EXPLORATION OR EXPLOITATION:
NETWORKING STRATEGIES IN CONTEMPORARY
BUSINESS MODELS OF SMES
Helsinki School of Economics, P.O.Box 1210, FI-00101 Helsinki, Finland
Helsinki School of Economics, P.O.Box 1210, FI-00101 Helsinki, Finland
Laurea University of Applied Sciences, Vanha maantie 9, FI-02650 Espoo, Finland
LTT Research Ltd., Unioninkatu 18, FI-00130 Helsinki, Finland
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.
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
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
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.
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.
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)
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
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.
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 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;
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.
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
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
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
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. 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
We manufacture and provide (whole) products and services .282 .582
We provide large system solutions /service modules to .242 .347
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
We also have horizontal collaboration with our competitors .703 .549
We will take the active hub role and strengthen our position in .310 .388
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
We provide large system solutions/service modules to .510 .347
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
Seeking efficiency in operation Factor 4
We will focus on our current products, technologies and .893 .805
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
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
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
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
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.
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.
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
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
We will focus on our current products, technologies and customers.
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