adapting the algorithms for structural network analysis. Finally, we compare the companies’ structural position in the network and their R&D input. Our results indicate that value networks emerge and develop to a large extent through their structure. A strong position in these networks seems to indicate external resource orientation. Furthermore, the results of this paper provide indications about the circumstances and effects of a company’s structural position in an ICT industry network. & 2008 Elsevier B.V. All rights reserved.
Keywords: Supplier networks Supply-chain management Risk management
1. Introduction Interest in industrial networks has increased since various companies and industries have become more dependent on each other. Organizations with complementary strategies, resources and capabilities are joining forces in order to meet the needs of customers in a manner that benefits all participants in the value network. Thus, we have been witnessing the emergence of entire value networks. Our aim in this paper is to examine the theoretical and empirical foundations of the analysis of value-network dynamics in the Information and Communications Technology (ICT) industry. According to Steinbock (2002), the industry has changed from one of closed specifications, central innova-
Corresponding author. Tel.: +358 40 550 7499; fax: +358 5 621 6699.
E-mail address: hallikas@lut.fi (J. Hallikas). 0925-5273/$ - see front matter & 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2007.11.014
tion and domestic marketing to one in which open specifications, distributed innovation and global networking prevail. An example of this kind of networking is the standardization organization, whose role has changed over the years. The reasons for this trend include the high speed of technological change, which increases standardization activities but also makes them non-transparent and complex: there have been institutional changes in standardization, driven by the short development and product life cycles. Whereas traditional standard-setting organizations used to develop standards for the ICT industry, due to the rapid pace of innovation informal consortia have now taken more of a hold on these activities. This phenomenon has been explained through game-theoretical models, for example. It has been found that formal processes are of a higher quality and legitimacy, but are slower to develop than informal processes. On the other hand, the traditional, formal standard-setting organizations may adapt well to meeting the needs of mature industries operating within stable
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markets. In the context of dynamic industries such as ICT, the emphasis of traditional standard-setting organizations on transparent, inclusive processes and broad consensus is not relevant to the needs of dynamic sectors (Winn, 2005). The institutional view in economic literature is often connected to the organization as an institution (Williamson, 1985). More recently, Nooteboom (2004) provided a more comprehensive definition: institutions both constrain and enable action, they are the result of earlier action, they are relatively stable, and they are relatively binding. Furthermore, external change factors affect different institutional levels—the country, the industry, and the organization driving the change. In this study, we consider the ICT industry to be an institution, and explore the impact of inter-firm alliance formation and R&D investments on it. One recent research field focusing on the emergence and evolution of industry dynamics is related to the value network approach (see e.g., Allee, 2003), the original objective of which was to examine the tangible and intangible dynamic interchange between various actors in the industry. These networks often emerge and develop in line with the collaborative relationships of firms within and across industry value chains and networks. Alliance formation and dynamics are therefore essential factors in determining their development. An important element affecting the value distribution in networks is related to the formation of linkages between organizations in various industries. Powell et al. (1996) found that the position in industry-wide strategic networks and the capability of exploiting alliance learning was the locus of innovation. Other studies have also described the causal linkages between network position and innovation outputs, such as patents (Ahuja, 2000). Companies in industry networks may implement different involvement strategies, such as finding a balance between dependency on internal and collaborative resources. Industry networks have a finite duration and thus their own life-cycle. Given the fact that the network structure is dynamic and constantly changing, it is essential to consider their evolution. Ebers (1999) defines inter-organizational networks as institutionalized, recurring, partner-specific exchange relationships of finite (often based on goal accomplishment) or unspecified duration involving a limited number of actors. It follows that individual collaborative relationships lead to the formation and development of network structures. Our study concerns the development of collaborative relationships in the ICT industry. What makes the ICT industry interesting in the global context is that it is currently in a state of continuous and at least partially unpredictable change. Companies form close relationships in order to access complementary resources, and such links create the network that promotes value delivery. Different actors such as service operators and infrastructure manufacturers adopt different strategies for positioning themselves in the industry. This practical consideration provides a strong argument for exploring
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the dynamics of the alliance and network development. In brief, this paper has the following objectives:
To provide a theoretical outline of structural networks and alliance research.
To analyze the evolution of the ICT network in terms of position and power relations over different time spans. In order to outline the theories and methods used in analyzing the development of collaborative relationships, we first provide an overview of the theoretical basis of interfirm networking. There are several relevant theories and approaches to explaining the development of networks. On the other hand, the strategic choice of positioning seems to be one of the fundamental decisions that companies involved in an industry network have to make. We focus our investigation of network dynamics and evolution on a structural analysis of the case ICT network and the alliance evolution of the different actors involved. We consider the nature of alliances through the SDC Platinum database,1 and analyze the actors’ structural positions in the industry network by adapting the algorithms and UCINET software2 developed for the structural network analysis. Finally, we compare the companies’ structural positions in the network and their R&D input. This is a question of indicating the actors’ strategic positioning in terms of their usage of internal (R&D inputs) and external resources (network position). 2. Theoretical considerations 2.1. The context Institutional theorists have described the nature of institutional pressures to achieve conformity and uniformity. Greenwood and Hinings (1996) emphasized the exogenous nature of change, which emanates from the realm of ideas and legitimacy. In the analysis of value networks it is essential to understand the incidence and pacing of radical organizational change, in particular the differences between organizations as they respond to apparently similar contextual pressures. Dynamics have been defined as patterns of value commitment, dissatisfaction with interests, power dependencies, and capacities for action. In order to further understanding of the radical changes in value delivery we focus on the value-net approach, which is characterized by a high degree of clustering and variable path length between different actors. The term customer-perceived value has been at the heart of business thinking in recent decades. One could argue that the idea of different value systems originally stems from the value-chain concept (Porter, 1985). Value is not an unambiguous element in any case. Every company provides its customers with an amount of value 1 The Alliance and Joint Venture database, called SDC Platinum, is provided by Thomson Financial, and it provides information on over 57,000 worldwide alliances and joint ventures. 2 Ucinet is a Windows-based program (see Borgatti et al., 1992).
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(Hughes et al., 1998). However, in the context of the research on value networks, the phrase is used to describe tangible and intangible benefits that different players in value systems (such as end-users) receive from utilizing the product or service. To be successful, a company has to direct its efforts towards creating value for which the customer is ready to pay. A value network combines the advantages of a traditional supply-chain/network and value-chain activities. While the former emphasizes joint efforts in achieving efficiency, the latter focuses more on the value-creating activities. Bovet and Martha (2000), Parolini (1999) and Mo¨ller et al. (2005) use the term value net, for example, while Allee (2002) and Timmers (1999) use value network. Allee (2002) defines the value network thus: ‘‘A value network is any network or web of relationships that generates tangible and intangible value through complex dynamic exchanges between two or more organizations. Any group of organizations engaged in both tangible and intangible exchanges can be viewed as a value network.’’ The right-hand side of the continuum represents the so-called future-oriented value–production system, with radical innovations opening up new business opportunities. Different types of dynamic capabilities are then required—for visioning, innovation, network orchestration, and relationship management. Value needs to be delivered to customers in a way that sustains the profitability of the business while fully meeting the expectations of the different stakeholders. Achieving this return requires the appropriation of value for these stakeholders. Securing a dominant or preeminent position in the market place facilitates such appropriation (Hughes et al., 1998). Dominance can come from the superiority of the product offering, its pricing, striking differentiation, the control of different dynamic capabilities (see Fig. 1), and the ability to exercise power and influence at each stage of the value system. 2.2. The network structure and dynamics A modern economy based on knowledge favors customization, flexibility, rapid response, and the dis-
• Well-known activities • Well-known players • Well-known technologies • Well-known business process
internalization or deconstruction of the value and supply chains (Contractor and Lorange, 2002). Rapid technological changes and the uncertain environment are motivating firms to form alliances and to rely on network relationships. Powell et al. (2005) combine these motives with network dynamics in their extensive research in the biotechnology industry. They found that older, less-closely linked organizations were more likely to fail. On the other hand, Li and Whalley (2002) claim that the transformation from value chains to value networks is evident through structural analysis. For example, a lot of players from other industries have been drawn into the previously neatly defined telecommunications market. This has brought about changes in market positions, strategies and revenue generation. To sum up, it seems that establishing collaborative relationships early enough and expanding them on a regular basis is a key to survival in today’s business world (Powell et al., 2005). However, supply networks like any other type of network often have one or several companies that orchestrate them. This leads us to the structures and structural positions in networks, which may provide many advantages to the focal firms. For example, the number of indirect ties could be a critical source of information and innovation (Gulati and Garguilo, 1999; Ahuja, 2000). This is linked to the theory of weak ties (Granovetter, 1973), according to which the number of ties influences the innovativeness of the network node due to the firm’s connection with its partners, which also brings indirect connections to their partners. These linkages could be seen as information intermediaries that enhance the knowledge and information base of companies, and thus increase their absorptive capacity, i.e. their ability to absorb and utilize external knowledge (see e.g., Cohen and Levinthal, 1990). Furthermore, the locus of innovation is often in networks of learning rather than in individual firms (Powell et al., 1996). In sum, there are several benefits associated with strong structural positions in a network. It is therefore worth analyzing which companies perform well in the industry network. The importance of structural analysis in study ing network dynamics is described in the following:
• Well-known value systems • Changes through incremental modifications
• Stable
Stable system/net
Incremental improvements
• Emerging value nets • Old and new actors • Radical changes • New business logic • Uncertainty about both value activities and actors
Emerging system/net
Fig. 1. The value nets/systems continuum (adapted from Mo¨ller et al., 2005).
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‘‘This linkage between network dynamics and the evolving structure of fields needs to be made in order to make progress in explaining how the behavior of actors or organizations of one kind influences the actions of organizations of another kind’’ (Powell et al., 2005, p. 1139). Later on they highlight the need to find out what types of actors and relationships are most critical in shaping the evolution of the field at particular points of time. Our study follows the principles set out by Powell et al. (2005) and Gay and Dousset (2005) and Coviello (2005), who studied networks from various perspectives. Gay and Dousset (2005) adopt a dynamic network visualization approach, which means that time must be conceptualized in the networks as either discrete or continuous. The visualization could also incorporate: (i) centrality (out degree, betweenness and closeness), (ii) turn-over of nodes (network growth), (iii) patent data, and (iv) technology flows/activities. Powell et al. (2005) mention the following as applicable to the implementation of structural analysis: (i) the dominant forms of the partner organizations, (ii) activities between companies (commercialization, finance, R&D and licensing), and (iii) degree measures. The authors of this study also collected a data set related to R&D investments in order to analyze the relation between network position and investment in internal assets. According to McGahan (2004), it is necessary in constantly changing and developing business environments such as the technology-intensive ICT market to take care of the core value-creating assets and to evaluate how quickly these assets are depreciating. One way of keeping assets valid is to develop products and processes continuously in response to the changing situations in the market. R&D investments are one indicator of this kind of activity. As Powell et al. noted (1996), the locus of innovation is to be found in networks, and we suggest that there are several benefits related to strong network positions. On the other hand, Cohen and Levinthal (1990) claim that R&D creates a new capacity to assimilate and exploit new knowledge, and they used R&D intensity as a predictive measure of innovation activity. They based their arguments on earlier research in which it was found that technical change within an industry was often closely related to the firm’s ongoing R&D activity. Furthermore, they suggest that the firm’s ability to exploit external knowledge is often generated as a byproduct of its R&D. In our view, therefore, there should be a link between R&D orientation and the network positions of the companies in an ICT value network. We applied this definition in our study in order to show that the preconditions of strategic supply-chain relationships ultimately arise from prior R&D investments made inside the firm. Without these investments the learning and knowledge transfer between the partners would be restricted due to cognitive distance (Nooteboom, 2004). The main argument here is that companies have to develop their absorptive capacity constantly and before engaging in effective knowledge sharing in a supply chain, although it may also increase as a result of their alliance relationships.
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In sum, companies have become nodes of series of inter-twined value chains, essentially becoming part of a complex and rapidly evolving value network. In order to survive in this new environment they all need to understand their positions, and to re-evaluate their strategies and business models. As we have seen, there are several ways in which to approach structural network analysis. Our paper focuses on these changes in the dynamic environment of the ICT industry, which is described in more detail in Section 3 below. 3. Findings from the ICT industry 3.1. Empirical data and methods As mentioned above, one of the premises of this study is the fast-growing and knowledge-intensive ICT industry, in which the number of alliances has been steadily increasing and the network structure has been evolving. ICT includes the Information Technology (IT) and Telecommunications (TLC) sectors, and in recent years the media industry has begun to converge with the ICT industry. When the network is analyzed as a whole there are numerous ways of describing the structure of and relations between nodes. We chose a set of 26 companies from different industries operating in the ICT sector (Table 1). These companies represent a variety of actors such as telecommunication manufacturers and operators, IT companies, media companies, Internet service providers, and component manufacturers. All collaborative relationships, including alliances and joint ventures, were included in the study. This data set provides a rough example of the actor structure of the ICT network, and illustrates the different roles. In order to investigate the development and dynamics of the ICT industry we took three different perspectives: (i) The number of alliances and the shift in the alliance activities. (ii) The shift in structural positions (centrality measures). (iii) The impact of R&D inputs. We chose two time frames (1999–2001 and 2003–2005), the former one representing the hype season of the ICT business, and the latter exemplifying the stable stage of the business. The year 2002 was left out of the Table 1 The focal companies in the study AOL AOL TimeWarner Cisco Systems Deutsche Telekom Ericsson Google Hewlett Packard IBM Intel LG Electronics Microsoft Motorola Nokia
NTT DoCoMo Oracle Qualcomm Samsung Electronics Sanmina-SCI SBC Communications Siemens Sony SonyEricsson Telenor TeliaSonera Vodafone Yahoo
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industry. The analysis is conducted with regard to two separate time frames, 1999–2001 and 2003–2005, in order to indicate changes in the structural positions of the companies. The descriptive statistics are given in Table 2. SDC Platinum database and the UCINET program and method were used in the network-structure analysis. UCINET software was originally developed for the purpose of analyzing social networks, and it includes the advanced features and algorithms of network analysis. The analysis is based on the mapping of the relationships and the linkages between the organizations in a network. The objective is to understand and measure the organization’s position, based on its node (company) centrality and its structural position. There are several approaches to measuring centrality in a network. Basically, it could be seen as an indicator of power. Furthermore, it could characterize the different positional advantages or disadvantages. The advantages
analysis to make the difference between these two time frames easier to identify. 3.2. The development of alliance activities The development of alliance activities is shown in Fig. 2 below. It gives, first, the general development in the number of alliances. As expected, the hype season is clearly connected with the higher number, whereas there were fewer in the more stable stage. The figure also shows the type of alliances entered into by the ICT companies. The number of supply-chain and computer-service alliances increased, while those in the traditional telecommunication services fell in number during the research period. This could have been a consequence of the search for efficiency and new business opportunities, which suggests that the industry is still at an immature stage. Furthermore, since telecommunication services represent such a big proportion of the total number of alliances, we split them into more detailed activities. According to the figure (the lower chart), R&D and software development services seemed to increase, while other activities fell. Consequently, it can be assumed that the companies are searching for new business opportunities.
Table 2 Descriptive statistics
Number of relationships
3.3. Network structure
Number of focal firms Number of nodes in a network
Next we will analyze the alliance network of the 26 companies and the changes of power structure in the ICT
Alliance network 1999–2001
Alliance network 2003–2005 (present)
Dichotomized relationships 26 2016
Dichotomized relationships 26 722
Alliance activities
3500
Amount of alliances
3000 2500 Other services Licensing services Computer services Supply chain services Telecomm services
2000 1500 1000
1834
500 402 0
Telecommunication services
100 % Software Dev services Internet services Communications services R & D services Telecomm services Multi-media services
80 % 60 % 40 % 20 % 0% 1999-2001
2003-2005
Fig. 2. The number and nature of alliance activities between 1999–2001 and 2003–2005.
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could lie in having several direct relationships with different actors, for example, or having ties with actors in several relationships. The position of broker between collected groups or clusters in the network may also offer several advantages. Perhaps the most simplified measure of network centrality is degree centrality. This principally measures the number of connections each node in a network has, and calculates the degree and normalized degree. The implication of degree centrality concerns node activity, and it may also indicate a hub position in the network. Another essential tool for analyzing network structure is betweenness centrality, which is used for measuring the structural position of a focal firm between clusters of nodes. It provides insight into the node’s position and its role as a gatekeeper between two independent network components. Furthermore, companies between clusters of nodes may transform this broker role into a power position. On the basis of earlier research on network analysis (Everett and Borgatti 1999), we used Freemans betweenness centrality measure to analyze the shift in the structural position of the selected 26 focal organizations in the case ICT network between 1999–2001 and 2003–2005. The results are shown in Table 3 below.
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Table 3 summarizes the betweenness centrality measures of the studied networks of companies. Not all of them feature in both analyses since they were not central or did not exist previously. The original 26 focal companies are included in the table if they existed and had an above-zero betweenness centrality measure. Following the UCINET analysis the companies were listed in ranked order. It follows that companies in the table present higher-ranked nodes in the analysis beyond the originally selected 26 focal companies. The analysis shows that the structural positions in the network have not changed much, indicating that companies such as Microsoft, IBM and Nokia, which were previously strong in forming alliance networks, have continued to create new alliances. This implies the adoption of dynamic strategies in the re-creation of the ICT industries. Given their strong positions in the betweenness centrality measure, these companies are also seeking to create collaborative relationships at the industry intersections, and thus to position themselves as gatekeepers between company clusters. Interesting companies emerged in the 2003–2005 alliance network. Some of these were new, such as the Internet search company Google. The Internet recruiting company CareerBuilder and the IP telephony solution
Table 3 Betweenness centrality measures of the 26 focal companies’ network Freeman betweenness centralities in the network Alliance network (1999–2001); number of nodes ¼ 2016
Alliance network (2003–2005); number of nodes ¼ 722
Firm
nBetweenness
Firm
nBetweenness
Microsoft Corp. IBM Corp. America Online Inc. Nokia Oyj Motorola Inc. Sony Corp. Oracle Corp. Cisco Systems Inc. LM Ericsson Telefon AB Siemens AG Intel Corp. Samsung Electronics Co Ltd Yahoo! Inc. NEC Corp. Sony Music Entertainment Hewlett-Packard Co. IBM Japan Ltd. AOL Time Warner Inc. Hitachi Ltd. Yahoo Japan Corp. Toshiba Corp. Compaq Computer Corp. NTT DoCoMo Inc. LG Electronics Inc. Motorola Corp. Fujitsu Ltd. Deutsche Telekom AG Telia AB Sonera Oyj Telenor AS Vodafone Group PLC
19.4 15.0 7.5 6.0 6.0 5.4 5.2 5.0 4.9 3.9 3.6 3.2 2.5 2.4 2.0 2.0 1.9 1.8 1.7 1.7 1.6 1.4 1.2 1.2 1.2 1.2 1.0 0.8 0.6 0.6 0.4
Microsoft Corp. IBM Corp. Motorola Inc. Samsung Electronics Co. Ltd. Intel Corp. Cisco Systems Inc. Nokia Oyj Sony Corp. Openwave Systems Inc. Microsoft Network LLC SAP AG EMC Corp. Oracle Corp. NTT DoCoMo Inc Siemens AG Toshiba Corp. LM Ericsson Telefon AB Fujitsu Ltd. Yahoo! Inc. Hewlett-Packard Co. Comcast Corp. CareerBuilder Inc. AOL Time Warner Inc. Google Inc. Apple Computer Inc. Avaya Inc. LG Electronics Inc. Time Warner Inc. Vodafone Group PLC SBC Communications Inc. Deutsche Telekom AG TeliaSonera AB
16.9 12.9 10.4 9.9 8.6 7.1 5.3 4.8 4.3 4.3 3.7 3.5 3.4 3.1 3.0 2.4 2.2 2.0 1.7 1.7 1.6 1.4 1.3 1.3 1.2 1.2 0.7 0.6 0.5 0.5 0.4 0.2
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Table 4 R&D investments and betweenness R&D av 99–01 AOL Cisco Deutsche Telekom Ericsson Google HP IBM Intel Microsoft Motorola Nokia NTT Siemens Sony Vodafone Group PLC Yahoo!
Betw 99–01
2.65 18.69 1.85 16.94
1.80 5.00 1.00 4.90
5.72 5.70 12.15 16.27 12.49 7.85
2.00 15.00 3.60 19.40 1.20 6.00 1.20 3.90 5.40 0.40 2.50
7.52 5.70 0.72 13.20
Table 5 Correlation between R&D investments and Betweenness in 1991– 2001
Betw 99–01 Pearson correlation Sig. (1-tailed) N
Betw 99–01
1
.333 .123 14
.333 .123 14
0.32 14.80 1.17 15.52 6.94 4.47 5.48 13.89 17.03 11.23 10.65 3.26 6.80 6.60 0.56 11.16
1.30 7.10 0.40 2.20 1.30 1.70 12.90 8.60 16.90 10.40 5.30 3.10 3.00 4.80 0.50 1.70
Correlations
R&D av 99–01
14
Betw 03–05
Table 6 Correlation between R&D investments and betweenness in 2003– 2005
Correlations
R&D av99–01 Pearson correlation Sig. (1-tailed) N
R&D av 03–05
1 15
R&D av 03–05 Pearson correlation Sig. (1-tailed) N Betw 03–05 Pearson correlation Sig. (1-tailed) N
R&D av 03–05
Betw 03–05
1
.573 .010 16
16
.573 .010 16
1 16
Correlation is significant at the 0.05 level (1-tailed).
provider Avaya also represent new and emerging business models within ICT. The software company Openwave Systems, the IP access provider Comcast and the computer manufacturer Apple represent more traditional players in the sector, but their strong positions indicate their attempts to broaden their network roles. There are only a few operators with high structural network positions representing the strong regional scope and relatively small operators. Some focal companies such as Telenor, Sanmina-SCI, and Qualcomm dropped from the centrality list because of their low betweenness centrality measure. Qualcomm, for example, is well known for licensing and aggressively protecting its telecommunications digital property rights. It is therefore not surprising that it is structurally strong in the network of collaborative agreements. 3.4. Network position and R&D investment activity In order to analyze the relation between network position and investment in internal assets we collected data related to the R&D investments of selected companies, based on the Thomson One Banker Database (www.thomson.com/financial). Average R&D investments per unit of sales over 3 years were calculated and compared with the network position.
We conducted a preliminary correlation analysis and scatter-plotted the companies’ values on two variables in order to illustrate the positions of the companies on this scale. The data were divided in two sets according to the earlier time frame (1999–2001 and 2003–2005). Table 4 presents the data, and the correlations between the variables in the corresponding time frames are presented in Tables 5 and 6. The correlations are only indicative as the sample size is very limited. As Tables 6 and 7 show, there is some correlation between the figures in the later time frame, and if we compare the R&D investments during 1999–2001 to the network position indicator during 2003–2005. The correlation in the latter table might indicate that that effect of R&D investments required some time to show in a stronger network position. In further illustration of the relation between these two variables a scatter plot is presented in Fig. 3. There seem to be indications that companies investing a lot in R&D also occupy the central positions in their value networks. The scatter plot could be used as a starting point in analyzing the company strategies related to the variables. Seemingly, Microsoft is the clear number one in R&D investments and, correspondingly, has a central position in the value network. On the other hand,
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Table 7 Correlation between R&D investments in 1999– 2001 and betweenness in 2003– 2005 Correlations
R&D av99–01 Pearson correlation Sig. (1-tailed) N
R&D av99–01
Betw 03–05
1
.470 .045 14
14
Betw 03– 05 Pearson correlation Sig. (1-tailed) N
.470 .045 14
1 16
Correlation is significant at the 0.05 level (1-tailed).
Y 03-05 20.0
Betw 03-05
15.0
10.0
5.0
0.0 0
5
10
15
20
Fig. 3. The connection between R&D investments and betweenness, 2003–2005.
operators occupy the lower left-hand corner of the plot with lower R&D investments and a more peripheral network position. It could thus be assumed that companies that are more service intensive do not invest so much in technology development, but exploit available technologies in their business. On the other hand, companies that could be regarded as hardware producers in the ICT industry are at the center of the plot, having relatively high R&D investments but more variation in their network positions. 4. Discussion and conclusions This study reported a range of approaches through which to study network evolution, taking ICT as an illustrative example. Shifts in the general network structure can be explored in terms of alliance activities, via the change in the number of alliances entered into by the actors (operators, communication equipment providers, etc.), for example. On the other hand, it is essential to understand the actors’ power position: it is a question of studying their structural position in the industry alliance
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by applying algorithms from social network theory. There are a lot of possible metrics for analyzing networks. We used average degree of nodes and betweenness centrality. Some metrics describe the network as a whole and can be used to track macro-level dynamics (degree of nodes). Others, such as the betweenness centrality measure, can also be used on the network level in order to further understanding of the relative positions in relation to direct relevant competitors. We have shown that value networks emerge and develop to a large extent through their structure. As far as the theoretical foundations of the development of collaborative alliance networks are concerned, a strong position seems to indicate an external resource orientation. Consequently, it could be assumed that the strategies of these companies focus strongly on the usage of external resources, and that they seek to adapt to the changing environment by developing their offerings and innovations in cooperation with other actors. The drive for this kind of dependency on the usage of external resources could be attributed to standardization and open technological development, or the creation of complementary innovations that are beyond the resources available to single organizations in the industry. One driver could also be ex-ante investments in internal R&D, which could be used further to absorb knowledge from external sources. The results of this paper provide insight into the circumstances and effects of the companies’ structural positions in an ICT industry network. We also compared the firms’ network positions with their internal R&D efforts. Companies in a value network are able to develop their resource-based strategies in line with these two alternative paths. One limitation of our structural network analysis is the number of companies included in the study. The egocentric research approach taken made it impossible to give an absolutely reliable picture of the structural development of the whole ICT industry. Our study does, however, give an indication of and an insight into network-structure development. Further research should include the set of performance metrics in the analysis and take a larger sample of companies. References Ahuja, G., 2000. Collaboration networks, structural holes, and innovation: A longitudinal study. Administrative Science Quarterly 45, 425–455. Allee, V., 2002. The Future of the Knowledge: Increasing Prosperity through Value Networks. Elsevier Science, Burlington. Allee, V., 2003. The Future of Knowledge. Increasing Prosperity through Value Networks. Elsevier, Burlington, MA. Borgatti, S.P., Everett, M.G., Freeman, L.C., 1992. Ucinet-Guide-Ucinet for Windows: Software and Social Network Analysis. Analytic Technologies, Harvard. Bovet, D., Martha, J., 2000. Value nets: Reinventing the rusty supply chain for competitive advantage. Strategy and Leadership 28 (4), 21–26. Cohen, W., Levinthal, D., 1990. Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly 35 (1), 128–152. Contractor, F.J., Lorange, P., 2002. The growth of alliances in the knowledge-based economy. International Business Review 11, 485–502.
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