“SOLITUDE IS IMPRACTICAL AND YET SOCIETY IS FATAL?” –
SOURCES AND EFFECTS OF NETWORK SKEPTICISM IN SMEs
Thomas Ehrmann
Institute of Strategic Management
Westfälische Wilhelms-Universität Münster
Leonardo-Campus 18
48149 Münster, Germany
Phone: +49-251-8331959
Fax: +49-251-8338333
Brinja Meiseberg
(Corresponding Author)
Institute of Strategic Management
Westfälische Wilhelms-Universität Münster
Leonardo-Campus 18
48149 Münster, Germany
Phone: +49-251-8331959
Fax: +49-251-8338333
Email: meiseberg@ism.uni-muenster.de
Paper presented at the EMNet 2011
December 1 – 3, 2011, Limassol, Cyprus
(http://emnet.univie.ac.at/)
Abstract
In the ongoing evolution of the dominant organizational paradigm and mode of competition along the
continuum of single, autonomous firms to dyadic alliances to virtual companies, the current period is
marked by a rapidly growing prevalence of the network form of organization. Yet, irrespective of the
widespread belief that networks provide the most decisive benefits, there is a striking imbalance of
studies researching the beneficial effects of networking compared with those few focusing on potential
downsides of interfirm collaboration. Particularly, SMEs face critical challenges in implementing collaborative strategies, triggered by their owners’ preferences for maximum autonomy, difficulties in
identifying adequate partners, or a lack of trust towards larger partner firms. In consequence, SMEs
often remain skeptical of cooperations. In this paper, we build on the RBV and organizational economics. Based on case studies and survey data from a sample of 348 German SMEs, we explore the nature
and prevalence of network skepticism, and its effects on cooperation decisions. Several contingency
factors – coordination, communication and bonding capabilities – that may affect the linkages between
concerns and decisions are investigated as well. We provide managerial implications for the design of
network cooperations involving SMEs and for how (blind) network skepticism can be overcome.
“This attention to the functionality of network forms of organization explains why economic actors rely
on network forms of organization, but it does not explain why they do not.”
Podolny & Page (1998: 66)
Keywords: SMEs, alliances, network skepticism, capabilities, resource-based view, organizational
economics
JEL codes: D85, L14, D83, D74, L26
“SOLITUDE IS IMPRACTICAL AND YET SOCIETY IS FATAL?”1 –
SOURCES AND EFFECTS OF NETWORK SKEPTICISM IN SMEs
1. Introduction
Of all the phenomena that have gripped the business world in recent years, few match the impact of networks. In the ongoing evolution of the dominant organizational paradigm and mode
of competition along the continuum of single, autonomous firms to dyadic alliances to virtual
companies, the current period is marked by a rapidly growing prevalence of the network form
of organization (Parkhe et al. 2006). Several environmental shifts have opened up rewarding
opportunities for such interfirm cooperations – including the proceeding globalisation of markets, the rise of more technologically advanced economies, the convergence of and rapid shifts
in technologies, as well as regulatory changes in and across nations (Gulati 1995). Networks
are reshaping the global business architecture. The ubiquity of networks, and networking, at
the industry, firm, group, and individual levels has attracted significant research attention
(Hendrikse & Windsperger 2004; Parkhe et al. 2006).
In the realm of strategic management and business administration literature, the term “network” often refers to long-term cooperations between firms, like joint ventures, franchising,
R&D agreements, or licensing (Gulati 1995; McGee et al. 1995; Witt 1999). Research emphasizes the functionality of networks for managing resource dependencies and fostering learning
and knowledge exchange (Cliquet 2000; Windsperger 2004). With respect to these activities,
networks can provide efficiency advantages that markets or hierarchies do not possess; also,
networks can allow firms to secure resources that would not be available on markets at all, like
reputation or customer contacts (Uzzi 1996). Thus, by creating economic opportunities that are
difficult to replicate in any other organizational form, network relationships can serve as a
prime coping response to individual resource scarcity in the quest for competitive advantage
and economic rents. Studies have documented the dramatic growth of cooperative arrangements across industries in recent years, the variety of contractual agreements that formalize
relations, and the multitude of reasons why firms enter into partnerships (Parkhe et al. 2006).
Yet, irrespective of the widespread belief that networks provide the most decisive benefits,
some practitioners criticize the notion of cooperations as a “quasi-panacea” to organizational
challenges. Anecdotal evidence suggests that often, “network skepticists” can be found in
small and medium-sized enterprises (SMEs) (Hausschild & Wallacher 2003). SME decision1
Ralph Waldo Emerson (1803-1882; American poet)
2
makers do not necessarily deny that networks can hold out advantages for small firms as well,
including the ability to tap into new markets, access scale economies, obtain complementary
resources in underdeveloped value chain activities, or receive endorsements from reputable
incumbents (Reuer & Ariño 2007). However, unlike large companies, SMEs face specific
challenges in implementing collaborative strategies that can present major obstacles to cooperation (Alvarez & Barney 2001; Baum et al. 2000; Khanna et al. 1998).
Yet, a very limited number of studies investigate the phenomenon of what can be termed
“network skepticism”, particularly in the context of SMEs. In this paper, we explore this phenomenon. “Network skepticism” refers to SME decision-makers’ attitudes of doubt, or their
dispositions to incredulity, towards the advantageousness of network participation in general,
but specifically for their own firm. We build on two widely cited theoretical approaches to
organizations: First, the resource-based view suggests that firms seek to capitalize on and increase their capabilities and endowments; second, organizational economics assert that firms
focus on minimizing the costs of organizing (Combs & Ketchen 1999). Although these perspectives agree on managers’ likely actions in many areas, their predictions diverge when interfirm cooperation is considered (Combs & Ketchen 1999). Based on these two theoretical
strings, and building on qualitative case studies and quantitative data from a sample of 348
German SMEs, we examine the nature and prevalence of network skepticism and its effects on
cooperation decisions. Several contingency factors that affect the linkages between concern
and decisions are investigated as well – in particular, SME capabilities as regards coordination, communication, and bonding in alliances. We offer managerial implications for how
(blind) network skepticism can be overcome, and for network design involving SMEs.
The next section provides an overview of the literature. Then, hypotheses are developed. Subsequently, we describe the three-stage methodological approach used in this study, followed
by the results. The last section concludes, offering managerial and research implications.
2. Theoretical Background and Hypotheses
Research in strategic management has a long history of using the resource-based view of the
firm (RBV) to explain differential firm performance (Barney 2001; Peteraf 1993). Tying resources to competitive advantage, the RBV suggests that the availability of resources enables
the generation of Ricardian rents and quasi-rents (Conner 1991; Peteraf 1993). The RBV has
been broadened to account for external resources that are available to a firm through interfirm
networks (Gnyawali & Madhavan 2001; Gulati 1999; Lavie 2006; McEvily & Marcus 2005).
3
Previous research has emphasized the functionality of networks for managing resource dependencies (Cliquet & Nguyen 2004).2 By focusing on the effects of resource endowments on
managerial action and performance, the RBV holds that firms can combine internal and external resources for competitive advantage; thus, when facing resource limitations, managers are
attracted to cooperative arrangements to overcome such constraints (Combs & Ketchen 1999).
In contrast, based on transaction costs and agency theory, the organizational economics (OE)
perspective assumes that managers’ central concern is organizing activities efficiently (Combs
& Ketchen 1999). Accordingly, OE understands the various interfirm arrangements as alternative ways of organizing the exchange of goods and services in the context of imperfect information, self-interested behavior, and diverging goals (Hesterly et al. 1990). Here, interfirm
arrangements offer a variety of incentives and governance mechanisms that are not available
in market transactions (Uzzi 1996; Williamson 1975). For example, when network partners
invest in specific assets, reciprocal dependencies reduce incentives for opportunism, and thus
the costs of mutual control (Dyer 1996; Klein & Murphy 1988; Teece 1987). Accordingly, if
interfirm cooperation minimizes organizing costs, networks will provide efficiency advantages
(Combs & Ketchen 1999). Despite their different emphases, both the resource-based view and
organizational economics have gained considerable currency in recent years in explaining
interfirm cooperation and performance effects of network activities (Combs & Ketchen 1999;
Conner & Prahalad 1996; Markides & Williamson 1996; Roth & O’Donnell 1996).
However, there is a striking imbalance of studies researching the beneficial effects of networking compared with those few that focus on potential downsides of interfirm collaboration (Alvarez & Barney 2001; Baum et al. 2000; Labianca & Brass 2006; Parkhe et al. 2006). Podolny
and Page (1998: 66) observe that the “attention to the functionality of network forms of organization explains why economic actors rely on network forms of organization, but it does not
explain why they do not.” Even fewer studies investigate downsides of networking in the context of SMEs (Alvarez & Barney 2001; Baum et al. 2000), although SMEs face specific challenges when engaging in interfirm collaboration. Accordingly, the phenomenon of what we
term “network skepticism” in SMEs remains largely unexplored. The scarce range of results in
the literature indicates that small firms’ reservations against cooperations are triggered by difficulties in finding adequate partners, the management’s vision of maximum autonomy, a
strong focus on conservative firm traditions, or a lack of trust towards larger partner firms
(Alvarez & Barney 2001; Baum et al. 2000; Hausschild & Wallacher 2003; Figure 1).
2
For this study, a network is defined as a durable form of interfirm cooperation that is created and maintained by joint history
and ongoing collective action, that is underpinned by a strategic orientation, a sense of common interest, and the expectation
of gains (similar, Olsen 1965). The terms “network” and “cooperation” are used interchangeably.
4
Figure 1: Reasons for Skepticism towards Cooperation.
However, these concerns are by no means exhaustive, and their prevalence and decision relevance will be contingent on contextual factors inside and outside the firm. An adequate understanding of concerns in SMEs is required both for explaining network formation, functioning,
and evolution in theory, as well as for successfully organizing, managing, and governing cooperations in practice, among and including SMEs. Yet still, in the current body of organizational literature, insights on the phenomenon of SME network skepticism are notably sparse.
In an effort to shed some light on the nature and prevalence of such skepticism, we posit that
H1: A firm’s strategic tendency to network is contingent on firm-specific
benefits, and particularly, costs associated with interfirm relationships.
In addition, alliance research indicates that some firms have constantly greater success with
using cooperative arrangements than other firms (Schreiner et al. 2009). Scholars have suggested that successful cooperation depends on firms’ superior abilities to manage interfirm
relationships, that is, on superior “alliance capabilities” (Anand & Khanna 2000; Ariño & De
la Torre 1998; Doz 1996; Dyer & Singh 1998; Ring & Van de Ven 1994). Often, the most
critical challenges in these relationships, “problems of cooperation” and “coordination problems” (Gulati et al. 2005; Mellewigt et al. 2007), result from uncertainties towards the “true”
5
motivations of partners (Axelrod 1984; Das & Teng 1998; Khanna et al. 1998; Parkhe 1993;
Williamson 1985) and from task interdependence (Borys & Jemison 1989; Gulati & Singh
1998; Litwak & Hylton 1962). Some studies argue that these challenges can be addressed at
the network formation stage – by choosing the “right” partners (Hitt et al. 2000), appropriate
contracts, and adequate governance structures (Argyres & Mayer 2007; Hennart & Zeng
2005; Mayer & Argyres 2004; Reuer & Ariño 2007; Schreiner et al. 2009). Yet, as the network lifecycle unfolds, alliance management capabilities become all the more important; ultimately, those cooperations turn out successful that are able to dynamically adapt and finetune network processes over time. In consequence, research has stressed the significance of
three distinct firm capabilities – coordination, communication, and bonding – in managing
any alliance successfully (Hansen et al. 2008; Schreiner et al. 2009).
First, alliance management capability entails a firm having the ability to coordinate and manage interdependence between partners (Schreiner et al. 2009). It involves the ability to identify and build consensus about task requirements, to specify the roles and responsibilities of
each participant in task execution, and to adapt internal and interfirm procedures when circumstances change (Lawrence & Lorsch 1967; Gulati et al. 2005; Schreiner et al. 2009;
Thompson 1967). Second, communication as “the formal as well as informal sharing of meaningful and timely information between firms” (Anderson & Narus 1990: 44) represents the
“glue” that holds an alliance together, and entails a firm having the know-how and skills to
convey relevant knowledge and information about itself to the partners in a timely, accurate,
and complete manner (Schreiner et al. 2009). Third, the bonding dimension includes providing reliable responses to a partner’s work-related needs, being proactively responsive to their
concerns, attending seriously to their views, ideas, and circumstances to signal respect and
appreciation for the other, and evoking norms of reciprocity (Granovetter 1973; Harrison et al.
1998; Schreiner et al. 2009). In sum, firm capabilities can help SMEs manage relationships
successfully, and capabilities may also ease managers’ concerns towards cooperations and the
restraining effects of these concerns’ on their cooperation decisions. Hence, we posit that
H2: A firm’s strategic tendency to network is contingent on its capabilities to manage coordination, communication, and bonding needs in interfirm relationships.
3. Methods
We apply a three-stage procedure to investigate sources and consequences of network skepticism in SMEs. First, to examine the nature of network skepticism in SMEs in more detail, we
use a case study approach (Study I). Past research highlights that an area where case studies
6
are particularly relevant is when analyzing the development of particular organizational forms
(Eisenhardt 1989; Rubin & Dnes 2010). For this study, two researchers conducted two-hour
interviews with CEOs from ten SMEs. We used semi-structured questionnaires that asked for
general information on the firm; for current cooperations and network activities; for
(dis)advantages of cooperations in general; for benefits the firm had sought and could (not)
realize in cooperations, and for any concerns and disadvantages the firm associated with their
network activities; and for partner selection, control and governance mechanisms. Respondents were also offered the opportunity to advance on specific issues they considered important
(Jick 1979; Yin 2003). To approach the topic from various perspectives, we select firms from
three different backgrounds and present their summarized interview information below. Yet,
there are important drawbacks to case research, including the limited generalizability of findings, and potential ambiguity of cause-effect relationships. Large-sample survey techniques
can overcome such shortcomings; however, they do not permit to gather information on theoretical constructs which are not well understood, as is the case with network skepticism.
Hence, we combine both approaches, integrating findings from case research and survey data.
Thus second, we use a survey approach (Study II and III), to examine the prevalence and effects of network skepticism in depth. We hand-collected a data set of German SMEs listed in
the Hoppenstedt online firm directory. Firms were classified as “SMEs” according to the European Union Commission’s definition – based on the number of employees (500 max.), annual revenues (€50m max.) and the balance sheet total. We randomly selected 2000 firms
from the list, including diverse industries like production, construction, retail, and information
services. In August 2011, self-administered questionnaires with a personalized cover letter
that offered a university contact and a complimentary report of results were distributed among
these firms. The questionnaires targeted one highly ranking strategy executive. Following the
procedures to ensure data validity described by Campbell (1955), Kumar et al. (1993), and
Schreiner et al. (2009), we identified these individuals using two criteria – the possession of
sufficient knowledge about their firm’s network activities, and an adequate level of involvement in the issues under investigation. Based on Kumar et al. (1993) and Schreiner et al.
(2009), we use these validation items: (1) How long the informant had been working for the
firm, (2) How knowledgeable she deemed herself about her own company and its product/service program, and (3) How knowledgeable she deemed herself regarding the collaboration management practices and the collaboration partner (item 1 measured in years; items 2
and 3 on a four-point Likert scale anchored by 1 – “Very low knowledge”; 4 – “Very high
knowledge”). The mean response for the first item was 10.8 years (standard deviation (SD)
7
6.7 years); for the second and third items 3.23 (SD 0.41) and 3.56 (SD 0.33). All the respondents indicated to command knowledge about their firm’s network activities and its partner
firms equal or greater than two on the four-point scale. 57% of respondents were either CEOs
or board members; 43% were executives in strategy-related positions such as marketing, sales
or alliance managers. Based on previous research (Combs & Ketchen 1999; Schreiner et al.
2009), the questionnaire asked respondents for company-specific data (firm age, size, scope,
industry, management team composition, finances, investments, firm reputation, alliance capabilities developed over time (Appendix), partner firms, current network involvement); for
cooperation benefits the firm had sought and (not) realized; for concerns and disadvantages
the firm associated with their network activities, and for these concerns’ relative importance;
and for partner selection, control and governance mechanisms. The exact formulation of the
questionnaire was tested in a qualitative-explorative pre-study involving case study interviewees and five SME researchers. In three rounds of follow-up calls, non-respondents were contacted for telephone interviews. Responses were collected until October 2011. 348 responses
could be used for the analysis (response rate 17.4%). To test for nonresponse bias, we compared early and late respondents in terms of demographic characteristics and other key variables in the study (Kumar et al. 1993). In addition, we compared respondents with a random
sample of 75 nonrespondents on firm age, industry, and number of employees. Both comparisons did not reveal significant differences, indicating that nonresponse was not an issue. We
further controlled for common method bias in the self-reported variables using Harman’s single factor test. The test yielded more than one factor, no factor accounted for most of the variance (the variance explained by the largest eigenvalue was 26%); thus, according to Podsakoff
et al. (2003), common method bias was not an issue either.
Based on the sample SMEs’ responses, first, we provide descriptive statistics on network involvement and on the prevalence of various concerns associated with interfirm relationships
(Study II). Then, we estimate a Polytomous Logit Universal Model (PLUM; an extension of
the general linear model to ordinal categorical data) that reveals firm-specific and cooperation-specific attributes that drive a firm’s decision to engage in versus remain skeptical of
networking (Study III). Here, the dependent variable is the intensity of network involvement
displayed by each firm. From a modeling perspective, this variable is an inherently ordered
multinomial-choice variable. Accordingly, the model is estimated by maximum likelihood and
takes the following form:
Yi   ' xi  u i with (i = 1, 2, … , n)
8
where Y represents the underlying response variable, x is set of exogenous variables, ui is the
residual. An observation belongs to the jth category if
 j 1  Y   j with (j = 1, 2, …, m)
Then, the probability of belonging to a certain category j is given by
Prob(Y  j xi )  ( j   ' xi )  ( j 1   ' xi )
where  stands for the cumulative standard normal distribution. Using a dichotomous variable Zij, which takes a value of 1 if Yi falls in the jth category and a value of 0 otherwise, the
likelihood function can be defined as:
n
L
i 1
 (
m
j
  ' xi )  ( j 1   ' xi )

Z ij
j 1
Maximizing the latter equation yields the model’s parameters that help to determine the probability (Maddala 1983) that a firm displays a certain network involvement.
Thereby, Study I and II provide insights on the nature and prevalence of network skepticism
(in line with H1), Study III investigates determinants of the extent of SME network involvement including SME alliance capabilities (as summarized by H2).
4. Study Results
4.1. Study I: Case Studies
First, to understand the nature of network skepticism in SMEs in more detail, we conducted indepth interviews with SME CEOs as described above. Below, we present information gathered
from three firms from different backgrounds to approach the topic from multiple perspectives.
Firm A is a German public company specialized in debt collection, particularly debt arising
from internet services. The company has 50 employees and a total of €5m revenues per year.
The firm is actively involved in a (pre-existing) network of debt collecting firms. The primary
purpose of the network is to provide some additional services to clients that the firms could
not provide solitarily. Firm A is particularly interested in partners that are active in crossborder operations. Firm A often hands international debt collection mandates over to these
firms’ foreign subsidiaries, in return for a (nominal) percentage of profits from the mandate or
some other service performed by Firm A. The greatest risk involved in this form of cooperation, that had become a substantial problem in the past, was that Firm A lost the “lead” on clients whose debt collection had been handed over. Some clients took their future outstanding
9
bills directly to the subsidiary firms, so that Firm A lost considerable business. This loss of
control had made Firm A reluctant towards further cooperation. Also, Firm A thought that
over time, time and capital investments needed for coordination activities had grown considerably. Repeatedly, Firm A terminated cooperations as company cultures and codes of conduct
in doing business would cause conflicts, or as a partner would not be honest in keeping agreements. Still, over the last years, Firm A had tried to select and develop relationships with a few
trusted partners. From Firm A’s perspective, the central factor for network functioning was
that the goals pursued through joint action were clearly spelled out. Although losing influence
in the network, or domineering partner firms, were concerns for Firm A, these were acceptable
as long as existing agreements were kept. Firm A was not concerned about know-how drain,
as from Firm A’s point of view, any “clever” company could gather the desired knowledge in
the market anyway. Yet, Firm A believed that this attitude would rather not be representative
of their industry. In sum, Firm A had joined a pre-existing network and actively developed a
select few relationships over time; the main aim was gaining access to others’ international
experience; and the risk of losing clients, uncertain gains, and coordination costs were Firm
A’s major concerns – particularly, as Firm A is still young and resources are sparse.
Firm B is a family firm with 200 employees and a long tradition in sheet-metal forming and
producing components for automotive suppliers. The family holds both management and ownership of the firm. At the beginning of the interview, Firm B’s CEO emphasized that Firm B
would, on principle, not engage in any cooperation. The main reason was that the firm generates its revenues from superior technical know-how, which needed all the protection it could
get. In addition, in the past, the firm had been “forced” into cooperating by a major customer
once, leading to a substantial know-how drain after external employees had been allowed inside the firm and quality control was organized jointly. Some cooperation partners later used
the know-how gained from Firm B for their own operations. Firm B was aware of the existence of numerous cooperations in their industry. They believed that the major benefit of these
cooperations was improving firms’ abilities to expand. As Firm B had no expansion plans,
they found they could “afford” not to cooperate. Also, the German certification system that
applies to many components used in automobile manufacturing “protected” the industry from
new competitors, and Firm B had never failed to meet certification requirements, so that customers often placed long-term orders with Firm B. Therefore, Firm B believed their market
position to be secure. Apart from the fear of know-how drain, Firm B was also concerned that
internal data on cost calculations could leak out to partners or customers. In particular, if others found out that Firm B used proprietorial financing only, they would demand lower prices
10
from Firm B, as Firm B obviously did not pay interest for external financing. In sum, unlike
others in the industry, Firm B would not enter cooperations, as gains were considered relatively unimportant and concerns of know-how drain and sensitive data leaking out were severe.
Firm C is a distributor of electrical equipment with over 400 employees in various locations
across Germany. For some time, Firm C had actively worked on forming a network that pursued bulk purchase savings for its members. The network was still growing, and new firms
were welcome to join in, but only if they held a comparably strong market position as the
companies already cooperating. Firm C thought the risks involved in this cooperation to be
uncritical, yet the loss of autonomy caused by joint ordering was seen as a “nuisance”, in addition to the fact that some network members would grow faster than others and encroach on
others’ customers. From Firm C’s perspective, the latter problem was a natural one as the stated purpose of the cooperation was to help members grow and become more competitive. Besides, Firm C indicated concerns of legal problems related to cartelization. What made their
cooperation work, in Firm C’s opinion, was frequent and open communication. In particular,
any secret side-agreements among suppliers and some firm would undermine trust, and eventually lead to an expulsion of that firm from the cooperation. Fairness was supported by “contracts” expressing each firm’s goodwill in joint dealings. In sum, Firm C was active in forming
a network that pursued cost reductions for its members, and for Firm C, all concerns towards
cooperations were of minor relevance in face of the realized efficiency gains.
The case study data already indicate that in practice, SME involvement in cooperative relationships varies substantially. Firms seek diverse benefits by engaging in cooperations, be
those in line with the RBV or OE predictions, and anticipate different relationship downsides
or “costs”, which affects their tendencies to network. Thereby, the case study data lend some
support to H1. To investigate the nature and prevalence of network skepticism in more detail,
Study II explores SME concerns and expectations towards network membership in depth,
based on a sample of 348 German SMEs.
4.2. Study II: Descriptive Empirical Analysis
Previous literature has described the network lifecycle in stages, ranging from firms’ initial
interest to cooperate, a search for adequate partners, network formation, participation, renegotiation and network evolution, to network dissolution (Becker 1999; Möller 2006; Parkhe et al.
2006; Schäper 1997). Based on the literature, we distinguish five stages through which firms’
network involvement can develop over time (Figure 2): (1) the firm has a general interest to
cooperate; (2) the firm undertakes efforts to form or join a network; (3) a network was actually
11
created or the firm joined a network; (4) the firm actively participates in a network, (5) the
firm’s network is constantly adapted and has evolved over time. Following this classification,
we first examine the sample firms’ involvement in networking activity (as of summer 2011).
Figure 2. Involvement in Cooperations in the Sample.
First, as indicated by the case study data, the sample survey results establish that SMEs, in
practice, show very different tendencies to collaborate (Figure 2). Although 89% of the sample
firms claim an interest in cooperating, only 58% are actually engaged in interfirm relationships, and only 31% of the sample firms participate in a network that is functioning well
enough so that it can evolve and adapt over time.
Next, for each stage in the network lifecycle, the concerns that keep firms from engaging in
more intense relationships and their relative importance (seven-point scale, 1 – “Not strong”, 7
– “Very strong”) are displayed below (Figure 3). In the following, we pool those firms that
claim an interest in networking, or an uptake of efforts that however, have not led to any network membership (stages 1 and 2). These firms, unlike others, have not committed to more
intense activities. We term these firms “network skepticists”. We exclude all those firms that
deny any interest in interfirm relationships (like Firm B above) from future analysis, as these
firms cannot be considered as “skepticists”, which leaves a total of 309 SMEs.
Among these 309 firms, the network skepticists fear risk-and management-related problems
most – particularly, they are concerned about know-how drain in cooperations, about losing
sensitive data, and about being exploited by opportunistic partners. Also, due to a lack of experience, they question their abilities in managing cooperation and competition at the same time.
Firms in the third stage, that belong to a network as “nominal” members rather as they do not
participate in activities intensely, forward profit-and partner-related reasons for their inactivity.
According to the survey results, identifying adequate partners is a major problem for these
firms, as is uncertainty about expected gains from cooperation. Network participants in the
fourth stage forward partner-and risk-related reasons for investing little into developing and
adapting their relationships over time. On the one hand, firms in the fourth stage share the emphasis on risk-related concerns – risks associated with specific investments and hold-up – with
network skepticists (stages 1 and 2); on the other hand, partner-related concerns of costly con12
flicts and overly complex decision-making processes are highly relevant to both the firms that
belong to stage 4 and 5. Besides, firms in stage 5 (network evolution) may believe their current
relationships suffice, so there is no need for additional cooperation.
Figure 3. Concerns towards Network Activities in the Sample.
Next, we concentrate on two groups – those firms that engage in network activities (stages 3-5)
and the network skepticists (stages 1 and 2). Interestingly, the advantages of networking are
perceived relatively similar among both groups, even if network skepticists show a strong fo13
cus on resource-related considerations, while network participants consider efficiency-related
benefits as well (Figure 4). Yet, the most relevant concerns associated with interfirm relationships, which restrain the skepticists’ from more intense cooperation, are largely different.
Figure 4. Perceptions of Advantages and Disadvantages of Cooperation.
Last, by summing up the major barriers to networking in each of the respective stages as perceived by the sample SMEs, Figure 5 provides an overview of Study II results. It also offers
some implications for the underlying reasons of why these concerns occur, which in turn helps
with their management. Whereas Study I has focused on the nature of cooperation benefits and
costs on the individual firm level, Study II shows commonalities as regards networking benefits and concerns among network skepticists, and among firms in other stages of the network
lifecycle. By demonstrating that firms associate diverse benefits and particularly, costs with
interfirm relationships, and accordingly, they display different tendencies to cooperate, both
Study I and II provide support for H1.
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Figure 5. Summary of Study II Results.
4.3. Study III: PLUM Regression
H2 argues that firms’ tendencies to cooperate are contingent on their alliance capabilities.
Highly capable firms can “benefit more” from networking than others less well prepared. As
there are no perfect proxies or secondary data on a firm’s alliance management capability (Gulati 1998), in line with previous research, we relied on key informants’ assessments to collect
relevant data (Schreiner et al. 2009), as outlined above. The variables are described below.
Dependent Variable: Interfirm Cooperation
Again, we use the five lifecycle stages that describe the current network involvement of firms
(Figure 2) – (1) the firm has a general willingness to cooperate, (2) the firm undertakes efforts
to form or join a network, (3) a network was actually created or the firm joined a network, (4)
a firm actively participates in network, and (5) the network in which the firm participates
adapts and evolves over time. “Network skepticists” select stage 1 or 2. Focusing on that particular cooperation which respondents evaluate to be most central to their business, “network
participants” indicate the latest stage of network involvement their firm has currently reached.
15
Independent Variables: Capabilities (see Appendix).
Coordination dimension. Following Schreiner et al. (2009), we use four items from previous
literature on organization theory (Thompson 1967) and alliances (Gulati & Singh 1998). These
items reflect whether a firm has previously developed processes designed to coordinate activities, including the adaptation of work processes and incentive structures. For all the variables
measured by multiple items, we build scales by summing and averaging item scores.
Communication dimension. Five items from research on communication in alliances (Das &
Teng 1998; Mohr & Spekman 1994; Schreiner et al. 2009) reflect whether a firm has developed skills to explain its market position, competency, and value propositions to partners.
Bonding dimension. Six items from organizational literature (Kahn 1993; Kim & Mauborgne
1998; Schreiner et al. 2009) reflect whether a firm has developed attentive, supportive behavior towards partners, like willingness to support in difficult times in their relationships.
Control Variables related to the RBV.
Brand name reputation. SMEs without brand reputation may either engage in cooperation intensely to gain credibility; or, they may cooperate less, e.g. due to difficulties in attracting partners. Following Combs and Ketchen (1999), we measure reputation by four items on sevenpoint Likert scales: Comparing this company to competitors, (1) How well respected is this
company? (1 – “Not respected”, 7 – “Very respected”); (2) How good of a value is this company perceived to provide for the price? (Not good/Very good); (3) How strong is this company’s
reputation for consistent quality and service? (Not strong/Very strong); and (4) How strong is
this company’s brand name recognition in its area of activity? (Not strong/Very strong).
Management team experience. An experienced management team (MT) can be a strategic resource (Justis & Judd 1989; Shook & Shook1993). Depending on the amount of managerial
talent already present in the firm, cooperation may be more or less attractive (Bradach 1997;
Shane 1996). Following Combs and Ketchen (1999), MT experience is measured by the number of years of executive-level industry experience of each SME’s management team.
Slack capital. Scarcity of slack capital has been shown to increase using networks, e.g. joint
ventures and franchising (Martin & Justis 1993). Following Hambrick and D’Aveni (1988) and
Combs and Ketchen (1999), we measure each SME’s equity-to-debt ratio, as of 2009.
16
Control variables related to OE.
Asset specificity. If assets exist that are required for working together (e.g. technical adjustments in production) and that cannot be easily transferred to alternative uses, partners are likely to continue to cooperate to recover their investments, which can increase the efficiency of
joint action (Combs & Ketchen 1999; Dyer & Singh 1998). The extent of specific assets is
measured by three items on seven-point Likert scales (Combs & Ketchen 1999): Comparing
this company to competitors, (1) Is equipment for working together customized? (1 – “None
at all”, 7 – “Very much”); (2) Would it be difficult to adjust production processes when cooperating with a new partner? (Not difficult/Very difficult); and (3) Would it be difficult to use
this firm’s equipment for another business? (1 – “Not difficult”, 7 – “Very difficult”).
Specific knowledge. Specific knowledge is often tacit or complex (Polanyi 1962; Shane 1998).
Its transfer entails costs that discourage cooperation (Jensen & Meckling 1995; Kogut & Zander 1992). The variable is measured by four items on seven-point Likert scales (Combs &
Ketchen 1999): Comparing this company to competitors, (1) How long would it take to train
competent managers? (1 – “Not long”, 7 – “Very long”); (2) How long would it take to train
competent employees? (Not long/Very long); (3) How difficult would it be to communicate
job requirements to mid-level managers? (Not difficult/Very difficult); and (4) How difficult
would it be to include all of the mid-level manager’s job tasks in a manual? (Not difficult/Very difficult).
Geographic dispersion. As dispersion increases the costs of monitoring fully-owned operations, it renders cooperation more attractive (Windsperger & Dant 2006). Dispersion is measured as the number of countries where an SME was doing business in 2009 (Combs & Ketchen 1999; Lafontaine 1992).
Additional Controls.
A long-term relationship may have already passed through a critical shakeout period, characterized by conflict and renegotiations, after which parties may develop a higher level of joint
action (Heide & Miner 1992; Parkhe 1993). We measure relationship duration as the years of
a firm’s involvement in the cooperation under investigation (Combs & Ketchen 1999). Although by definition, SME size does not vary dramatically, it may still affect the relative SME
attractiveness for potential partners (Heide & John 1990). Size is measured as the number of
employees in 2009. Industry may have an effect on cooperation intensity, as needs and risks
vary across industries (products – 0, services – 1). Table 1 displays descriptive statistics, Table 2 shows regression results.
17
Table 1. Descriptive Statistics.
Variable
Mean
Std. Dev.
Dep. Variable:
Networking Stage
3.33
1.51
Capabilities:
Coordination
4.30
2.25
0.37***
Communication
4.90
2.05
0.27***
Bonding
4.28
2.25
-0.13*
4.05
2.24
-0.26*** -0.17**
-0.09†
0.05
19.45
11.39
-0.22*** -0.12*
-0.07
0.04
0.22***
Slack Capital
3.88
2.17
-0.24*** -0.14*
-0.10†
0.16**
0.16*
Asset Specifity
4.29
2.14
Specific Knowledge
4.14
2.27
Dispersion
4.47
2.09
0.05
5.47
2.95
262.78
143.81
RBV Controls: Brand Reputation
Management Experience
OE Controls:
Other Controls: Relationship Duration
Firm Size
Industry
0.17**
0.19***
-0.08
0.06
-0.01
0.21***
0.05
-0.12*
-0.02
-0.10†
-0.11†
0.04
0.15**
0.05
0.10†
-0.03
-0.08
-0.19**
-0.04
0.05
-0.06
0.23***
0.13*
0.07
-0.16**
-0.07
-0.04
-0.16**
0.12*
-0.16***
0.03
0.07
0.01
0.10***
-0.08
--0.14*
-0.10†
0.00
-0.07
0.05
0.08
0.17**
0.15**
0.14*
-0.13*
-0.13*
-0.23***
0.11†
-0.11*
0.16**
0.07
-0.26*** -0.06
Significance levels (two-tailed): *** p < 0.001; ** p < 0.01; * p < 0.05; † p < 0.1.
18
0.03
-0.16**
-0.05
0.14*
0.19**
-0.14*
0.05
19
Table 2. PLUM Results.
Capabilities:
RBV Controls:
OE Controls:
Other Controls:
Model a
Model b
Coefficient
(Std. Error)
Coefficient
(Std. Error)
Coordination
0.274***
(0.051)
Communication
0.207***
(0.054)
Bonding
-0.047
(0.050)
Brand Reputation
-0.178***
(0.050)
-0.151**
(0.051)
Management Experience
-0.013**
(0.005)
-0.013*
(0.005)
Slack Capital
-0.122*
(0.053)
-0.109*
(0.054)
Asset Specifity
0.134**
(0.051)
0.124*
(0.053)
Specific Knowledge
-0.163**
(0.050)
-0.173**
(0.051)
Geographic Dispersion
-0.018
(0.050)
-0.039
(0.053)
Relationship Duration
0.055**
(0.019)
0.030*
(0.019)
Firm Size
0.000
(0.000)
0.000
(0.000)
Industry
0.305†
(0.221)
0.142†
(0.210)
F
81. 007***
2
Nagelkerke Pseudo-R
N
125.133***
0. 243
0. 411
301
301
Significance levels (two-tailed): *** p < 0.001; ** p < 0.01; * p < 0.05; † p < 0.1.
Hit rate: 81.4% of cases classified correctly.
Firms that had not entered in any business relationships in the last three years excluded from this analysis.
The PLUM results document that those firms that have high coordination and communication
capabilities, as predicted by H2, engage in more intense cooperation. Yet, bonding capabilities
appear to decrease network involvement rather (but the coefficient is insignificant); eventually, given that firms’ capability perceptions are accurate, those firms scoring high on bonding
capabilities are more easily exploited by opportunistic partners. Hence, for these firms, risks
associated with cooperations may become all the more pronounced.
20
As regards the RBV controls, a strong brand name is related to low networking activity, which
may result from efforts to protect the firm’s brand image, or as holding a strong market position decreases the need for acquiring resources (e.g., concerning legitimation needs or access
to market channels) through cooperation. A negative relationship also holds for management
experience and slack capital – the more experienced the management, and the higher slack
capital, the lower the tendency to cooperate. Again, resource needs may be tolerable, and as
many potential partner firms will score lower on experience and capital resources, their value
proposition may appear unattractive for high-scoring firms. As regards the OE controls, the
higher asset specifity, the higher cooperation activity. As specific investment increases the
credibility of the firm’s willingness to cooperate, it decreases the need for costly control, enabling more efficient exchange among partner firms. Specific knowledge, in turn, decreases the
firm’s tendency to network. As transferring specific knowledge is time-consuming and costly,
cooperation may prove less efficient and thus less attractive compared with hierarchical, internal operations. Thereby, both theories’ predictions play a central role in SME cooperation decisions. Besides, the longer a relationship has been in place, the more intense are networking
activities; time investment, intuitively, concurs with network evolution.
5. Discussion
Irrespective of the widespread belief that networks provide the most decisive benefits, there is
a striking imbalance of studies researching the beneficial effects of networking compared with
those few that focus on potential downsides of interfirm collaboration. Yet in practice, particularly SMEs often remain skeptical of interfirm cooperation. Based on qualitative case study
information and quantitative data from a sample of 348 German SMEs, we investigate the
phenomenon of “network skepticism” in SMEs, focusing on its nature, prevalence, and effects.
We find that SME managers’ tendencies to network depend partly on the specific benefits, but
particularly, on the specific costs associated with engaging in interfirm relationships. Although
the anticipated benefits and costs are firm-specific, there are some commonalities among those
firms that can be classified as currently belonging to the same stage of the network lifecycle.
For those SMEs that do not actively engage in network relationships, the “network skepticists”, barriers to network entry appear to be mostly management-related – many of these
firms question their competencies in handling cooperation and competition at the same time.
Network skepticists’ concerns are also risk-related – they often refer to the risk of know-how
drain inherent in collaborations, and to their fear of exploitation by opportunistic partner firms.
Whereas for both the two groups of network participants and network skepticists, the ad21
vantages associated with networking are similar, the perception of disadvantages largely diverges – which is particularly interesting in light of the fact that the costs of networking have
rarely been explored in the literature (Lavie 2006; Parkhe et al. 2006; Podolny & Page 1998).
Our findings highlight the significance of cost considerations for decisions to engage in versus
to avoid cooperative arrangements. We can also establish that both resource-related considerations, as argued by the RBV, and cost or efficiency-related considerations as argued by OE,
play a strong role in SME cooperation decisions.
Effects of cooperation concerns on managers’ decisions to engage in or to refrain from networking may further depend on each firm’s alliance management capabilities. We observe that
firms that have developed strong coordination capabilities over time show higher network involvement than firms that are less well prepared. Along the same lines, research on alliance
performance has argued that due to the divided authority structure and the physical, cognitive,
and cultural distance between partners, alliance functioning and performance suffers from inappropriate interfaces and boundary-spanning mechanisms, unclear procedures, responsibilities, and loci of control (Doz 1988; Larson 1992; Mohr & Spekman 1994; Schreiner et al.
2009). In consequence, coordination failures impede concerted actions needed to capitalize on
the specialized, but interdependent, activities of network partners (Thompson 1967). As the
cost of failed coordination may even exceed the benefits of cooperation, firms need adaptable
mechanisms, including procedures, rules and policies, to create an appropriate framework for
future interaction (Schreiner et al. 2009). We also find that firms that have developed communication capabilities engage more actively in networks than others that lack such competency.
Similarly, previous literature has argued that adversarial effects of information asymmetry are
intensified by a lack of adequate communication between network partners, which prevents
them from building a shared understanding of their obligations, from developing shared mental models of how to work together, as well as from mitigating adversarial group dynamics and
managing conflict (Borys & Jemison 1989; Larson 1992; Mohr & Spekman 1994; Schreiner et
al. 2009). Inadequate communication makes it also difficult for firms to assess the value creation opportunities within a particular relationship (Ring & Van de Ven 1994). Particularly,
skills in lateral communication are required to sustain and adapt partnerships through ongoing
renegotiations of initial agreements (Schreiner et al. 2009). Our results support this claim.
Yet, contrary to expectation in the literature, unlike high coordination and communication
capabilities, bonding capabilities do not promote intense networking. Previous research suggests that close personal relationships serve as a conduit for creating expectations of mutual
cooperation, for developing trust and reciprocity, and for facilitating knowledge-sharing
22
(Granovetter 1973; Gulati 1995; Kale et al. 2000; Larson 1992; Ring & Van de Ven 1994; YliRenko et al. 2001). On the upside, interpersonal bonds can also facilitate conflict resolution,
secure the adaptation and execution of long-term exchange, and foster continuity (Schreiner et
al. 2009). On the downside however, high bonding abilities may also increase the risk of exploitation and hold-up by opportunistic partner firms, so this capability may not come as constantly useful.
In sum, our findings document the importance of developing coordination and communication
capabilities in the early stages of cooperations, to form realistic expectations of relevant benefits and risks in joint activity and implement adequate collaborative strategies. In the initial
phase of networking, establishing a culture of cooperation with clear goals, roles and responsibilities, can promote successful start-up of collaborative arrangements. The more intense
networking involvement gets, the higher the perceived risk of costly conflicts becomes. In
consequence, governance and integration mechanisms in the network become increasingly
important to capitalize on the specialized, interdependent activities of firms. Among the sample firms, 45% stated that in their experience, integration and coordination of activities was
achieved best by joint meetings of all the firms involved. Another 10% declared that one or
more firms took the lead in organizing activities. 17% explained that their network had an
organizing committee of employees from the participating companies that would orchestrate
activities. Interestingly, 8% believed that for fairness reasons, the best coordination device
ever used in their network was an external committee, paid jointly by all network members,
that would allocate, regulate and help fine-tune activities among the firms. Here, some potential for more professionalized cooperation management may still remain untapped.
To conclude, this study takes a step towards developing a more comprehensive picture of
network involvement of small and medium-sized firms by opening up the black box of
sources and effects of network skepticism in SMEs. However, there are numerous limitations
to this research. Foremost, the analysis partly relies on self-reported data. To guard against the
issues related to such data, we use a three-stage methodological approach, apply previously
validated scales and numerous control variables, and check for common method bias. Additionally, to reduce the risk of reversed causality (Study III), capabilities are related to managers’ previous experience with their respective firm, and the tendencies to network are measured by the current efforts to cooperate. We suggest that reversed causality is uncritical as the
current networking efforts do not allow changing firm capabilities in an instant, but capabilities develop over time and the current tendencies to network are an outcome of this developmental process (and of resource and efficiency-related considerations), rather than that the
23
developed capabilities could all be an outcome of current cooperation efforts. Yet still, networking and developing capabilities will, of course, to some extent co-evolve. Here, using
longitudinal and dyadic data could provide further insights on capabilities-building and on
forming and (re)negotiating relationships in SMEs. Besides, while economic rationales are
appropriate to explain governance choices in light of exchange hazards (Cochet & Ehrmann
2007), incorporating concepts from cross-disciplinary studies on organizational and individual
embeddedness, ethics, fairness, or social distance, can be equally useful to understand the
evolution and termination of relationships structures among and including SMEs.
24
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APPENDIX.
Measurement Scale and Items – Capabilities
Note: Seven-point Likert scales are used to provide responses for each item, 1 – “Strongly
disagree”, 7 – “Strongly agree”. Respondents were requested to evaluate the items with reference to a particular firm with whom their firm had an ongoing or past collaborative relationship that was considered as most central to their business, within the last three years. Constructs and items taken from Schreiner et al. (2009).
a)
Coordination dimension (CR 0.81, AVE 0.57)
1.
For coordinating partner-related activities, we have established internal processes
(e.g., for marketing, project coordination) within our company.
2.
For the cooperation with partners, we have established cross-company processes,
meaning processes reaching across company boundaries.
3.
Within our company, we meet regularly to adapt our working procedures to partners.
4.
Within our company, we have adjusted our incentive systems (bonus, goal agreement)
to serve the goals of a partnership.
b)
Communication dimension (CR 0.89, AVE 0.61)
1.
On any given occasion, we can explain the win-win situation of a cooperation to partners.
2.
We try to achieve an instant link of certain customer needs to our name in partners.
3.
We make an effort to let partners know exactly our market positioning.
4.
We make an effort to make partners understand our service and product offering.
5.
When organizational changes occur, we inform partners about the new contact persons in our company.
c)
Bonding dimension (CR 0.89, AVE 0.59)
1.
Even in difficult situations, we signal readiness for discussion towards partners.
2.
We stand by a partner’s side even in difficult situations.
3.
We listen attentively when partners explain problems to us.
4.
We care about the concerns of partners even if we do not expect any advantages to
arise for us in the short term.
5.
During conversations we feel intuitively what partners actually want.
6.
When discussing points of disagreement, we always try to see a partner’s point of
view.
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