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2007bai7644.doc

  1. 1. EXPLORATION OR EXPLOITATION: NETWORKING STRATEGIES IN CONTEMPORARY BUSINESS MODELS OF SMES Mika Westerlund*, Helsinki School of Economics, P.O.Box 1210, FI-00101 Helsinki, Finland mika.westerlund@hse.fi Sami Kajalo, Helsinki School of Economics, P.O.Box 1210, FI-00101 Helsinki, Finland sami.kajalo@hse.fi Seppo Leminen, Laurea University of Applied Sciences, Vanha maantie 9, FI-02650 Espoo, Finland seppo.leminen@laurea.fi Petteri Pohto, LTT Research Ltd., Unioninkatu 18, FI-00130 Helsinki, Finland petteri.pohto@ltt.fi ABSTRACT In this study we focus on the business models of small and medium sized firms that operate in networks of business actors. Our study contributes to the ongoing discussion on the relationship between business networks and business models by specifying the relationship strategies as drivers for inter-organizational networking and considering their role in the business model of a firm. Based on the explorative factor analysis method utilized in our empirical analysis of 91 firms, we analyze four diverse networking strategies to act in a business network. Furthermore, we use the cluster analysis method in regard to these strategy choices to identify three groups of firms that represent distinctive types of business models, which describe how the strategies are manifested and employed to cope in turbulent business environment. These business models are: customer oriented exploration, subcontracting oriented partnering, and process oriented exploitation. Grounded on the analysis, we argue that these business models are tied with the exploration or exploitation perspective to the resources. Firms attempt to either emphasize exploration strategy in seeking effectiveness or exploitation strategy in seeking efficiency of operation, or, they attempt to balance between the two. Keyword: Network, Strategy, Business model, SME *) Corresponding author Note: This study is part of the Liike 2 programme of the Academy of Finland.
  2. 2. INTRODUCTION Networking is an integral market phenomenon describing contemporary business practices. Emerging networks of companies and organizational actors alike are rapidly seen to replace traditional markets and vertically integrated hierarchical firms (Castells, 1996; Parolini, 1999; Hinterhuber, 2002). Concurrently, business model innovation is claimed to become the basis of competitive advantage, and the key to creating successful business models for the knowledge economy lies in understanding the dynamics of business networks (Tapscott et al., 2000; Allee 2000; Timmers, 2003). Companies enter complex business relationships in order to exploit and develop their resources, and to create and maintain the basis for their competitive advantage (Hung, 2002). In addition, business networks enable them to adapt rapidly to market flux and combine knowledge and capabilities beyond their own possession in order to create new business opportunities. Major approaches in prior research of business models focus on either identifying their types or their elements. Some studies take an apparently hierarchical perspective on the types of business model by investigating the firms’ positions in their vertical value chains (see e.g. Timmers, 1999; Timmers, 2003). Conversely, others address the specific internal and external elements that construct the explicit business model of a firm (e.g. Osterwalder, 2004; Morris et al., 2005). For example, Betz (2002) claims that generic business models can be constructed with inputs and outputs to the business, and the useful inputs and outputs relate to resources, sales, profits, and capital. Furthermore, he argues that instead of emphasizing economic aspects in their analysis, the elements of business models should be considered more strategy-driven choices of conducting current and future business. As a key strategic aspect, business models describe the choice of partners and the construct of the business network including multiple actors (Alt and Zimmerman, 2001; Timmers, 2003; Morris et al., 2005, Westerlund and Rajala, 2006). However, research lacks extensive insights on the firms’ strategy formulation and its implementation in their business models from the inter-organizational network perspective. In this study we focus on the business networking of small and medium sized firms (SMEs). Specifically, our objective is to further understand the underlying networking strategies that act as drivers or organizational guidelines for operating in the business networks. We see that these kind of strategic choices are manifested and employed in the form of explicit business models of a firm in order to cope in turbulent business environment. This is especially relevant considering the limited resource base of SMEs, which require specializing to specific core competences and lead to dependence of the external resources and skills in other areas of business. Thus, we are interested in what are the key networking strategies of small- and medium sized companies and how they are manifested through different kinds of business models in industrial SME context. We attempt to contribute to these issues, and thus, to the ongoing academic discussion on the relationship between networking strategy and business models, by employing statistical analyses in the empirical data of industrial SMEs. Based on the explorative factor analysis method utilized in our sample of 91 firms, we identify four diverse operating strategies as drivers to act in a business network. These networking strategies are interpreted as: 1) innovative customer orientation, 2) strategic network
  3. 3. orientation, 3) subcontracting as the strategic focus, and 4) seeking efficiency in operation. Furthermore, we use the cluster analysis method in regard to these strategy choices to identify three groups of firms that represent different business models. A fundamental nature in these distinctive business models is the firm’s reactive or proactive business orientation in the sense of seeking either efficiency or effectiveness in their operation, or attempting to balance between the two. Thus, the identified models retain variously to the issue of exploration and exploitation. They are described as:  Customer oriented exploration;  Subcontracting oriented partnering; and  Process oriented exploitation. The structure of our paper is as follows: After this introductory section, we proceed to discuss the theoretical foundations on both inter-organizational networks and business models of a firm. Then, we consider the methodological issues concerning our empirical data and present the findings from our statistical analyses. Finally, we conclude the study by discussing the theoretical implications of our study, and suggest some potential avenues for future research. THEORETICAL BACKGROUND The essence of business networking Business networks are increasingly seen as the source of distinctive competitive advantage to companies (Jüttner and Schlange, 1996; Möller and Halinen, 1999; Gulati et al. 2000; Möller et al., 2005). Dependence on network relationships and the effective maintenance of such networks will dictate the core survival strategy of firms in the global economy (Chapman et al., 2002). The network approach stems from the idea that each company in the market has a number of different relationships with customers, suppliers and other business actors alike. The basic form – called a dyad in the literature – is a focal relationship between two actors, and complex networks are defined to consist of relationships between more than three actors (Anderson et al., 1994). These networks are sets of relationships between firms, where companies engage in multiple two-way relationships to bring increasingly complex products and services to the market (Aldrich, 1998). Companies are able to enhance their effectiveness and competitive advantages through the network. Actors’ interests, perceptions and positions are interesting subjects in the network context. These issues have been of key concern in the research literature on networks from the perspective of industrial network approach (INA) since the early 1980s (Easton, 1992; Håkansson and Snehota, 1995; Ford, 1997). According to Håkansson and Johanson (1992), network actors have five different characteristics: they 1) perform and 2) control activities that are based on 3) control over resources and 4) develop relationships with each other 5) through exchange processes. Furthermore, Holmlund and Törnroos (1997) point out that the actors are goal-oriented; they act in order to make economic gain and to increase their control over the value-creating network. Actors have differential knowledge about activities, resources, and other actors in the network, and they act as information sources as well as provide opportunities for identifying new business alternatives.
  4. 4. Actors make constant efforts to achieve better position in the network. This is driven by the attempt to ensure their potential for future business and enabled by the direct control over critical resources (Johanson and Mattson, 1992). Firms have typically either central or partner position in their value-creating networks, and the hub-firm with a central position is argued to have more control over the directions of the network and on the activities of the other actors (Håkansson and Snehota, 1990; Doz and Hamel, 1998; Barabasi, 2002). The importance of possession or access to key resources becomes obvious especially when firms aim to improve the current operation of the network, and in the case of new business development. Generally, prior research literature (e.g. Håkansson and Snehota, 1990; Gupta et al., 2006) acknowledges two distinctive approaches that describe actors’ interest towards resources: the exploration strategy and the exploitation strategy. Exploration and exploitation approaches have a fundamental difference between them. Exploration refers to firms’ capturing of resources through activities characterized by search, variation, risk taking, experimentation, play, flexibility, discovery, and innovation (March 1991). It is a flexible future-oriented process, which adapts itself to the new configuration the firm can discover and arises from individual deviance as a source of innovation in networks (Nooteboom, 1999). Conversely, exploitation includes refinement, choice, production, efficiency, selection, implementation, and execution as approaches in resource capturing. It consists of a refinement of existing technology in order to strengthen the excellence in present operation. The exploitative firm sustains a price competition with a high level profit objective. Thus, exploitation requires routines and coordination for an efficient management of the activities (Nooteboom, 1999). In addition to these two strategies, a number of authors (e.g. Gupta et al., 2006) have suggested that firms should engage in both exploration and exploitation mechanisms, because the organization is devoted to exploration when dealing with exploitation simultaneously. This is generally referred as the attempt to create organizational ambidexterity (Tushman and O’Reilly, 1996; Gupta et al., 2006). Finally, in order to understand networks, it is essential to discuss the nature of relationships. The INA (see e.g. Håkansson and Johansson, 1992; Ford et al., 1998; Ford et al., 2002) considers business relationships in terms of involvement, where low-involvement relationships are handled with limited co-ordination, adaptation and interaction. Conversely, the high-involvement perspective to relationships includes more co-ordination and adaptation that create interdependency between the actors. The nature and role of inter-firm relationships has been shown to influence on the business strategy from the perspective of business networks (Wilkinson and Young, 1994; Håkansson and Snehota, 1990). In sum, the theory of industrial networks provides us with dimensions to distinguish between different types of network relationship strategies in terms of the nature of relationship, the position of a firm in its value-creating network, and the future-perspective in respect to objectives in the network. These dimensions are used in the analysis of our empirical data. Conceptual foundations of business models The recent trend in business and academic discussions alike has been the emergence of the concept of business model. For example, Germany and Muralidharan (2001)
  5. 5. claim that efficient value capturing needs continuous business model innovation centered on customer needs. Moreover, Allee (2000) and Chapman et al. (2002) claim that the key to creating successful business models for the contemporary knowledge- economy lies in understanding the dynamics of value-creating networks. A key issue in these arguments is the understanding and definition of what business models are. Westerlund and Rajala (2006) point out that management research literature uses the concept of business model to refer to ways of creating value for customers and to show how a business turns market opportunities into profit through the agency of sets of actors, activities and collaboration. Thus, the business model of a firm spells out how the company makes money by specifying where it is positioned in its value- creating network (Timmers, 2003). Describing their idea of the business model of a firm, Tapscott and Gaston (1993, 202-203) emphasize a managerial vision of how business will function profitably and competitively. They decompose firms’ business functions into internal components of the value-creating network and stress the management control view of the business. This perspective is supported by e.g. Tikkanen et al. (2005) in their notion on the importance of managerial cognition in business models. Slywotzky (1996, 4), in turn, discuss business models through the idea of business design, which is the totality of how a company selects its customers, defines and differentiates its offerings, addresses the tasks it will perform for customers, and captures profit from its operation. In this sense, it is the entire system for delivering utility to customers and earning profit from that activity. Companies may offer products or technology, but their offering is embedded in a comprehensive system of activities and relationships that represent the company’s business design. Also Barabba (1998, 34-59) accentuates the network approach and concludes that activities and relationships are central to business models. The concept of business model is also closely related to business strategy. Business model has a position as a conceptual and theoretical layer between strategy and process (Osterwalder, 2004; Morris et al., 2005; Tikkanen et al., 2005). It incorporates some elements of business strategy and aims at describing the business as the manifestation of that strategy as well as an abstraction of actual business operations (Rajala et al., 2003; Seddon and Lewis, 2003; Morris et al., 2005; Westerlund and Rajala 2006). A typical objective is to describe different types of business model. Brandenburger and Stuart (1996) identify four value-based business strategies that companies use to seek competitive advantage. These strategies are: (1) the classic differentiation strategy, (2) lowering opportunity cost to suppliers of providing resources to the firm, (3) lowering willingness-to-pay of buyers to other firms’ products, and (4) raising the opportunity cost to suppliers of providing resources to other firms. Lowering opportunity cost to suppliers closely relates to the prescription, that companies should establish value-managed partnerships with their suppliers. However, the contemporary inter-organizational network perspective with multiple business actors has complexity-related implications on the firms’ business models, and already Håkansson and Snehota (1990) called for more extensive insights on the firms’ strategy formulation and its implementation in the network context. These notions are of key interest in our empirical analysis of the networking strategies and their manifestation through business models of small and medium sized companies.
  6. 6. RESEARCH METHODOLOGY For the purposes of our empirical research, we collected a set of quantitative data through an online survey. The survey was conducted over a period of three months in the early 2005 and it was aimed at the CEOs and Chairmen of the board of knowledge- and production-intensive small and medium-sized companies in Finland. The SMEs were defined as firms having between 50-500 employees. We sent an e-mail to 1000 managers whose contact information was obtained from a commercial database. A total of 91 managers filled our questionnaire which yielded at a response rate of 9.1%. As expected, almost all of the respondents turned out to be firms operating in the industrial markets and the median for the size of the respondents was 100 employees. The questionnaire contained 12 attributes that addressed the three themes of interest concerning the focus of our study. The themes or specific constructs in our questionnaire were: 1) the nature of collaboration, 2) firm’s position in the network, and 3) their expected networking strategy in the future. We believe that these constructs, which are drawn from the literature in our theoretical discussion, enable us to reveal companies underlying networking strategies that are essential in order to identify their business models (see Appendix 1). All items were measured on a four- point Likert scale and respondents were provided with a possibility to pass the question without giving their opinion. Factor analysis Factor analytic methods range from confirmatory techniques to pure exploratory procedures (Tucker and MacCallum 1997, 145). The exploratory factor analysis (EFA) seeks to uncover the underlying structure of a relatively large set of variables. There are no prior theory-driven hypotheses to confirm through the empirical data, and factor loadings are used to merely intuit the factor structure of the data. This is the most common form of factor analysis, where the researcher's à priori assumption is that any indicator may be associated with any factor. To conform with the notion by Costello and Osborne (2005), which concerns the most preferred type of EFA, we use the principal components analysis (PCA) with orthogonal (varimax) rotation as the method for the data analysis. Furthermore, we decided the number of factors to be retained for rotation on the basis of the Kaiser criterion, according to which all factors with Eigenvalues greater than one are feasible. There are several views on the minimum number of cases required for the factor analysis. Hair et al. (2006, 112-113) suggest that the minimum absolute sample size should be 50 observations and preferably the sample size should be 100 or larger. Generally, an adequate number of cases is suggested to vary between 100 and 300 (Gorsuch, 1983; Hatcher, 1994; Hutcheson and Sofroniou, 1999; Norušis, 2005, 400). Also, as a general rule, the minimum is to have at least five times as many observations as the number of variables to be analyzed (Bryant and Yarnold, 1995;
  7. 7. Hair et al., 2006, 112-113). In our data consisting of 91 cases and 12 variables this subjects-to-variables ratio equals to 7.58. Thus, although the number of our cases is low compared to what is suggested by some authors, we see that the results from our analysis based on PCA – in order to identify the underlying factors that act as drivers or strategies for networking – have sufficient explanatory power. Cluster analysis Firms’ strategies are manifested through their business models, which are abstractions of the business at a specific moment of time (Seddon and Lewis, 2003). In addition to identifying the strategies for networking, we use the cluster analysis method to identify the main types of business models that describe the implementation of these strategies. The objective of cluster analysis is to group objects based on the characteristics they possess so that there is a greater similarity between units within groups than between units in different groups (Everitt, 1993; Hair et al., 2006, 555-628; Klastorin, 1983, 92). Shortly described, cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise (Saunders, 1994). Given the above, cluster analysis can be used to discover structures in data without providing an explanation or an interpretation, and cluster analysis simply discovers structures without explaining why they exist. Cluster analysis begins by formulating the clustering problem by defining the variables on which the clustering will be based (Hair et al., 2006, 555-628). In our study, we established these variables through the preceding exploratory factor analysis. In the clustering procedure, we relied on the increasingly popular K-means reassignment method, which splits a set of objects into a selected number of groups by maximizing between-cluster variation relative to within-cluster variation (Punj and Stewart, 1983; Steinley, 2006). However, in non-hierarchical clustering such as this method is, the decision on the number of clusters has to be made in advance. Multiple trials showed that the reasonable number of clusters regarding the analysis of our data would be three, where the relative distribution of firms in each cluster is seemingly balanced. Although one cluster includes less objects (23) in comparison with the other two clusters (32 and 36, respectively), yet it seems to provide a viable choice where within-cluster distances are fair. After implementing the clustering procedure according to the method presented above, the derived clusters are interpreted and profiled in terms of the variables used. This involves examining the cluster centroids and enables assigning each cluster a description or a label. Usually, as the result of a K-means clustering analysis, one should examine the means for each cluster on each dimension to assess how distinct the clusters are. Ideally, the result of this is obtaining different means for most, if not all dimensions, used in the analysis. The magnitude of the F values another indication of how well the respective dimension discriminates between clusters. The three clusters that result from clustering the four factors in our analysis are labeled on the basis of the characteristics of the factors that have the highest means within the cluster.
  8. 8. EMPIRICAL RESULTS We conducted a factor analysis for the data (PCA with varimax rotation; values suppressed at the minimum loading 0.20). In order to rely on the results of the analysis, we first investigated the required test values for the goodness of the method. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy tests whether the partial correlations among variables are small. Described in other words, it is an index for comparing the magnitudes of the observed correlation coefficients to the magnitudes of the partial correlation coefficients. Large values for the KMO measure indicate that using a factor analysis for the variables is an adequate method, and a value greater than 0.50 is considered desirable (Malhotra and Birks, 2007, 651). The KMO was 0.627 in our study, and thus exceeded the recommended level. In addition, the Bartlett's test of sphericity is significant (p<0.01), indicating that the strength of the relationship among variables is strong. To conclude, Kaiser-Meyer-Olkin (KMO) measures and Bartlett's sphericity test showed that the sample met the criteria for factor analysis. Factors representing strategies that drive firms’ networking Results of our factor analysis after the orthogonal rotation revealed four underlying patterns that are identified as different strategies or drivers for companies’ networking policy. According to Costello and Osborne (2005) a factor with fewer than three items is generally considered weak and unstable, and five or more strongly loading items are desirable and indicate a solid factor. The factors from our empirical analysis seem to conform with this notion relatively well. Moreover, all of these factors had an Eigenvalue of more than one, and the four-factor solution explained 60 percent of the cumulative variance. Factor analysis and the interpretation of factors are presented in Table 1. Table 1. Factor loadings and interpreting the factors Innovative customer orientation Factor 1 h2 We will focus on customer relationships .859 .753 We will focus on providing high value-added to our customers .844 .738 We will focus on developing innovative service concepts .715 .619 We will take the active hub role and strengthen our position in .492 .388 the network We manufacture and provide (whole) products and services .282 .582 We provide large system solutions /service modules to .242 .347 manufacturers We do not collaborate with other companies on a long-term basis -.251 .503 Strategic network orientation Factor 2 We have mostly network-like collaboration with multiple .865 .787 companies We also have horizontal collaboration with our competitors .703 .549 We will take the active hub role and strengthen our position in .310 .388
  9. 9. the net We mostly have dyadic collaboration with other companies -.645 .525 We do not collaborate with other companies on a long-term basis -.479 .503 Subcontracting as the strategic focus Factor 3 We provide products/services completely on subcontracting .799 .640 principle We provide large system solutions/service modules to .510 .347 manufacturers We have mostly dyadic collaboration with other companies .287 .525 We have horizontal collaboration with our competitors .224 .549 We manufacture and provide (whole) products and services -.701 .582 We will take the active hub role and strengthen our position in -.213 .388 the net Seeking efficiency in operation Factor 4 We will focus on our current products, technologies and .893 .805 customers We will focus on developing innovative service concepts .303 .619 We do not collaborate with other companies on a long-term basis -.459 .503 To summarize, the dimensions according to which the firms in our data describe their strategies for networking with other business actors are: 1. Innovative customer orientation; 2. Strategic network orientation; 3. Subcontracting as the strategic focus; and 4. Seeking efficiency in operation. The four factors derived from our analysis are distinctive by their essence. The factor reflecting “innovative customer orientation” is clearly customer-focused in the product and service production. Another aspect is the recognition of the importance of the business network and the firms’ hub position in it. Similarly, the factor interpreted as “strategic network orientation” includes long-term relationships between multiple actors. However, this factor emphasizes partner relationships in contrast to the customers in the case of previous factor. An opposite direction is underlying in the factor labeled as “subcontracting as the strategic focus”. Here the motive for companies is to engage dyadic relationships in a partner position. Finally, the fourth factor, identified as “seeking efficiency in operation”, reveals us the philosophy of focusing on current business and transactional relationships with short-term incentives. These factors provide us with the perspective to small and medium sized firms’ approaches of conducting business in terms of network relationship strategy. Clusters of firms reflecting differentiation of business models After identifying the different strategies through the factor analysis, these standardized variables were subjected to K-means cluster analysis. The purpose was
  10. 10. to identify the diverse types of business models that manifest the implementation of these strategies. As a follow-up analysis of industrial SMEs, based on the strategic networking orientation, we formed homogeneous groups of firms operating in a networked business environment. The result of clustering was a three-group solution that can be logically interpreted in terms of firms conducting distinctive business strategies in their operation. ANOVA results indicate Innovative customer orientation (F=53.86, df=88, p=0.00), Strategic network orientation (F=3.02, df=88, p=0.05), Subcontracting as the strategic focus (F=37.62, df=88, p=0.00), and Seeking efficiency in operation (F=6.30, df=88, p=0.00) were significant contributors to the cluster solution. We interpret the solution as: 1) customer oriented exploration, 2) subcontracting oriented partnering, and 3) process oriented exploitation. The identified groups are presented in Table 2. Table 2. Cluster centers of firm groups Group 1 Group 2 Group 3 (n=23) (n=36) (n=32) Innovative customer orientation .910 .259 -.945 Strategic network orientation -.402 .243 .016 Subcontracting as the strategic focus -.753 .821 -.382 Seeking efficiency in operation -.609 .216 .195 DISCUSSION AND CONCLUSIONS In order to draw implications from our analysis, the resulted clusters need descriptive labels. Grounded on the cluster analysis, we identify Group 1 as “customer oriented exploration” where the innovative customer orientation factor becomes extremely topical with a positive value of 0.910 and the lack of subcontracting interests with a negative value of -0.753. Similarly, we label the Group 2 as “subcontracting oriented partnering” due to that according to the statistical analysis the factor reflecting subcontracting as the strategic focus is highly relevant with a positive value of 0.821. Finally, Group 3 is interpreted as “process oriented exploitation” where innovative customer orientation has a high negative value of -0.945. Another controversy with the Group 1 is the positive orientation (0.195) to seeking efficiency in operation. These three groups reflect the manifestation of strategy related to networks and relationships in companies’ business models. Their implications for theory and practice are discussed in more detail in the following. Customer oriented exploration (Group 1) The first cluster reflects strong focus on exploration through the network of business actors. Especially, companies in this group concentrate on developing and manufacturing innovative and value-adding product and service concepts with the emphasis on their customer relationships. We interpret this emphasis as an indicator that these firms operate closely in the customer interface and are the actual owners of
  11. 11. the end-customer base. A fundamental idea is to seek learning and new business opportunities through the exploration of knowledge and resources in the market and the network, and to ensure the firms’ future business potential through continuous actions in searching for customer-focused services and innovations. The firms of this group consciously avoid becoming the role of subcontractor or a minor partner in the network of actors, and they aim to strengthen their positions in the value-creating network. The active role of hub or central company in the network provides them with the resources required in seeking effectiveness – not efficiency – in customer relationships as their primary business model. However, these kind of companies are rather reactive than proactive by nature in new business development in a sense that they concentrate on fulfilling increasing customer needs, and do not attempt to influence the market through the launching of radical systemic partner network -driven innovations. Subcontracting oriented partnering (Group 2) Small and medium sized firms in the second cluster aim at taking the role of subcontractor. This reflects the strategic choice of becoming a partner in the network of multiple business actors. An interesting issue is, however, that they may simultaneously attempt to strengthen their position in that network. According to our understanding, this seemingly paradoxical act is due to that these firms make efforts to improve their visibility and share in the market as a strong and well-known key supplier. Their primary customers are large hub-firms of the network that need modular system and service solutions through trustable dyadic relationships in the production of offerings to end-customers. Naturally, excellence in the implementation of this strategy requires not only innovativeness and proactiveness in surviving the emerging challenges in dynamic environmental flux, but also reactiveness to decreasing profits due to the fierce price-competition. On the basis of our analysis, the fundamental idea in the business models of the companies in this cluster is that they attempt to continuously balance the exploration and exploitation of resources through the relationships and networks. Thus, their business models emphasize organizational ambidexterity, i.e. seeking both efficiency and effectiveness in the production and business development in order to create competitive advantage. Process oriented exploitation (Group 3) In the third cluster, companies are focused on improving their current business processes. A distinctive feature here is the high negative orientation towards innovativeness and emerging customer needs. On one hand, these companies do not attempt to become a partner-role in the value-creating network, but more a distinguished actor with some control opportunities in regard to enhancing the value- creation process and managing the other actors in the network. As the focus is on current products, services and customers, efficient production under increasing price- competition requires low production costs through-out the whole value-creating network. Controlling actions and the independence of any specific actors become key issues, as relationships of these companies are typically short-term and transcaction -based by nature. Thus, the structure of network needs to be flexible and dynamic, in order to ensure the changeability of partners during the mission of creating lower-cost offerings. On the other hand, these production- and process oriented companies show some innovativeness in utilizing partners in networks to produce improved offerings
  12. 12. to customers through value-added services. In general, the business models of firms in this category reflect a strong focus on seeking efficiency of operation through the exploitation of resources possessed by the actors in the network. To summarize the results from our empirical analysis, we identified three groups of firms that represent distinctive business models. The identified models manifest the networking strategies that act as drivers for the operation of small and medium sized companies in the network of multiple actors. We argue that these business models are tied with the exploration or exploitation perspective to the resources. Firms attempt to either emphasize the exploration strategy in seeking effectiveness in customer- orientation or the exploitation strategy in seeking efficiency of process and production operations. Or, they may attempt to balance between the two. We believe that despite our focus on limited group of small and medium sized firms representing various industries and business sectors, our results can be generalized to a larger population of business actors in the market. They contribute to the discussion on business networks by showing the different strategies that companies use as a driver for networking with other business actors. Moreover, the strategy and business model literature can benefit from our findings through the identification of specific types of business model that reflect the firms’ strategy from the relationship perspective. Finally, there are some limitations to consider concerning the present study. First, we are aware of the problems related to the use of small samples in statistical analyses. Although we could find some significant differences within our data, in future studies one should opt for a larger sample of observations enriching the analysis. Second, our analysis focuses primarily on the relationship element of the business model. There are, however, differing views on which elements comprise the business model of a firm. Thus, another avenue for further research would be examining the types of business model from based on other perspective than relationships. APPENDIX 1 List of constructs and their items used in the questionnaire. Nature of collaboration  We do not collaborate with other companies on a long-term basis  We have mostly dyadic collaboration with other companies  We have mostly network-like collaboration with multiple companies  We have horizontal collaboration with e.g. our competitors Position in the network  We manufacture and provide (whole) products and services; Principal; we sell end-products and services; are responsible for the development and manufacturing of offerings; and act as principal to system solution and service module providers and subcontractors.
  13. 13.  We provide large system solutions or service modules to manufacturers; System solution provider; we provide large system solutions or service modules to manufacturers; or manufacture end-products and services to principal).  We provide products or services completely on subcontracting principle; Subcontractor; our products and services are completely or partly integrated into the offerings of our principals. Networking strategy in the future  We will increasingly take the active hub role in the network; We aim strongly at acting as the central company of the network; our role and operation will be mainly similar than currently, but we will strengthen our position in the network.  We will focus on high value-added to our customers; We will provide and operate on larger whole-product and service concepts.  We will focus on gaining direct customer relationships; We will provide whole-products and services directly to customers and aim to intensive management of the customer interface.  We will focus on developing innovative service concepts; The role of specific innovative services will increase remarkably in our future business.  We will focus on our current products, technologies and customers. The response options ranged from 1, “Strongly disagree“ to 4, “ Strongly agree“. In addition, each item had a “pass on” option labeled as “I do not know” REFERENCES Aldrich, D. 1998. The New Value Chain, Informationweek, Iss. 700, 278-281. Allee, V. 2000. Reconfiguring the Value Network, Journal of Business Strategy, Vol. 21(4). Anderson, J.C., Håkansson, H. & Johanson, Jan 1994. Dyadic Business Relationships Within a Business Context, Journal of Marketing, Vol. 58(Oct), 1-15. Alt, Rainer and Zimmerman, Hans-Dieter 2001. Preface: Introduction to Special Section – Business Models, Electronic Markets, Vol. 11(1): 3-9. Anderson, J., Håkansson, H. & Johanson, J. 1994. Dyadic Business Relationships Within a Business Network Context, Journal of Marketing, Vol. 58: 1-5. Barabba, V.P. 1998. Revisiting Plato’s Cave – Business Designs in an Age of Uncertainty,” in Tapscott, Don, Alex Lowy, and David Ticoll (eds.)1998. Blueprint to the Digital Economy – Creating Wealth in the Era of E-Business. New York: McGraw-Hill.
  14. 14. Barabasi, A.-L. 2002. Linked: The New Science of Networks. Cambridge: Perseus Publishing. Betz, F. 2002. Strategic Business Models, Engineering Management Journal, Vol. 14(1): 21-27. Branderburger, A. M. & Stuart, H. W. 1996. Value-based business strategy, Journal of Economics and Management Strategy, Vol. 5(1): 5-24. Bryant, F. B. & Yarnold, P. R. 1995. Principal-components analysis and exploratory and confirmatory factor analysis, In Grimm, L.G. & Yarnold, P.R. (Eds.) 1995. Reading and understanding multivariate statistics, first edition. Washington, DC: American Psychological Association. pp. 99 –136. Castells, M. 1996. The Rise of the Network Society, The Information Age: Economy, Society and Culture, Vol. I. Cambridge, MA; Oxford, UK: Blackwell. Chapman, R.L., Soosay, C. & Kandampully, J. 2002. Innovation in Logistic Services and the New Business Model: A Conceptual Framework, Managing Service Quality, Vol. 12(6): 358-371. Costello, A.B. & Osborne, J.W. 2005. Best Practices in Exploratory Factor Analysis: Four Recommendations for Getting the Most From Your Analysis, Practical Assessment, Research & Evaluation, Vol. 10, No. 7. Doz Y.L. & Hamel G. 1998. Alliance advantage: Tthe art of creating value through partnering. Boston (MA): Harvard Business School Press. Easton, G. 1992. Industrial Networks: A Review, in Ford, D. (ed.) 1997. Understanding Business Markets, 2nd edition. London, UK: The Dryden Press. pp. 102-128. Everitt, B.S. 1993. Cluster analysis, (3rd ed.). New York: John Wiley and Sons Inc. Ford, D. (ed.) 1997. Understanding Business Markets: Interaction, 2nd edition. London, UK: The Dryden Press. Ford, D., Gadde, L.-E., Håkansson, H., Lundgren, A., Snehota, I., Turnbull, P. & Wilson, D. 1998. Managing Business Relationships. England: John Wiley & Sons. Ford, D., Berthop, P., Brown, S., Gadde, L.-E., Håkansson, H., Naude, P., Ritter, T. & Snehota I. 2002. The Business Marketing Course. Managing in Complex Networks. England: John Wiley & Sons. Germany, R. & Muralidharan, R. 2001. The Three Phases of Value Capture – Finding Competitive Advantage in the Information Age, Strategy+Business, First Quarter. Gorsuch, R.L. 1983. Factor Analysis. Hillsdale, NJ: Lawrence Erlbaum. Orig. ed. 1974 Gulati, R., Nohria, N. & Zaheer, A. 2000. Strategic Networks, Strategic Management Journal, Vol. 2: 203-215. Gupta, A.K., Smith, K.G. & Shalley, C.E. 2006. The Interplay Between Exploration and Exploitation, Academy of Management Journal, Vol. 49, No. 4, 693–706. Hair, J.F., Anderson, R.E., Tatham, R.L. & Black, W.C. 2006. Multivariate data- analysis (6th edition). London: Prentice-Hall. Hatcher, L. 1994. A step-by-step approach to using the SAS system for factor analysis and structural equation modeling. Cary, NC: SAS Institute. Hinterhuber, A. 2002. Value Chain Orchestration in Action and the Case of the Global Agrochemical Industry. Long Range Planning, 35 (6), 615-635. Holmlund, M. & Törnroos, J.-Å. 1997. What are Relationships in Business Networks?, Management Decision, Vol. 35(4), pp.304-309.
  15. 15. Hung, S.-C. 2002. “Mobilising networks to achieve strategic difference”, Long Range Planning, Vol. 35: 591-613. Hutcheson, G. & Sofroniou, N. 1999. The multivariate social scientist: Introductory statistics using generalized linear models. Thousand Oaks, CA: Sage Publications Håkansson, H. & Snehota, I. 1990. No Business is an Island: The Network Concept of Business Strategy, Scandinavian Journal of Management, Vol. 4(3): 187-200. Håkansson, H. and Johanson, J. 1992. A Model of Industrial Networks, in Ford, D. (ed.) 1997. Understanding Business Markets, 2nd edition. London, UK: The Dryden Press. pp. 129-135. Håkansson, H. & Snehota, I. 1995. Developing Relationships in Business Networks. London: Routledge. Johanson, J. & Mattson, L.-G. 1992. Network Positions and Strategic Action – An Analytical Framework, in Ford, D. (ed.) 1997. Understanding Business Markets, 2nd edition. London, UK: The Dryden Press. pp. 176-193. Jüttner, U. & Schlange, L.E. 1996. A Network Approach to Strategy, International Journal of Research in Marketing, Vol. 13: 479-494. Klastorin, T. D. 1983. Assessing Cluster Analysis Results, Journal of Marketing Research, Vol. 20 (February): 92-98. Malhotra, N. & Birks, D. 2007. Marketing Research: An Applied Approach, (3rd ed.). Harlow: Prentice Hall. March, J.G. 1991. Exploration and exploitation in organizational learning, Organization Science, 2(1), 71-87. Morris, M., Schindehutte, M. & Allen, J. 2005. The entrepreneur’s business model: toward a unified perspective, Journal of Business Research. Vol. 58: 726-735. Möller, K. & Halinen, A. 1999. Business Relationships and Networks: Managerial Challenge of Network Era, Industrial Marketing Management, 28, 413-427. Möller, K., Rajala, A. & Svahn, S. 2005. Strategic business nets—their type and management, Journal of Business Research, 58: 1274– 1284. Nooteboom B. 1999. Innovation and inter-firm linkages: New implications for policy, Research Policy, Vol. 28: 794-806. Norušis. M.J. 2005. SPSS 13.0 Statistical Procedures Companion. Chicago: SPSS, Inc. Parolini, C. 1999. The Value Net: A Tool for Competitive Strategy. Chichester: John Wiley & Sons Ltd. Punj, G. & Stewart, D.W. 1983. Cluster Analysis in Marketing Research: Review and Suggestions for Application, Journal of Marketing Research, Vol. XX (May), pp. 134-148. Osterwalder, A. 2004. The Business-Model Ontology – A Proposition in Design Science Approach. Academic Dissertation, Universite de Lausanne, Ecole des Hautes Etudes Commerciales. Rajala, R., Rossi, M. & Tuunainen, V.K. 2003. A Framework for Analyzing Software Business Models, Proceedings of the European Conference on Information Systems 2003, Naples, Italy. Saunders, J. 1994. Cluster Analysis, in Hooley, G.J. & Hussey, M.K. (eds.) 1994. Quantitative Methods in Marketing, UK: The Dryden Press Seddon, P.B. & Lewis, G.P. 2003. Strategy and Business Models: What’s the Difference, Proceedings from the 7th Pacific Asia Conference on Information Systems, Adelaide, South Australia, July 10-13.
  16. 16. Slywotzky, A.J. 1996. Value Migration. Boston: Harvard School Press. Speed, R. 1994. Regression Type Techniques and Small Samples: A Guide go Good Practice, in Hooley, G.J. & Hussey, M.K. (eds.) 1994. Quantitative Methods in Marketing, UK: The Dryden Press Steinley, D. 2006. K-Means clustering: A half-century synthesis, British Journal of Mathematical and Statistical Psychology, Vol. 59, pp. 1-34. Tapscott, D. & Gaston, A. 1993. Paradigm Shift: The New Promise of Information Technology. New York: McGraw-Hill. Tapscott, D., Ticoll, D. & Lowy, A. 2000. Digital Capital – Harnessing the Power of Business Webs. Boston: Harvard Business School. Tikkanen, H., Lamberg, J.A., Parvinen, P. & Kallunki, J.P. 2005. Managerial Cognition, Action and the Business Model of the Firm, Management Decision, Vol. 43 (6): 789-809. Timmers, P. 1999. Electronic Commerce – Strategies and Models for Business-to- Business Trading. England: John Wiley & Sons. Timmers, P. 2003. Lessons from E-Business Models, in ZfB – Die Zukunft des Electronic Business, 1/2003, pp. 121-140. Tucker, L.R. & MacCallum, R.C. 1997. Exploratory Factor Analysis. Available online at: http://www.unc.edu/~rcm/book/ Tushman, M.L. & O’Reilly, C.A. 1996. Ambidextrous organizations: Managing evolutionary and revolutionary change, California Management Review, Vol. 38(4): 8-30. Westerlund, M. & Rajala, R. 2006. Innovative business models and offerings based on inconclusive evidence, Innovative Marketing, Vol. 2(2): 8-19. Wilkinson, I. F. & Young, L.C. 1994. Business Dancing – The Nature and Role of Interfirm Relations in Business Strategy, Asia-Australia Marketing Journal, Vol. 2(1): 67-79.

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