References

Transcription

References

Editorial Board
Wojciech Czakon (head), Aldona Frączkiewicz-Wronka, Janina Harasim,
Grzegorz Kończak, Małgorzata Pańkowska, Andrzej Piosik, Adam Samborski,
Sławomir Smyczek, Maja Szymura-Tyc, Tadeusz Trzaskalik, Urszula Zagóra-Jonszta,
Patrycja Klimas (secretary)
Programming Committee
Danuše Bauerová, Antonio Cartelli, Józef Dziechciarz, Bohdan Gruchman,
Günter Hofbauer, Natalia P. Ketova, Natalja Lace, Jorma Larimo, Victor N. Ovchinnikov,
Maria Romanowska, Frederic Le Roy, Reiner Springer, Heinz-Dietrich Steinmeyer
Publishing editor
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© Copyright by Publishing House of The University of Economics in Katowice 2014
ISSN 1732-1948
Edition: 160 copies
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Jerzy Niem
mczyk, Ewa Stańczyk-Hu
S
giet
COOPERA
ATIVE AND COMPETITIV
C
VE RELATION
NSHIPS
IN HIGH EDUCATION
E
N SECTOR IN
N POLAND ..................................................................
5
Anna Lipk
ka, Stanisław Waszczak, Alicja
A
Winnick
ka-Wejs
LOYALTY
Y AND WORK
KAHOLISM IN
N THE METH
HODS OF HU
UMAN CAPIT
TAL
EVALUAT
TION (IN) AN
N ORGANIZA
ATION – A CO
OMPARATIVE
E STUDY .................
25
Dominikaa Latusek-Jurcczak, Kaja Prrystupa-Rząd
dca
COLLABO
ORATION AN
ND TRUST-B
BUILDING IN
N OPEN INN
NOVATION
COMMUN
NITY .............................................................................................................................
47
Frédéric Le
L Roy, Famaara Hyacinthee Sanou
DOES CO
OOPETITION
N STRATEGY
Y IMPROVE MARKET
M
PE
ERFORMANC
CE?
AN EMPIR
RICAL STUD
DY IN MOBIL
LE PHONE IN
NDUSTRY ........................................
63
K
Patrycja Klimas
MULTIFA
ACETED NAT
TURE OF CO
OOPETITION
N INSIDE AN
N AVIATION
N
SUPPLY CHAIN
C
– THE
E CASE OF THE
T
AVIATIO
ON VALLEY
Y...................................
95
Wojciech Czakon,
C
Karo
olina Mucha--Kuś, Mariuszz Rogalski
COOPETIITION RESEA
ARCH LAND
DSCAPE – A SYSTEMATIC
S
C
LITERATU
URE REVIEW
W 1997-2010 ....................................................................................... 121
Jerzy Niemczyk Ewa Stańczyk‐Hugiet Wroclaw University of Economics, Poland
COOPERATIVE AND COMPETITIVE
RELATIONSHIPS IN HIGH EDUCATION
SECTOR IN POLAND
J ERZY N IEMCZYK , E WA S TAŃCZYK -HUGIET
Abstract
Coopetition builds on the idea that firms – competitors cooperate to create
values and to appropriate value. Despite extant research on this topic, our understanding about how firms are engaged in cooperative relationships with their
rivals is still in its early stages.
This paper explores the higher education sector in Poland from the perspective of cooperative and competitive relationships, and analyses its performance
on three different levels, i.e. macro, meso, and micro using case-based insights
to answer the question(s).
We propose that cooperative relationships amongst a variety of different
universities increase their competitiveness and enhance the diffusion of knowledge.
In the long run this translates into benefits for all parties and into a rise in the efficiency of the entire education sector.
Keywords: coopetition, competition, cooperation, strategy, university, resource
heterogeneity, convergent goals.
Introduction
We can observe increasingly innovative forms of relationships between
competing organizations when we view them through the framework of the cooperative relationship (Czakon, 2012).This makes us believe that the source of
competitive advantage, is a set of relationships between the firm and other market
players. The organization that sets up such a relationship can be more beneficial in
the market. As a result the competition for the relational value is treated as a third
leg in the theory of strategy (Contractor, 2002). Research on coopetition is limited
mainly to business organizations, however, relations of coopetition can easily be
observed in the non-business sectors, for instance in higher education.
Siregar, Dagnino, and Garraffo (2011) state: “Connectivity between the
concepts of Relationships, Strategy and Resources brings perspectives such as
resource based view and relational view into [our] consideration as potential
theoretical perspective in explaining coopetition. All the results at the end may
affirm coopetitive strategy as a new form of strategy, an alternative to the two
other main paradigms – competition and cooperation – that are already corroborated in the field of strategic management”.
6
COOPERATIVE AND COMPETITIVE RELATIONSHIPS…
The higher education is a unique sector that permits us to observe systems
of cooperation and competition, both characteristics of so called coopetition
strategy. Moreover, colleges and universities communicate with their environment in a natural way. This communication process takes place through: students, that in the vast majority are employed in surrounding areas; staff, who are
carrying out research for outside companies and teams of researchers, which are
working together with outside companies as well as within the framework of the
university’s general procedures and so they are developing interconnected links
with surrounding businesses and companies so establishing an cooperative relationships. In almost laboratory conditions, we mean case-based insights individual variants of relationships are possible to be analysed and assessed qualitatively in terms of their effectiveness and efficiency.
This article aims to diagnose the current state of cooperative and competitive relationships within the academic environment and suggests what future
might be. The reasoning visualizes that we undertake the identification of the
type of the relationship between the organizations in the researched sector. In
other words the concept of the paper refers to the identification of the types of
relationships. This is particularly important since such a relationship has not
been observed yet.
1. Coopetition in strategic management literature
In the world of business many companies decide to take not only competitive
actions, but also actions, which rely on cooperation with other competitors. Research
conducted by A. Brandenburger and B. Nalebuff (1996) likewise G. Dagnino and
G. Padula (2002), amongst others, present situations where competitive and cooperative actions appear simultaneously. Research on coopetition has been increasing rapidly in recent years and the very concept has been used to clarify the economic and social effects of networking in various sectors and countries (de Ngo, and Okura, 2008).
Until 1996, studies on coopetition were limited to proposals of A. Brandenburger and
B. Nalebuff (1996). Since the mid-90s of 20 century however many more publications focusing on this subject have become available, such as: dyadic coopetition
between two entities (Bengtsson, Kock, 2000), heterogeneous coopetition (e.g. Luo,
2004) and inter-organizational coopetition (e.g. Amburgey and Rao, 1996; Tsai,
2002; Luo, Slotegraaf and Pan, 2006).
G. Hamel et al. (1989) treat coopetition as a continuation and natural consequence of competition. Cooperation and competition can therefore be per 7
ceived as the phases of the organization’s life cycle. M. Bengtsson and S. Kock
(2000) suggest that the benefit companies derive from coopetition is an effect of
the combination of the pressure of competitors (effect of the competition), with
an ability to access greater resources (effect of the cooperation
Coopetition is defined as "a system of actors operating on the basis of the
partial compliance of interests and purposes". It is an approach that is still developing, allowing for new and different examinations into the field of strategic
management (Dagnino et al., 2008). Coopetition, claims W.Czakon, is a particular object of study, requiring a specific theoretical approach (Czakon, 2009).
In general terms, coopetition is a strategy of joint value creation, a strategy
of competition in the distribution of values in conditions of a partial similarity of
purposes and the changeable structure of the positive-sum game (Dagnino et al.,
2008). In A. Lado’s, as well as M. Bengtsson and S. Kock’s (2000) opinion, it is
these two significant forces, the pressure of competition and desire for cooperation – that constitute coopetition, allowing for a rare situations in which competitors show the initiative directed at rent-seeking (Lado et al., 1997). Coopetiton is
also “driven” by the need for strategic flexibility. Many studies have focused on
the search for an innovative benefit in innovation-related coopetition, or simply
in innovation networks (Ritala, Hurmelinna-Laukkanen, 2009). These studies are
conducted mainly in business enterprises and rarely in other types of organizations (Lundberg, Andresen, 2012). It is possible, however, to find examples of
coopetition in the education sector in several research projects – International
research collaboration: opportunities for the UK higher education sector, 2008;
Review of closer collaboration between universities and major publicly funded
research agencies, 2004 (Commonwealth of Australia, 2004); Lundberg, Andresen, 2012. How do we study co-opetition in practice? Research on
coopetition has theoretical character largely. Listed authors attempt to offer
coopetition classifications and models (Luo’s studies, 2004, 2006, 2007; Rusko,
2011, p. 311-320; Mention, 2011, p. 44-53).
2. Research concept
In this study, Kenworthy’s method of distinguishing levels of micro, macro and
meso coopetition is used (Kenworthy, 2005). The level of macro coopetition refers
to the relation between groups of organizations, including those from various sectors. The meso level refers to vertical and horizontal relationships amongst organizations. The micro level concentrates on the entities within the organizations.
8
Most studies focus only on inter-organizational network ties and do not incorporate into their research the effect of interpersonal relationships, which can also
facilitate economic interactions between organizations (Ingram and Roberts, 2000).
The basic question concerns the benefits, in terms of knowledge and economic value that we receive from every type of relation.
For the purpose of this study three important variables have been considered: interests and goals, relations, and resources. As Branderburger and
Nalebuff propose coopetition/coopetition strategy is characterized by partially
convergent interests and goals and this view has been widely accepted by researchers since 1996. The relations between partners are another important attribute determining cooperation. The importance of relations in building a competitive advantage is highlighted by a resource-based view (Barney, 1991).
Relations are a source of competitive advantages. They who have the valuable
resources win a competitive advantage.
Relational capital between partners in a network of relationships can foster
cooperative relationship, as it creates a basis for learning and knowledge transfer
on the one hand, and curbs opportunistic behaviour so preventing the leakage of
critical knowledge, on the other. Therefore, relational capital can enable competition and co-operation to co-exist.
The overall goal for firms to cooperate with other firms is to strengthen
their competitive positions by inter-partner learning and by obtaining valuable
resources from their cooperative relationships. This is recognized in both the
literature on alliances (e.g. Parkhe, 1993; Reuer and Tong, 2010) and coopetition
(e.g. Gnyawali and Madhavan 2001; Luo, 2007). Papers written on alliances
(thoroughly) described how firms achieve stronger competitive positions by
cooperating with other firms through: internalizing partner skills and resources
(Ahuja, 2000; Prahalad and Hamel, 1990; Oum et al., 2004), learning from partners (Dussauge, Garrette and Mitchell, 2000), knowledge sharing and creation
(Inkpen, 2000; Khanna, Gulati and Nohria, 1998), growth in size and market
share (Oliver, 2001; Reuer and Tong, 2010), protection from radical new innovations which will erode a firm’s competitive position (Rothaermel, 2001;
Afuah, 2000), sharing the risks and costs of research and development (Hagedoorn,
2002; Ouchi and Bolton, 1988), raising entry barriers (Eisenhardt and Schoonhoven,
1996) and creating economies of scale (Koh and Venkatraman, 1991; Garrette,
Castaner and Dussauge, 2009; and Yami et al., 2010).
Gulati (Gulati, Singh, 1999) develops the notion of network resources,
which refer to those resources that emerge from a firm being embedded in inter-
9
firm networks. Other studies show that network resources are particularly important to a firm's acquisition of competitive capabilities (McEvily and Zaheer,
1999) because network resources offer valuable information about new business
opportunities (Gulati, Singh, 1999). Regarding the resource-benefit of the cooperative relationship, the main aspect discussed by some researchers is, that it is
best if the variety of resources available in the organisations environment is use
together in order to survive the demanding and changing environment (Tsai,
Fang, and Lin, 2005). Cooperation between firms occurs when they present economic benefits for each other rather than the result of the alliance meaning that
costs associated with acquiring resources in the market or developing them internally are incurred (Williamson, 1991). Typically, the type of association in
collaborations is tilted towards partial interdependence, in which the firm uses
cooperative arrangements with other firms to attain its objectives.
Heterogeneity in resources can foster coopetitive relationships, because
unique and complementary resources can be advantageous both for co-operation
and competition.
In a typical network, three types of resource flows take place between partners – information flows, asset flows, and status flows – and firms’ ability to
access and use network resources varies depending on their structural position in
the network (Gnyawali and Madhavan, 2001).
3. Research framework and methodology
The study assumes three levels analysis of the types of relationship. The
first level is a macro level between universities and the environment, mainly
business. The second level is between universities and the third is level is between university departments, inside organizations. On all these levels, factors
that may affect the increase in the quality of the relationships will be examined.
The research assumes that these factors are: the degree of similarity of the objectives, the type of relationship binding the main participants and the degree of
resource diversity on both/all sides.
10
Figure 1. Conceptual model
Meso
Macro
•similarity of the objectives
•type of relation
•resource diversity
•type of relation
Micro
•type of relation
Coopetition
In particular, at the value of the macro level relies on communication and
information flow as well as generating inter-sector knowledge and the transference of this information. This results in the possibility of accumulating
knowledge. The value in this case is obtained by reducing aggressive and suboptimal rent-seeking, and by agreeing on sharing both benefits and funds.
Relationspis of the university and contextual surrounding strengthens their competitiveness, but also affects the synergy between the competitive parties (Bizzi
and Langley, 2012).
Therefore, we ask the following question:
Q1: Does a higher level of convergent interests and goals between the
university and business environment lead to a higher level of cooperation?
Q2: Does the sense of cooperation between the university and business
environment dominate in terms of their relationship?
Q3: Will heterogeneity in resources between the university and business
environment lead to coopetition?
In our opinion, answering these questions helps identify the types of relationships between universities and business organizations. The chosen way to
identify this type of relationship enables us to determine whether in this case, the
relationship between these entities is simultaneous competition and cooperation,
or if there is only cooperation between them.
11
At the meso level, the value can be seen in the results of intra-sector creation and transfer of new knowledge, deep communication and the flow of information as well as joint action on co-development.
Coopetition of many differing varieties of universities increases their competitiveness and enhances the diffusion of knowledge, and in the long run, translates into benefits for all coopetitive sides and a rise in efficiency rise throughout
the entire education sector.
Therefore, we ask the following question:
Q4: Will higher levels of convergent interest and goal between universities lead to higher level of cooperation?
Q5: Will higher levels of cooperation between universities lead to increased
level development throughout the education sector (meso coopetition level)?
Q6: Will heterogeneity in resources between universities lead to coopetition
(equal competition and collaboration)
At the micro level, where value is added through an extensive system of
communication and information flow, and through the creation and transfer of
new knowledge within an organization, the economic benefits (economic value)
are obtained through greater involvement of all stakeholders of the organization
(Enz and Lambert, 2012; Bizzi and Langley, 2012).
Cooperation between many different departments and units can influence
the rise in their level of effectiveness, In order to answer this we must first look
at the following questions:
Q7: Will higher levels of convergent interest and goals between departments and researchers lead to higher level of cooperation between departments
(cooperation – dominated)?
Q8: Will higher levels of competition between departments and researchers lead to higher levels of faculty (school) effectiveness?
Q9: Will heterogeneity in resources between departments and researchers
lead to coopetition (equal competition and collaboration)?
Referring to resource diversity we follow Bengtsson and Kock thesis: “Heterogeneity in resources can foster coopetitive relationships, as unique resources
can be advantageous both for cooperation and competition” (2000, p. 421).
Finding the complex results need in order to answer these questions, using
the available data and information, is almost impossible. The main obstacle is
the lack of possibilities in comparing in similar operating conditions. Practically
12
speaking, in order to fully answer these questions a sample of mature sectors in
established Polish universities would have to be available for research. Such
a situation will more than likely only be possible in about 10 years as currently
the higher education sector and in particular the economics department is in
a process of growth and development, triggered by implementing new changes
in how it functions in Poland.
4. Sample characteristics and methods
As of 1 March 2014 in Poland there were 5 public universities and 38 faculties of economics at other public universities (16 at universities, 18 at polytechnics and 4 at the agricultural colleges) operating. In addition, nearly 150 private
higher education institutions educating in the field of economics and 25 public
vocational schools educating at the first cycle of studied, mainly in the field of
management were operating. The largest and most important “players” in the
market include: University of Economics in Katowice, Cracow University of
Economics, Poznan University of Economics, Warsaw School of Economics,
Wroclaw University of Economics.
As of 30 November 2010, 59,184 students were enrolled in these five universities. A total of 415,559 – students were studying in the field of economic
and administrative sciences at public universities in Poland. This means ca. 2/3
of all students in Poland (the total of 1,841,251) were studying (CSO, 2011).
Other statistics which show the study of economics in relation to the rest of the
higher education sector are unable to be used for analysis owing to the specific
nature of the study of economic. These specific features are: a very low level of
expenditure needed for educating an individual student in comparison to other types
of colleges, a high percentage of part-time students – nearly 50% and a low statistical citation rate in the field of social sciences. All these factors make the study of
economic very hermetic in a sense.
The five most important business schools in Poland are representative for
the Polish higher education. These universities have a decisive influence on the
level of both economics and management and the quality of those graduating
with economics in Poland. They will also be subject for exploration in the context of the stated hypotheses.
There are 23 departments in five business colleges in Poland. A further 38
economic departments exist in other private higher education institutions. In
total, in public universities there are 51 departments grouping ca 200 chairs,
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bringing nearly 2,500 researchers and PhD students. These employees work
independently conducting research, which may result in some 5,000 scientific
articles, monographs and chapters in books per year.
The HEI sector, in particular the economic department in Poland is a highly
competitive sector. It is difficult for a natural cooperation. This follows from the
fact that virtually all of its revenues comes from teaching activities (the subsidy
and tuition fees from non-full-time students). Therefore, each student recruited
actually means more revenue. And every school is a real competitor. The number of students ranges from 10 thousand at Warsaw School of Economics to
21 thousand at Cracow University of Economics (2010). This illustrates the differences in the activity of recruiting departments.
Data used in the work was obtained from different research methods. They
were primarily unstructured interviews with the staff of the analysed business
schools, as well as primary data from different reports and financial statements
from analysed business schools. The different data was then compared in order
to eliminate factual inaccuracies. The study used comparative analysis and
presentation of the usual techniques of quantitative data.
5. Macro level analysis
The macro level concerns the analysis of the relationships between universities and the business environment. This relationship is still in its infancy and is
mainly limited to the use of resources offered by both partners in teaching activities. A high degree of convergent interest and goals has to be dealt with. In most
Western universities, this type of relation is also one of the most common. Analysis of five universities’ and careers offices’ websites selected for the study
show that these universities are connected to about 20 global companies which
offer their student work programs.
The second area of cooperation is in applied research, undertaken at the request of individual companies. The situation of analysed universities differs
substantially from other universities. In case of economic HEIs the competition
for research grants is great and completely differs from the competition for
grants from departments at other colleges. Apart from colleges, global consulting companies, small and medium enterprises advising in the area of finances,
accounting, management, logistics, etc. and of course companies’ own developmental departments seek to receive contracts from the business domain.
14
A report prepared by the staff of the University of Economics in Katowice
emphasizes these facts and indicates that companies "are mostly not interested in
the implementation of joint research (66% negative responses), nor in participating in the process of education (65%) or providing university students and graduates with placements (55%). They are moderately (30-40% of responses) interested in consulting, expertise and training conducted for their benefit by
university staff” (Model współpracy uczelni..., 2010).
A rapid development of consultancy conducted by university research staff
outside the formal framework of the university is a significant specificity of
Polish economic HEIs. Most research staff working for business colleges is or
will be also working in a business or as a business consultant. This results from
the specificity of teaching and research activity, as well as from an economic
necessity caused by the low rate of pay for salaries, and competitive salaries in
the private consulting sector. Such connections unquestionably improve the
quality of research and teaching processes.
Previous studies show the positive impact of better coordination and increased diversity of resources for the cooperating parties (Garcia and Velasco,
2002). Coopetition also means an access to external knowledge (Spence, Coles,
and Harris, 2001) and coordination of organizational learning, particularly
through an access to the partner's core competencies (Bengtsson and Kock
2000). From a strategic point of view the cooperation with competitors gives the
opportunity to be more flexible and more responsive to the environment.
Coopetition also has potential costs, such as losing control of key activities, information and resources (Håkanson and Ford, 2002).
Similar challenges are faced with regards to relationships between colleges
and business companies. The recruitment activity of global businesses is conducted virtually in all business colleges in Poland and is another example of
cooperation, especially as a few from these global businesses have own colleges
located outside Poland. It is also interesting to look at those companies whose
business and capabilities prove to be a useful resource for the educational establishment they have a relationship with. The clearest example of this can be seen
when looking at financial pulling power of both partners. The business is a party
in possession of a financial surplus. According to research commissioned by
EandY in 2008, Research and Development activities in Poland take place mainly in the public sector. 60% of the expenditure on Research and Development
was financed by the state, while in the EU it was 40%, and in case of OECD
countries – 34% (OECD, 2008; Wolszczak-Derlacz and Parteka, 2008).
15
Universities in Poland have reduced financial resources. An interesting example of cooperation can be seen in research where scientists who have
knowledge and skills that the company’s staff do not possess, benefit from the
knowledge of these companies which is often stored in the form of databases,
specific knowledge and secret knowledge. This is particularly true of research in
the area of macro-economics and finance. Unfortunately, such forms of cooperation in Poland are still rare. Such relationship always benefits and costs both
parties. Whilst there are many positive examples, it is also possible to find a lot
of negative attempts to use “the competition and cooperation”. Many companies
use their position of power to forcing universities which are in a disadvantage
position to take certain actions, such as forcing a university to place their funds
on developing practices and departments which focus on the needs and practices
of that particular company.
The observations carried out in five business universities after the reforms
present a very high willingness to change; there is an annual double-digit rise in
the share of the external research funding and a growing participation in the
teaching process of business specialists.
The identification of the types of relationships at the macro-level seems problematic, since the research activity outside the university cannot be a measure of
coopetition between universities and organizations from the business environment. It
is because the activity in this field indicates the relationship of cooperation.
6. Meso level analysis
The area of research activities is becoming more competitive. Limited
budgetary resources are being divided by the National Science Centre according
to a criteria, which is based on multiple teams of researchers who existing in
competition with one another. Though there are funding bodies which, favour
research carried out jointly by several centres, however they are in the minority
in Polish economical schools and almost seem like a symbolic gesture of cooperation rather than anything else. In Poland, research is customarily conducted
individually, in contrast to other Western countries, where most research is collaborative. The activities of five major business schools give a few examples of
cooperation, such as: joint research conferences, and textbooks and scholarly
monographs written by research teams.
The lack of joint research, as well as the small number of individual research programs, is the result of the fact that researchers are overloaded with
16
teaching work. This is particularly evident in the economic universities, which
are oriented towards financing their activities with tuition fees. This is confirmed
by the previously quoted evidence of studies carried out by EandY. A negative
correlation between the teaching burden and research productivity of the staff
has been observed.
The conclusion is that research and teaching are in fact more competitive,
than complementary (Wolszczak-Derlacz and Parteka, 2008).
7. Micro level analysis
In conditions when autarky, but also maintaining high competitiveness is
ineffective, a partnership can turn out to be the success factor between members
of interacting organizations. According to P. Bourdieu, social capital, which
came into existence as a result of having durable networks of relations, is a collection of resources supported by a mutual acquaintance and recognition, and participation in such an organized network provides each of its members support in the
form of resources – including relational – which is owned by the whole group
(Bourdieu, 1985). In this regard, each member of the network becomes a kind of
node, the agent making their own relational resources available to other members of network. In this way an existing multi-directional plain of contact enhances
the possibilities of the partnership, and each of the entities involved are a potential
node, enabling the further expansion of the network. Anything which connects
with other operators of within the same environment, regardless of their position
and the nature of the relationship may become a node.
Collaborating teams are most often grouped around independent researchers
having considerable research achievements and/or academic position, and most
often solve research problems from narrow research sub disciplines. More and
more of these horizontal structures can be noticed among young staff working
mainly in a virtual environment. In the future, there will be more such examples.
Rules for financing research projects have impact on that. Both the financial
resources coming from the government and the European Union prefer teams
composed of researchers from different backgrounds. It is also possible to find
examples of excessive competition (rat race) that blocks the creation of systems
of cooperation. It is a syndrome of generation Y.
Competition leads to the department’s development measured in: the number of research activities carried out, degrees obtained and the quality of the
teaching process and position in the market. Unfortunately, in the majority of
17
Polish economic universities two examples of activities can be observed. The
first is limiting the faculties and departments ability to collaborate and pursue
strategies of independence (autarky). Understanding the simple rules of synergy
is often difficult. Increased ability of implementing the principle of synergy may
result from of the competition between researchers for research grants. Analysis
of applications submitted to the National Science Centre; show a dynamically
growing research activity in academic staff. Also, activities within a network of
contacts may surprisingly increase such activities.
In practice, researchers, not only the Polish ones, have very narrow specializations. Such a solution should therefore contribute to linking diverse
knowledge and skills while maintaining the possibility of competing for other
resources. Examples of such processes can be observed in research and teaching.
They are: joint research projects, faculties and teaching specialties led through
different departments, joint scientific conferences, a voluntary association of
units in the form of institutes, and cyclical administering of the departments’
affairs. However, the limited scope of such solutions, results from the general
low financing of science in Poland, which has already been pointed out.
Conclusions, recommendations, limitations
The aim of cognitive research is to formulate applications of testing the effectiveness of types of relationship at different levels of analysis. Being able to
carry out examinations which go beyond the formal boundaries of an organization is especially valuable. Showing possible means of exploiting cooperation
benefits and costs from the level meso and macro to the level of the micro organization is another conclusion of these examinations. The greatest difficulty is in
proving that the benefits of such collaborations between different parties are in
fact beneficial for all and that the sum of the whole is greater than its parts.
Changes in the criteria for assessing the effectiveness of higher education,
which are now moving in the direction of those criteria used in other Western
universities, will foster the growth of cooperation in this regard. Apart from rare
examples of collaboration in the form of joint conferences, incidental joint research projects and attempts to implement network possibilities, so as to exchange knowledge, cooperation is practically non-existent. The lowest level of
cooperation exists at the micro level. Here too, it is possible to take note of only
a few examples of institutional cooperation. Many different forms of cooperation
occur at the level of departments and individual employees. However, there exist
18
the benefits which are offered by relationship networks between researchers and
educators, though these exist at a personal/ individual level rather than owing to
a formal contractual agreement. To some extent future research approach should
be oriented to the network level. Studying coopetition at the network level and
maybe the articulation of different levels within the network is good perspective
to extent uor understanding of coopetition in higher education sector.
Co-location, cluster formation, international and national networking, sharing of infrastructure, co-investment in infrastructure and research, are critical for
collaboration.
We recognize some limitations of the study, mainly due to the methodology
adopted and context as well. This study addresses a specific context. Due to this there
is a need for generalization of research finding to allow for further in depth research
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Anna Lipka, Stanisław Waszczak, Alicja Winnicka‐Wejs University of Economics in Katowice, Poland
LOYALTY AND WORKAHOLISM IN THE
METHODS OF HUMAN CAPITAL
EVALUATION (IN) AN ORGANIZATION
– A COMPARATIVE STUDY
A NNA L IPKA , STANISŁAW W ASZCZAK , ALICJA W INNICKA -W EJS
Abstract
The aim of the study is to show how selected methods of human capital
evaluation determine loyalty and workaholism, the features affecting employees`
efficiency. It was found that seven analysed methods of evaluation differ in
terms of ways of defining generators and devaluators of human capital value.
Based on the conducted analysis, it was found that the historical costs method,
the discounted revenue streams method, the HR Balanced Scorecard and the
Saarbrücken formula are not suitable for recognition of loyalty and workaholism
as behavioral sources constituting the value of human capital. The most useful
methods for their recognition were: Mayo monitor, risk – value method and personnel portfolio. The conclusion is that, with the use of the Mayo monitor, the
dynamics of loyalty types and the types of workaholics, which correspond to the
variable in the monitor, may be captured, forming the skills profile. Also the risk
value method may depict a risk map for loyalty and workaholism, for example,
to assess the quality of methods and their diagnosis. In turn, the personnel portfolio allows us to determine the dynamics of the share of different types of
workaholism and loyalty.
Keywords: methods of human capital evaluation, loyalty, workaholism, value
generator, devaluator.
Introduction
Both loyalty and workaholism are terms referring to the qualitative value of
human capital – individual and team capital as well as the whole organization.
Quality, on the other hand, determines human capital value which can be understood
in a variety of ways (among others, as the replacement, strategic or market value). It
is assumed that the way loyalty influences that value is different from the way
workaholism affects it, i.e. loyalty is its generator, and workaholism its devaluator.
The aim of the article is to verify the assumption on the basis of critical
analysis and reference books as well as to deal with the following research issue:
What are the similarities and differences between loyalty and workaholism in
selected human capital evaluation methods and can one identify the methods which
would be more and less useful to determine generators as well as devaluators of
human capital?
26
LOYALTY AND WORKAHOLISM IN THE METHODS…
In the first section of the paper the state of research on loyalty and
workaholism as the features that constitute the quality of human capital were
presented. The evolution and the current stage of development of human capital
valuation methods were also presented in this section.
1. The state of research on loyalty and workaholism
as determinants of human capital quality
Loyalty and workaholism are not the only determinants of the human
capital quality. However, these days they seem to be of great significance.
Employee loyalty can be described as “the (perceived) probability of work
continuance in an organization by an employee with greater or lesser commitment –
and certain emotional attachment towards the organization regardless of its image on
the market – thanks to employee`s or other staff`s well-being or due to lack of
other opportunities to find a different job or high costs of changing the
employer“ (Lipka, 2012, p. 20). Such a definition of loyalty reflects the following
types (singled out according to such inner mechanisms as trust, habit, commitment):
− partnership loyalty (there is trust, habit and positive organizational
commitment),
− commitment loyalty (there is trust and positive commitment),
but it does not reflect the types of loyalty with no commitment, i.e.
− loyalty of convenience (there is trust and habit),
− conscious loyalty (there is only trust),
− loyalty out of habit (there is only habit).
It does not reflect those types of loyalty where employees` commitment is of
negative nature (e.g. employees sabotage the company`s operations), i.e.
− lenient loyalty (there is also trust and habit),
− conditional loyalty (there is no habit, but there is trust),
− helpless coercion loyalty (there is no trust, but there is habit),
− unaccepted coercion loyalty (there is no trust or habit).
The above definition refers to the most desired types of loyalty from the
organization`s perspective. One should keep in mind that commitment, differentiating
those types (apart from trust and habit), includes:
− affective commitment,
− continuance commitment,
− normative commitment (Meyer, Allen, 1991, pp. 67-69).
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Partnership and commitment loyalty (and to a lesser degree loyalty of
convenience, conscious loyalty and loyalty out of habit), especially that of
indivisible and long-term nature, can be a behavioural boost to the human capital
value as well as to the organisation itself for the following reasons:
− the influence on employment stability (so difficult to achieve in times of
increased competition, lack of MVEs and battling to get the best personnel,
even intensified by demographics and changing values), which helps increase
company profitability (Reichheld, Teal, 2007, p. 159),
− smaller loss of customers (and its implications, e.g. increased turnover)
(Gerpott, Paukert, 2011), which is of greater significance to services where
tacit knowledge is vital (Schüller, Fuchs, 2005, p. 29),
− loyalty effect (Schüller, Fuchs 2005, p. 189), i.e. good performance after a longer
period of employment (gaining experience),
− better work quality (the same reasons as in loyalty effect),
− smaller costs for training courses,
− greater resistance to marketing activities of competitive businesses among
persons representing the above types of loyalty, and greater tolerance to lack
of pay rise or even reduced salaries (Buchanan, Gilles, 1990),
− good opinions and referrals contributing to the company`s human capital and
clientele,
− cost reduction relating to turnover and training programmes,
− high staff morale despite witnessing (disloyal) employees leave,
− the viability of planning,
− cost reduction relating to controlling (Šmid, 2003, p. 141),
− lack of limitations in applying the latest management methods (e.g. High
Performance Work Systems).
All in all, loyalty:
− affects attitudes and values (loyalty itself can be labelled as good attitude or
conduct),
− prevents the loss of knowledge, aptitude and skills,
− contributes to company`s healthy relationships,
− improves motivation,
i.e. improves all the components of human capital.
Workaholism, the other qualitative feature of human capital, is described as
“obsessive commitment which is characterized by high employee`s ambitions,
inability to control habits in the workplace, and too much work (work dedication)
connected with lack of the individual`s activity in other areas of life, which
28
results in the deterioration of employee`s well-being and dysfunctional interpersonal
relationships (Wojdyło, 2010, s. 18). This syndrome is considered a type of
addiction (Oates, 1971), which in opposition to other addictions is socially approved
and it is even associated with high job performance. Workaholism is also described
in terms of behavioural aspect (it is represented by A behaviour, which is
characterized by fast pace of life, taking up extremely difficult tasks and setting high
standards), affective aspect (job satisfaction), cognitive aspect (automatic thinking,
fixed opinions and assumptions concerning work), attitudes towards work
(overzealous) or compulsive-obsessive personality (perfectionism, exaggerated
thoroughness, rigidity, inability to delegate work). All the above factors foster
excessive involvement in a working life.
The main symptoms of workaholism include:
− inner obligation to work with no outer coercion (inability to `cut off` from
work and not to think about it),
− recognizing work as the core of one`s life, which determines its purpose and
establishes it as the main value as well as one`s identity and self-esteem,
− loss of control over one`s life in a working environment due to excessive
involvement in one`s work and setting too high, often unrealistic standards
(“lost in work”),
− working life takes over other areas of one`s life (lack of other activities,
working time is extended to leisure),
− erratic behavior which results from applying defensive mechanisms (among
others, ignoring the problem, rationalising longer working hours, compensating
all kinds of emotional deficiencies, denying bad effects of work addiction),
− inability to “abstain” from work without some negative consequences, such
as self-destructive thoughts and emotions (constant thinking about work,
stress, guilt complex, anxiety) (Lipka, Waszczak, Winnicka-Wejs, 2013).
The definition of workaholism and its symptoms imply that it is a syndrome
and a problem determined by personality, entailing negative consequences for an
individual, their family as well as for an organisation. However, is the abovementioned opinion on workaholism not one-sided (too clinical an approach) and
radical? According to some researchers, such as Machlowitz, “workaholism is
an employee`s commitment to career life”, “workaholics are passionate about
their work” and therefore “the organisation reaps benefits employing such
workers” (Golińska, 2011, p. 12). If one considers a broader context, including
work as a priority in a value hierarchy and work ethics, then workaholism seems
to be a natural consequence of high expectations from the family, working
environment and society towards an individual.
29
One of such expectations is conscientiousness, which in Five Factor Theory
by Costa Jr. and McCrae constitutes a dimension of distinctive features – one of
five personality dimensions (apart from neuroticism, openness to experience,
extraversion and agreeableness). Those factors include six components, describing
the aspects of a given factor in detail. When it comes to conscientiousness, its
components are as follows: competence, propensity for tidiness, obedience, an aim
for achievements, self-discipline and prudence – qualities which are very
appreciated in employees. Conscientious individuals being dutiful and disciplined as
well as ambitious and industrious, become workaholics at some point` (McCrae,
Costa, 2005, p. 65). But only conscientiousness taking the form of
compulsion/obsession with work – shown in diagnostic criteria ICD-10 and DSM-IV-TR as exaggerated experiencing of doubts, rigidity and perseverance,
focusing on details, perfectionism interrupting tasks completion – poses a problem
to an organisation because it badly affects its functioning and the quality of
human capital. Therefore conscientiousness may lead to workaholism, but not
necessarily – it depends, to a great degree, on the management style and the
ability to take advantage of human potential, company culture and the situation
on the job market.
The consequences of workaholism are multidimensional. The following
implications can be singled out here: personal well-being, the implications for
workaholic`s social environment and risks for an organisation relating to
employing workaholics. Workaholism affects all the components of human
capital in terms of its value depreciation. And so, excessive involvement in
a working life can badly affect knowledge, impairing individual`s memory,
perception and focus as well as cognitive structures, including thought
activation, hyperactivity of intellectual processes, escapism, reduced intellectual
activity (Kalinowski et al., 2005, pp. 117-119). Accompanying anxiety may lead
to certain losses (emotional atrophy) and a loss of certain skills, such as a skill of
communicating clearly, a loss of empathy and sympathy (Killinger, 2007, pp.
111-153). Constant working typical of workaholism makes a human body
function at full stretch at the cost of good health and well-being. The reduction
of human capital value in this area may lead to bad health consequences and
erratic behaviour – noticeable or inconspicuous (cf. Meissner, 2005, p. 49, after:
Städele, 2008, p. 46). Workaholism can result in concentration problems,
physical and psychological disorders, overtiredness, which can even lead to total
depreciation of human capital (in the case of karoshi – an employee dies of
overworking). Decreased value also concerns motivation. It is known that
30
workaholics are driven by inner coercion. Their motivation for work is
determined by, first of all, a hidden motivation for their ego protection and selfesteem or gaining recognition from their colleagues (Porter, 1996, pp. 70-84).
Workaholism also has an effect on employees` attitudes towards the ways of
performing tasks and their relationships with co-workers. Perfectionism as
setting too high standards and rejecting imperfect solutions can make it
impossible to take full advantage of certain attributes of human capital in an
organisation. If work becomes a paramount value (it becomes “god”), work-life
balance might be upset, which, in turn, affects a person`s functioning in various
spheres of life.
It seems, therefore, that the direction of the impact of workaholism is different from that of loyalty. The quoted examples of lowering the value of all the
components of human capital seem to indicate that workaholism can be described as a devaluator of human capital value. To state that it is essential to use
human capital valuation methods. These methods are – to an extent which is
larger than ever before (cf. Table 1) – in the center of researchers’ interest due to
the fact that they are treated as a component of business entities valuation –
a component which is difficult to measure, but necessary to take into account,
since without it a situation depicted as phase 6 (cf. Table 1) occurs.
HR specialists and managers have to take into account a possibility of using
the results of human capital evaluation in personnel controlling. If an organization
uses the strategy of maximizing human capital value, and generally, the idea of
value management, one has to keep in mind that certain measurement means, e.g.
those concerning loyalty and workaholism must be generally accepted.
Table 1. The evolution stages of human capital evaluation methods
Stages – Period
1 mid 60s, 20th c
2 mid 60s – beginning of 70s,
20th c
3 beginning of 70s – mid 70s, 20th c
4 mid 70s – beginning of 80s,
20th c
5 80s, 20th c
6 beginning of 90s, 20th c until
the present day
Description of the stage
Theory of human capital and the psychology of an organization
The development of the methods based on historical and
replacement costs; Brummet, Flamholtz and Pyle introduce
the notion of Human Resource Accounting
Fist applications of the designed methods in companies
Fewer researches, more combined methods, which leads to
a decreased number of specialists for applying the methods
Fierce competition between US and Japan leads to a greater
interest in the field and greater significance of an employee
in business operations; lack of solutions to the problems
relating to the existing methods
New wave of a greater interest due to the increasing role of human
capital and the existing value gap; change of market-to-book ratio
Source: Based on: Gebauer (2005, pp. 18-230); Dudycz (2005, p. 216); Lipka, Król, Waszczak, Satoła (2008, p. 13).
31
The choice of valuation methods must have regard to organization strategies and boundaries, also methodical ones, to their applicability. In the previous
study (Lipka, Król, Waszczak, Satoła, 2008) it was found that methodology
issues connected with determining human capital value depend on:
− lack of justified assumptions and gaps in descriptions of research methodology,
− difficulties with establishing the basis for determining the value and
assuming research method (the basis is understood as individual or team or
human capital as a whole organization; omitting or taking into consideration
so-called contextual factors relating to human capital management, i.e. the
strategy, structure, company culture),
− problems referring to measuring the variables/ways of acquiring data
(including economic as well as psychological and organizational data),
− difficulties of temporal nature (depreciation of human capital, changeability
of the value generated by human capital in time: using historical and/or
prospective data with the aid of the methods: a need to take advantage of
future values in reference to the present ones),
− feedback problems: human resources management – human capital
evaluation (narrowed down to the efficiency evaluation of HR practices),
− issues referring to the economic situation and management trends (e.g. increasing
variety of human capital in an organization, flexibility, the application of lean
management and work-life balance and downward movement programmes).
2. Hypothesis and testing procedure
The following hypothesis was formulated for the research problem specified
in the introduction:
H1: The methods of human capital evaluation differ in terms of loyalty and
workaholism, which shows that some of them are less and others are more useful
to specify given generators and devaluators of human capital.
In the study, selected evaluation methods were included, belonging in the
following categories: cost (historical costs method), revenue (discounted revenue
stream method), multiplier (Mayo monitor, risk value method), indicator
(Balanced Scorecard), mixed (Saarbrücken formula), alternative (personnel
portfolio) (some authors take into consideration only the first two categories).
Access to the detailed descriptions of these methods and the clarity of algorithms
during their application were considered while choosing the methods.
32
To verify the hypothesis by deduction based on critical analysis and reference
books, the following sources of presenting evaluation methods were used: Brummet
et al. (1968, pp. 221-223), Flamholtz (1975a), Mayo (2001, p. 14), Lipka (2007,
p. 22-24), Scholz, Bechtel (2005, p. 32-36), Becker, Huselid, Ulrich (2002, pp. 65-89).
The following procedure was assumed:
− a description of loyalty and workaholism in terms of their contribution to
a qualitative value of human capital and their influence on all the components of
human capital, i.e. knowledge, aptitude, skills, health, motivation, attitude, values,
− loyalty and workaholism as determinants of human capital value in seven
selected evaluation methods,
− comparative studies and the selection of the most useful method(s).
3. Loyalty and workaholism in selected methods
of human capital evaluation in an organization
3.1. Historical costs method
In the most popular cost method – historical costs method (Brummet,
Flamholitz, Pyle, 1968) – in its “most updated” version, retention investment
costs (i.e. loyalty investment) could be incurred as follows:
− hiring – direct costs e.g. offering competitive remuneration as part of
diversified personnel marketing,
− hiring – indirect costs e.g. loyal MVEs promotion,
− training – direct costs e.g. training programmes improving employee loyalty,
− training – indirect costs e.g. productivity loss during retention investment.
According to this method loyalty gauged by retention time increases human
capital value. As far as workaholism is concerned direct hiring costs can be
taken into consideration as in the case of loyalty, for example the costs related to
hiring a non-workaholic, i.e. a person who can skilfully manage the time and take a
rest after work, taking care of their health, knowledge, skills and motivation. Such
costs are supposed to prevent or intervene in (reduce the effects of) workaholism.
The same objective is ascribed to direct training costs (e.g. costs related to individual
coaching enhancing time management), indirect hiring costs (e.g. employee
reallocation costs preventing their workaholism) and indirect training costs (e.g. time
spent by other workaholics talking to the workaholic employee, during which they
fail to perform their duties). According to this method, like in the case of loyalty, the
higher the incurred costs, the higher the value of human capital (the obvious flaw
here is the assumption that costs reflect value).
33
3.2. The discounted revenue streams
This method, devised thirty years ago by Flamholtz (1975a), is being
revived these days and it is used in human resources in a number of novel
applications. It is strictly connected with the loyalty of employees whose value is
determined by loyalty itself and revenues they generate on various positions they
hold in an organization. According to this method, even those employees who
are loyal with no organizational commitment are valuable provided their work
performance is “average” and they are willing to be promoted. The employees who
represent conditional loyalty are also valuable if their promotion is a precondition
for staying in the company.
The method shows that human capital value increases due to efficient
retention practices (e.g. better fulfilment of employees` needs, such as authority,
group identity or achievement) or a faster career path (when the probability of
leaving the company is smaller than the values in Table 2 for u, w, p, s, v when
other conditions are stable).
Table 2. Employee evaluation changes according to discounted cash flow method –
example
Initial data
Employment date S
Position 31.12. 2012
Average employment period of the S employee in the M company:
Forcast career path of the S employee
34
Year
Possible
positions
Probability of taking up the position
by S (%)
2013
2013
2013
2014
2014
2014
2015
2015
2015
2016
2016
2016
2017
2017
2017
I
II
III
I
II
III
I
II
III
I
II
III
I
II
III
l
0
0
b
c
0
k
e
f
g
h
i
j
d
z
01.01.2009 r.
X
7 yrs
Probability of leaving
the company by S before
the end of year (%)
u
u
u
w
w
w
p
p
p
s
s
s
v
v
v
table 2 cont.
Annual positive cash flow for the position
I
II
III
Discount rate
Specifications
A euro
B euro
C euro
m%
Currents incurred costs
Expected value
(in money units)
Annual positive cash flow for the S
employee in 2013
Annual cash flow for for the S employee
in 2014
(100%-u%) * 100% * A
in 2015
(100%-p%) * (k * A + e * B ) f * C)
in 2016
(100%-s%) * (g * A + h * B + i * C)
in 2017
(100%-v%) * (j * A + d * B + z * C)
Total
Total of the above: D
(100%-w%) * (b * A + c * B)
Discounted
value (in
money units)
(100%-u%) *
100% * A
[(100%-w%)
* (b * A + c *
B +: (1 + m:
100)
[(100%-p%)
* (k * A + e *
B ) f * C)] :
(1 + m: 100)2
[(100%-s%)
* (g * A + h *
B + i * C)] :
(1 + m: 100)3
[(100%-v%)
* (j * A + d *
B + l * C)] :
(1 + m: 100)4
Total of the
above: W
The value of for the S employee on 31.12.2012. W
Source: Own algebraic generalization based on the accounting example in: Bochniarz, Gugała (2005).
Reallocation to higher positions with potentially higher revenue may meet
the expectations of some workaholics (e.g. workaholics with impaired focus),
whereas some of them might not be satisfied (e.g. compulsive-addicted
workaholics or those savouring work). The discounted revenue streams method
does not differentiate among revenues brought by different employees following
the same career paths. It is assumed that the revenues are the same regardless of
the position. So, this method can distort the evaluation of those employees who
are workaholics and whose revenue will be higher in the short run, but in the
long run it will be lower than the assumed average (due to the above-mentioned
somatic, mental and social effects of workaholism).
35
2.3. Mayo Monitor
This method uses the so-called individual asset multiplier including
aptitude, potential, contribution to value and employee`s values compatibility
with company`s values. If loyalty is a value (as in some examples of corporate
culture, such as culture of power and role model (Harrison and Handy); process
culture (Deal and Kennedy); hierarchy culture (Cameron and Quinn); productive
culture (Snyder); harmony culture (Peters); communal culture (Goffee); Eiffel
Tower culture (Trompenaars and Woolliams), and the employee represents the most
desired types of loyalty, this kind of compatibility is scored the highest (2.0 according
to the assumptions of the method). Theoretically, a type of loyalty affects the
generated value. The type itself can be treated as a variable referring to aptitude
profile. Its changing character can be interpreted in terms of potential (for
example, from conditional loyalty to loyalty of convenience, from lenient loyalty
to commitment loyalty, from helpless coercion loyalty to loyalty out of habit or
from conscious loyalty to partnership loyalty). The above implies that loyalty
can be subjected to evaluation with the aid of Mayo Monitor.
Can workaholism be subjected to such evaluation? Certainly, a type of
workaholism and its intensity (the level of its development) belong in the group
of variables describing aptitude profile, that is one of the variables in Mayo
Monitor. However, only in some types of corporate culture (e.g. in “aggressive”
culture, Krawiec, 2010, p. 47), workaholism is considered a value. According to
the assumptions of Mayo Monitor, this type of compatibility should be evaluated
higher than incompatibility. As far as contribution to creating a value is
concerned, it can be insignificant in the case of a relentless workaholic (because
of rigidity while doing a job), bulimic workaholic (working unsystematically),
savouring workaholic (“immersed” in details and failing to meet deadlines) or
narcissistic controller (unable to work in a team). The issue concerning the
factors which determine the possibility of transition between workaholic types is
interesting. It seems that apart from personality qualities, there are other vital
factors, such as the nature of tasks (among other things, the level of their
complexity), the employee`s motivation and preferences (interests,
coercion/satisfaction level), employer`s requirements (high performance, time
pressure), a stage in the employee`s personal life (age) and career life (career
path planning and its progress), and self-awareness (insight into a problem) as
well as making the effort to tackle problems connected with workaholism
(family pressure and support, professional therapy).
36
2.4. The risk value method
The method was devised a few years ago (Lipka, 2007) and it is constantly
being improved. It is assumed that according to a selected analytical and point
method of job evaluation, every employee should get a task/position of the
highest point value which he is able to successfully perform (or meet given job
requirements), having all the necessary skills (aptitude profile). This point value
can be exchanged into money units (it is established how much one point is
worth). However, this is not the whole procedure because it is thought that there
might be specific types of personnel risk when an employee is not able to
successfully perform the task even though they have the necessary skills and
expertise. In other words, the risk destroys, i.e. reduces human capital value
measured by the possibility of successful task completion at a specific difficulty
level (criteria such as physical exertion, routine or working conditions, which are
not related to job complexity, responsibility or team work, should not be taken
into consideration when assessing the difficulty of the task). It seems that
devising risk maps for both loyalty and workaholism is feasible (cf. Table 3) as
well as determining its level (separately for loyalty and workaholism), for
example with the aid of Martin and Heaulme`s equation (1998):
j
i
r=
∑∑ r n
i =1 j =1
i
ij
,
j
∑∑ n
i =1 j =1
ij
ij
where:
rij – risk value,
nij – number of personal risk types in the field with a given value
and a matrix where there is a risk level for specific probabilities relating to the
occurrence and the volume of effects (in Table 3 loyalty risk is 3.66, and for
workaholism risk is 8.80, which means that the risk is high only in the case of
workaholism as it exceeds 5.0).
37
Table 3. Example map of loyalty and workaholism risks
Type of risk related to loyalty (L) and
workaholism (W)
Quality of methods for diagnosing loyalty
types in an organisation (L)
Quality of methods for diagnosing
workaholic types in an organisation (W)
Work efficiency level for workaholics (W)
Selection appropriateness of Employee
Relationship Management strategy (ERM) (L)
Communication skills in team work (W)
Quality of method selection for preventing
workaholism (W)
Quality of method selection for
workaholism intervention (P)
Employee loyalty scheme profitability (L)
Level of loyalty intensity (L)
Loyalty types dynamics (L)
Probability of
occurrence
Level of
effects
Risk
value
medium
medium
4.0
medium
medium
4.0
very high
high
8.0
high
high
7.0
very high
high
8.0
medium
extreme
7.0
medium
extreme
7.0
very low
medium
low
medium
low
high
1.5
1.8
4.0
So, employee value based on analytical and point job evaluation would be
corrected according to risk value. The risk value method includes both loyalty
and workaholism, which means it is an all-purpose method.
2.5. The Balanced Scorecard
Indicator methods measure the level of goal achievement in specific
perspectives. For example, HR Scorecard (Becker, Huselid, Urlich) provides
information concerning human capital in terms of basic human resources
management tools: recruiting, rewarding, development, retention. This evaluation
method comprises both loyalty and workaholism (cf. Table 4).
Table 4. HR Scorecard comprising selected aspects of loyalty and workaholism
Process
Evaluated
aspect
1
Cost
38
Recruiting
Rewarding
Development
Retention
2
Cost of shaping
working
environment
fostering new
employees` loyalty
(L) / Cost of hiring
specialists
identifying
workaholics during
recruitment (W)
3
Benefit costs (for
loyalty) (L) /
Costs of
rewarding soft
skills` (c.f. 2.6)
(W)
4
Costs of
employee
training in the
most desired
type of loyalty
(L) / Cost of
intervention to
change a
workaholic (W)
5
Cost of employee
retention(L) / Cost
of investment in
work-life
programmes (W)
table 4 cont.
1
Time
Quantity
Quality
Feedback
2
Time for
acquiring loyal
(L) / nonworkaholic (W)
employees
3
Motivation
scheme period to
improve loyalty
(L) / reduce
workaholism (W)
4
Period of time
for loyalty
stages (L) /
workaholism
stages (W)
Number of
rejected jobhoppers in
recruitment
process (L) /
Number of
identified
workaholics in
the process (W)
Quality of
recruiting loyal
employees (L) /
non-workaholics
(W)
Number of
employees
included in
compensation
system
improving
loyalty (L) /
reducing
workaholism (W)
Quality of
rewarding loyalty
(L) / intervention
to prevent
workaholism (W)
Number of
managers
included in
loyalty coaching
programmes (L)
/ workaholism
prevention (W)
Managers`
satisfaction from
acquiring loyal
employees (L) /
non-workaholics
(W)
Employees`
satisfaction from
rewarding their
loyalty (L) / from
rewarding antiworkaholism (W)
Increasing
percentage of
employees
highly assessing
loyalty training
courses (L) /
antiworkaholism
courses (P)
Differences in
retention
investments (L)
/
antiworkaholism
(W)
5
Time
implementation of
employee loyalty
scheme (L) / worklife programme
(W)
Number of
employees
included in
employee loyalty
scheme (L) /
cognitive therapy
to prevent
workaholism (W)
Quality evaluation
of employee
loyalty scheme
quality (L) /
Quality evaluation
of behavioral
therapy for
workaholics (W)
Retaining
employees` trust
included in loyalty
schemes (L) /
unchanging
percentage of
workaholic
employees suffering
from the withdrawal
symptoms while at
leisure (P)
Source: Our own additions to HR Scorecard shown in: Fitz-Enz (2001, p. 115).
The method can be used to compare value changes of indicators, yet it does
not provide a result relating to human capital value determination.
2.6. Saarbrücken formula
a)
b)
c)
d)
e)
The following parameters are taken into account in the formula:
number of full-time employees,
market remuneration rate,
loss of expertise (determined by the ratio of the industry-determined period of
expertise validity and years of service in an organisation),
expertise compensation measured by the costs of investment in knowledge
development,
motivation.
39
Loyalty (in the meaning of attitude) is closely connected with motivation.
Employees’ job resignations have an impact on the workforce volume, yet only
up to a point as the latter depends on the number of the newly recruited
employees. Loyalty measured by the years of service in the organisation results
in loss of knowledge (thus, there is a different aspect of the connection between
loyalty and expertise than the one indicated at the beginning). The remaining
parameters do not seem to have any connection with loyalty.
Workaholism, in turn, is connected only with motivation. Therefore, the formula
encompasses more parameters related to loyalty than workaholism. Most certainly the
formula does not measure the loss of human capital value caused by workaholism and/
/or indicate that the value is maintained due to its efficient prevention (e.g. replacement
of traditional motivation systems sanctioning workaholism with “soft” criteria-orientated
systems, such as customer satisfaction – Poppelreuter, 2007, p. 179).
2.7. Personnel portfolio
Personnel portfolio, as opposed to the methods analysed so far, was drawn
up with strategic human resources management in mind, however it may also be
used for human capital evaluation. By means of the portfolio, it is possible to
estimate (unfortunately only approximately, which is a drawback of the method)
the share of individual employee segments, e.g. representing particular loyalty
intensity (which is gradable) or the degree of workaholism (cf. Figure 1 and 2)
among the employees in an organization.
Loyalty towards the organisation team
Loyalty only towards the team
Figure 1. Personnel portfolio of loyalty intensity
Z
V
X
Y
Divisible loyalty
Indivisible loyalty
Explanation: the most positive influence on building the value of human capital is depicted by square V.
40
Workoholism – predicted state
Figure 2. Personnel portfolio of workaholism degree
III
A
B
C
II
D
E
F
I
G
H
K
I
II
III
Workoholism – present state
Explanations:
Degree I – low.
Degree II – medium.
Degree III – high.
Interpretation:
A, D – unfavorable (from the point of view of human capital) workaholism development leading
to the decrease in human capital value (with ceteris paribus),
B, E, G – stopping the unfavorable dynamics of workaholism – without influence on the dynamics
of human capital value (with ceteris paribus),
C – the highest and constant workaholism having negative influence on human capital value
F, H, K – achieving the effect of a converted workaholic – influence on the increase of human
capital value (with ceteris paribus).
The method enables grasping the dynamics of different loyalty types or
workaholics. Thus, it indicates their rising or falling share in individual segments
of intra-organizational labor market. Distinguishing these segments may be
helpful in optimizing the process of increasing human capital value. The
following categories may be distinguished during the process of increasing the
value in connection with shaping loyalty within the ERM (Employee
Relationship Management) strategy:
I. Most Valuable Employees,
II. Most Growable Employees,
III. Below Zero Employees,
due to the fact that “not all internal clients will be satisfied with the same products and
personal services” (Stotz, 2007, p. 32). In personal marketing particular attention needs
41
to be paid to the first of the segments – which is a value generator. Otherwise (even in
a situation of a surplus of labour supply over labour demand, because it concerns
persons who do not have problems finding a new job), the likelihood of job resignations
will grow higher and an increase in human capital value will not take place.
In the case of workaholism, transitions are also possible, e.g. from a committed
workaholic to a workaholic who limits their commitment to work, which may be
depicted by means of a portfolio. The size of segments used for indicating
changes in the share of particular types of workaholics may be determined via
such methods as Work-BAT scale by Spence and Robbins, WART by Robinson
and Philips, SZAP by Golińska, KOP Hornowska and Paluchowski or UWES by
Schaufeli and Bakker (Szpitalak 2012, pp. 73-79; Golińska, 2008, pp. 54-67).
It must be emphasized that these segments may be determined in a quantitative
aspect (percentages of employees who belong to them), which opens up the
possibility of comparisons and evaluations – even monetary ones.
Conclusions
Critical analysis and evaluation of reference books showed that human
capital evaluation methods vary with respect to the possibility of including value
generators connected with loyalty and devaluators connected with workaholism.
In the seven selected evaluation methods, basing on the following groups: cost,
revenue, multiplier, indicator, mixed and alternative, it was identified to what degree
the determinants of human capital value such as loyalty and workaholism were
taken into account and the suitability of the methods was evaluated (cf. Table 5).
Table 5. Selected methods of human capital evaluation of/ in an organization
and an inclusion of loyalty and workaholism in them
Name of the
method
1
historical costs
method
(R.L. Brummet,
E.G. Flamholtz,
C.P. Pyle)
discounted
revenue streams
method
(E.G. Flamholtz)
42
Loyalty
Degree/taken
Evaluation
into account
of suitability
2
3
Workaholism
Degree/taken
Evaluation
into account
of suitability
4
5
yes, indirectly
low
yes, indirectly
low
yes, directly
medium
yes, indirectly
medium
table 5 cont.
1
Mayo monitor,
(A. Mayo)
risk value
method
(A. Lipka)
2
yes,
indirectly
yes,
directly (when
the map of
personnel risk
includes loyalty)
HR Balanced
Scorecard
(B.B. Becker,
M.A. Huselid,
D. Urlich)
Saarbrücken
formula
(Ch. Scholz,
V. Stein,
R. Bechtel)
personnel
portfolio
(G.S. Odiorne)
3
4
5
high
yes, indirectly
high
high
yes,
directly (when the
map of personnel
risk includes
workaholism)
high
yes,
directly (when it
includes
indicators
concerning
employee
loyalty)
medium
yes,
directly (when it
includes the
indicator of
workoholics
medium
yes,
directly in
selected
parameters
medium
yes,
directly in one
parameter
low
high
yes,
directly
(when it includes
the degree of
employees’
workaholism)
high
yes,
directly
(when it includes
the degree of
employee
loyalty)
Evaluation basis
The degree to which the determinant was considered:
a) directly (the selected determinant is included in the formula, there is
connection with loyalty or workaholism,
b) indirectly (the selected determinant is not included in the formula;
specification is required in relations to the selected component of evaluation;
there is lack of close connection with loyalty or workaholism).
Suitability evaluation:
− low (due to false methodological assumptions; there is lack of formula
measuring the increase in or loss of human capital value),
− medium (distortion of the evaluation due to lack of diversification between
revenues brought by a variety of loyal/ workaholic employee types; difficulty
in delivering the result in the form of specified human capital value),
− high (easiness in determining human capital value).
43
Table 5 shows that loyalty is included directly in as many as five analysed
methods and workaholism in four of them. Most of the methods takes into
account both loyalty and workaholism, thus indicating their universal nature.
However, the suitability for including particular generators and devaluators of
human capital varies to a large extent. The least suitable method turned out to be
the historical costs method whereas the highest suitability value was awarded to
the monitor Mayo method, risk value method and personnel portfolio method.
The remaining methods present a medium level of suitability for the
determination of human capital value taking into account loyalty/workaholism.
As part of further research on these issues different typologies of workaholism
and loyalty could be taken into account. The hypothesis formulated and verified
in the article could be tested by being applied to other generators and devaluators
of human capital, such as different aspects of attitudes towards working time.
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46
Dominika Latusek‐Jurczak Kaja Prystupa‐Rządca Kozminsky University, Poland COLLABORATION
AND TRUST-BUILDING
IN OPEN INNOVATION
COMMUNITY
D OMINIKA L ATUSEK -J URCZAK , K AJA P RYSTUPA-R ZĄDCA
Abstract
Growing popularity of open innovation communities poses various challenges for business practice. One of them is trust, which facilitates social interaction, provides basis for risk-taking and strengthens cooperation. In virtual environment traditional mechanisms of its development are unavailable. However, in
many companies using virtual teams trust is created, maintained and capitalized,
which provides indication that it may be developed in other ways.
In this paper, the authors present a study of work within testing community
in computer game industry based on two-year qualitative fieldwork, which may
serve as an example of trust emergence in virtual environment.
Keywords: open innovation community, trust, virtual teams.
Introduction
The advancement of the web and mobile communications has led to a globally shifting movement away from business’ brick and mortar team structures to
innovative technical teams working with more interactively connected technologies. The growing popularity of open innovation communities is grounded in the
idea that “firms can and should use external ideas as well as internal ideas, and
internal and external paths to market, as they look to advance their technology”
(Chesbrough, Vanhaverbeke, West, eds., 2006). However, the use of open innovation communities poses various challenges for all actors engaged. One of them
is trust, which is particularly difficult to create, maintain and repair in virtual
environments (Cook, Snijders, Buskens, Cheshire, eds., 2009; Knights, Noble,
Vurdubakis, Willmott, 2001).
This paper applies a two-year qualitative fieldwork within the computer
game industry testing community to develop a newly applied understanding of
trust challenges in the open innovation communities.
Trust facilitates social interaction, provides basis for risk-taking and
strengthens cooperation. Trust is a necessary component of team environments
supporting and executing innovation. Traditionally, trust building processes are
enabled by mechanisms such as repeated interaction (e.g. ensuing familiarity)
and stabilizing third parties (e.g. institutions in various forms). In the context of
distributed teams (Bosch-Sijtsema, Fruchter, Vartiainen, Ruohomäki, 2011) and
48
COLLABORATION AND TRUST-BUILDING…
cooperation taking place in virtual environments (Cook et al., eds., 2009), these
traditional mechanisms are usually unavailable.
The open innovation community can be defined as “as a group of unpaid
volunteers who work informally, attempt to keep their processes of innovation public and available to any qualified contributor, and seek to distribute their work at no
charge” (Flemming and Waguespack, 2007, p. 166). The model gained popularity in
knowledge-driven sectors, inter alia through game development companies. Taking into account rapidly changing industry trends and customers’ preferences,
the game development market is considered risky business venture, since ultimately the game may not meet customers’ preferences, and such preferences
may be more nuanced and difficult to understand across virtually diverse communities (Prato, Feijoo, Nepelski, Bogdanowicz, Simon, 2010). To minimize
such risk companies customarily test their products before officially launching
them by engaging people from outside of the organization.
Game production companies use different strategies of implementing external gamers’ into their projects. They vary in their decision about when to engage
the outsiders, from where to acquire them, how to communicate with them, and
how they should protect their product legally. The number of game testers in
focus groups differs according to the size of the game; however, business practice suggests it not smaller than several dozen. Smaller organizations possessing
limited budgets cannot afford to pay for testing, and they often seek volunteer
testers. At the same time, for small companies, the need for rigorous testing
phase is even more essential; due to limited resources, they depend much more
than on the success of each single game than big companies do.
This creates an interesting situation where, on the one hand, a company
needs to guard its intellectual property since its loss would be equal to the failure
of the product. On the other hand, however, the company needs to disclose sensitive information about the game to the group of volunteers who cannot be effectively monitored and controlled.. Prior research indicated that trust is the alternative mean of control in the case when legal or official protection is
unavailable. However, the development of trust in virtual environment is more
complicated than in traditional circumstances of cooperation. Therefore the aim
of this research is to examine the how is trust created, maintained and capitalized
on in open innovation communities. The study has exploratory nature as this
issue has not been examined at large (Fleming and Waguespack, 2007).
In the first section of this article we examine possible sources of trust in
case of no prior experience with peers which is the situation common in open
49
innovation communities. In the empirical part we present the case of Cubicon,
the company that engaged the community of gamers in testing of their new
product. In the discussion part we use the concept of swift trust to explain the
process of trust building in the specific circumstances of the case.
1. Types of trust
Trust can be defined as “the willingness of a party to be vulnerable to the
actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control
that other party” (Mayer, Davis, Schoorman, 1995, p. 712). Trust is usually
a product of repeated interaction; it is gained in time through mutual group
member experience in collaboration and is supported by stable environments and
third parties. But contemporary life has situations where participation and collaboration is required without any prior knowledge of partners (Latusek and
Cook, 2012). Existing research indicates three possible sources for trust in situations when previous experience is unavailable:
− Stable institutions (Latusek and Cook, 2012),
− ‘Generalized trust’ (Sztompka, 1999),
− ‘Swift trust’ (Meyerson, Weick, Kramer, 1996).
2. Stable Institutions
Networks are a key source of social capital, and they reflect how rich in social capital that the society is (Cook, Hardin, Levi, 2005; Lin, 2001). In complex
environments characterized by uncertainty, as are most contemporary societies,
exchanges may occur among actors who are able to restrict or limit uncertainty
to a level that makes the risk of cooperation acceptable. This security may be
provided either by some form of reliable institutional backing when trust is unavailable. In modern societies, where most exchanges are impersonal, institutions
play a crucial role (North, 1990).
Note that in those settings where trust matters most (under high uncertainty
and obvious risk) individuals are least likely to rely on trust and most likely to
require more formal mechanisms of coordination and control (Cook, Rice,
Gerbasi, 2004). ‘Reliability’, however, does not equate to requirement of trustworthiness, and we know that partners may be reliable due to institutional forces
50
(Cook et al., 2005). Supporting this claim empirically, Yamagishi, Cook,
Watabe (1998) experimentally separated assurance relations from trusting relations. It is important to remember that those institutions may lead to more secure
and cooperative behavior, they may not automatically produce trust (e.g. Latusek
and Cook, 2012; Sitkin and Roth, 1993; Yang, 2007).
3. Generalized Trust
People exhibit different levels of trust; some of people are more trusting,
and some are less trusting (Fink and Kessler, 2010; Fukuyama, 1995; Putnam,
1993; Putnam, 2000; Yamagishi and Yamagishi, 1994). The propensity to trust
which Tanghe Wisse and van der Flier (2010) define as the extent to which people have a general belief in the goodness of human nature is an amalgamation of
various factors, but, to a large extent, it is also a culturally learned attitude
(Mayer et al., 1995; Tanghe et al., 2010). Cultural attitudes emerge from accumulation of collective experiences shared by groups of people; in other words,
they are a product of history (Sztompka, 1999). As part of collective framework
of perception and interpretation, trust governs individuals’ behavior.
In Poland, the level of ‘generalized trust’ is comparatively low (Gerbasi and
Latusek, 2012). Such research finding of this is explained usually through the
country’s history (Sztompka, 1999). Today, Poland is still transitioning out of its
time under communist rule, even though it has been 20 years from the start of
transformation. The socialist state fostered a culture of suspicion and hostility,
and the little social capital that remained after decades of life under Soviet domination was subsequently destroyed by transformation to a capitalist state of the
early 1990’s. Under the socialist state, there was a strong reliance on closed networks of trust. Individuals accomplished many everyday tasks outside of the
state system through networks of trusted associates (Marin, 2002). The uncertainty that accompanied the transition away from socialist rule reinforced this
reliance on interpersonal bonds, which provided security and continuity. While
closed networks, such as a person’s relationship with their family members,
provided a safety net during times of change and uncertainty, an individual’s
reliance on these networks had negative consequences; this created a base for
corruption and cronyism (Peev, 2002; Rose-Ackerman, 2001a; Rose-Ackerman,
2001b). The patterns of social life formed under Soviet domination turned out to
be a two-folded problem, because, in one way, distrust towards the state and
reliance on personal connections was a useful defense against oppression and
51
provided shelter from totalitarian control. In comparison, it also did not contribute to building a more open society (Baier, 1986; Cook and Gerbasi, 2009;
Hardin, 2002; Meyerson et al., 1996; Williamson, 1993)
4. Swift trust
In an environment where traditional way of trust emergence is hindered by
time restraints or limited interaction between actors (e.g. temporary groups),
researchers have proposed a notion of ‘swift trust’ (Meyerson et al., 1996). It is
portrayed as a unique form of collective group members’ perception and relating
that is capable of managing issues of vulnerability, uncertainty, risk and expectations’ (Meyerson et al., 1996). ‘Swift trust’ emerges in circumstances where
there is limited pool of possible coworkers, which increases the speed of diffusion of information about each performance; in turn, this makes an individual’s
reputation more vulnerable. For instance, freelancers, who being part of an industry-specific network, are very vulnerable to others’ opinions because it may
affect their future employment possibilities. Moreover, a contractor’s reputation,
for instance team leader, plays an important role in the team development and
acceptance process as he or she is entrusted with selection of team members
(Kawin, 1992). In other words, team members presume that their engagement in
a project was measured on conscious criteria.
Task-related work enhances role-based interaction and emergence of more
stable and standardized expectations based upon terms of task and specialties
(Meyerson et al., 1996). Moreover, cooperation in such circumstances ensures
more interactions that are frequent and provide immediate experience with another partner. If there was a possibility of many immediate disappointments in
cooperation after this experience, a more rapid development of trust or distrust
would be expected (Gambetta, 1988).
‘Swift trust’ will appear in situations where uncertainty is high and unacceptable, and there are premises showing trustworthiness, as social situations
provide a cultural expectation of good will rather than ill will (Meyerson et al.,
1996). To reduce uncertainty, people rely on predisposition, categorical assumptions and implicit theories to move them toward the greater certainty of trust or
distrust (McKnight, Cummings, Chervany, 1998). Moreover, ‘swift trust’
emerges in situations where the risk of disappointment is smaller than the value
of advantages associated with taking this risk
52
5. Method
The aim of this research was to discover how trust develops in open innovation community. The research question was exploratory in nature, as mechanisms
of trust development in such environment were not examined in prior research. For
this purpose, the authors used qualitative approach based on grounded theory
(Glaser and Strauss, 1957; Konecki, 2000). According to this methodology, the
development of theory is the derivative of empirical data analysis, which directly
refers to observed reality. The nature of the research question that examines
social process induced the author’s decision to use qualitative analysis.
The authors decided to choose Cubicon case for several reasons. First, it describes the development of product which was later recognized by the public as
one of the best games worldwide of in its’ niche. Second, it serves as example of
successful virtual collaboration. Third, Cubicon embodies common ambitions of
many indie developers a group of friends with ambitious goal to manage small
development studio and design worldwide-known games.
As the method of qualitative analysis, the authors followed a case study approach (Yin, 2003). The basic techniques of data collection were semi-structured
interviews, a company blog, and an online forum. The interviewees were asked
about: game development process, launch and functioning of the community, management of critical situations in the project and reactions of other members, premises
of trust development towards others, and risk associated with cooperation.
Blogs are particularly useful in qualitative research as they allow researchers to examine social processes over the time, having insight into everyday life
of the team members (Hookway, 2008). Whereas, online forum was a rich
source of evidence of community members interactions. The interviews were
conducted in the period of April-June 2012 with all employees of the company;
the documents used in the analysis were from the period 20.11.2006-29.07.2012,
consisting of around 450 pages of documentation.
Data was coded and analyzed with qualitative research software Dedoose.
To maintain credibility of the results, the authors used the data triangulation
method. The identities of the interviewees in the text are coded according to the
agreement between the researchers and the organization under its study.
6. The case
Cubicon was a small game development company with four full-time contractors based in Poland. A young Polish game designer Greg Grudzinski and his
53
previous coworker, graphic designer, Lena Czerwona, launched it in 2011. The
rest of the small team worked from a distance. Uwe Andreassen, a Norwegian
programmer who became acquainted with Greg through an online community
devoted to Wizzardy. They already had a chance to work on one project. The other
specialist hired, Bob Eastman, was a British music composer who also worked earlier with Greg and maintained contact with him through community. The founder,
Greg, himself was an experienced game designer who worked both for small and
big Polish companies, starting his professional carrier at the age of 16.
The company focused on development of games from the niche genre
called visual novel, targeting the segment of well-educated women of the age
20-35. Despite being experienced in game development, the team disposed limited knowledge about this particular genre. However, the decision to enter the
segment was based on several premises. Namely, visual novels had lower production cost and were developed quicker than other games, such as, for example,
RPG*, the demand exceeded supply, and the team would be able to present their
unique graphic skills. The team fought to compensate the lack of knowledge and
experience by conducting market research through reading different forums,
blogs and playing games. Because of the investigation results, they developed
project called The Snow White. Not having enough experience in visual novels,
Greg decided to engage a gamers’ community to test the project.
7. Motivations to engage Cubicon’s online
gaming community
Greg started active participation in the gamers’ community when he was 10
years old. He was not only playing games, but also commenting others work. He
admired people who non-professionally developed games. Finally, Greg decided
to give himself a chance and designed small RPG game, Wizzardy. In similar
vein as other developers he consulted it with the gamers’ community. Finally, he
published the game on his own website and launched forum, in order to receive
more feedback.
Online forum, where the game was discussed, was not only a place for bug
reporting, but also served as a general discussion about Wizzardy and other interesting games. Some of forum members volunteered for beta testing. Among
them was Uwe, who had dual motivation in helping.
*
54
RPG – role-playing game, where the gamers takes the role of a character in fictional setting.
“I was fascinated with that game, but it had many bugs. I wanted to see a better
version of it. […] That time I planned to develop my own game. Beta-testing
seemed good opportunity to get useful experience”. (Uwe)
Uwe respected Greg for the effort and courage that he presented by improving
his game.
Meanwhile, Greg started his professional career at the biggest Polish game
company as a game tester. The company presented a completely different strategy towards knowledge sharing than he had been accustomed to in his online
Cubicon’s gaming community. His new employer also organized beta testing
sessions before each product launch. However, the procedure looked different
than he experienced earlier. Testers were invited to a big conference room; they
could not leave the room unsupervised before the official end of game beta testing. Moreover, the company introduced various procedures to secure confidentiality and intellectual property of their products. Greg did not appreciate this approach and decided that he would have never followed such methods. When
Greg decided to start his own business, he was sure about engagement of the
gamers’ community in the project. The online community gathered around his
earlier production was a ready-made solution.
8. Members of the Cubicon’s community forum
The forum was opened for everyone interested in Greg’s productions. The
primary condition permitting access to discussion was registration, which required electronic submission of a nickname, surname, and email address. The
profile was verified through email confirmation. The demo version of the game
was available for all after it was published on website of the company. However,
the access to more advanced versions was restricted to those community members who expressed interested in testing. The finished game was not available
online, but it was transmitted to each person individually via email.
Participants of the forum were from different countries, backgrounds and
professions (Table 1). Membership included gamers as well as small game producers of various experience levels and industries. As the forum was primary
dedicated to a genre of RPG games, the fans of visual novels started to join
gradually. Information about the new visual novel production was spreading
through various online communities. Individuals fascinated with this genre and
actively participating in online communities were a rather small group where
most members knew each other through online interactions.
55
Table 1. Resume of bios of some community members
Participant
A
B
C
D
E
F
G
H
I
J
Description
Game producer with 12 years of experience in the industry; journalist of one of
the biggest international game portals; winner of East Design Contest
23-years old biology student; fan of RGP and visual novels living in U.S. California
23-years old American studying Japanese linguistic in Japan; fan of visual novels
18 year old American; started playing RPG games; presently fan of visual novels
20- years old; lives in the East of U.S.; poetry lover
Australian who finished programming at the university; tried to develop games on
his own
22-years old British programmer from big international game development company
Norwegian consultant; afterhours game developer
16 years old American, developing games since the age of 10
Teacher of mathematics form Louisiana
As on most of online forums, Greg introduced a post calculator that enabled
track frequency of each member’s participation in discussion. The number of
posts written on the forum was deciding about the rank of each participant. Thus,
testers could easily determine the engagement of others participating in discussions of community. Moreover, being active on the forum allowed to build reputation among gamers.
9. Rules of the community
Greg did not impose any rules by himself to community operation. At the very
early beginning of forum existence, one of the community members posted general
rules of behavior, which were standard requirements in the virtual environment.
“These are the general forum rules: no spamming, no insulting, no bad words, no
flaming don't go off-topic in topics, no nudity, no other bad things you can think
of that they're bad. Please follow these rules and everything will be fine”. (A, posted
on 29.03.2007)
Rules were not violated by the community members. Interestingly, the topic
presenting the rules had been viewed only 2596 times (data from December 2012)
which made it one of the least popular topics broadcasted by forum members.
While working on The Snow White, Greg maintained a style of interaction on
the forum as when he was working on Wizzardy, leaving space not only for his project. Often, as a seasoned game developer, he served as a mentor providing development advice and assistance. He kept his responses as immediate as possible, to
56
maintain trust, and in cases of prolonged silence, always apologized. Open for criticism, Greg answered all questions and discussed reasoning of his decisions.
“Hey, sorry for a bit late reply to this. Major thanks for the feedback. Posts like
this are very useful for me as a developer. I can't promise I'll fix everything in
The Snow White (I'll try though) (…) [responses to propositions –AUT]:
3) This is actually a technical limitation resulting from how the scene system
was made. The game is able to rewind only within the current scenario (a mini segment of a scene). I know it can be a problem and I'll try to find a solution for it eventually. It's something that is more likely to happen in the future project, though.
4) Okay, I can add that. It's a bit more complex, so I'm not sure if I'll do it in The
Snow White or in the next game” (Greg, posted 11.07.2012)
In his opinion, any advice required comment since individuals devoted their
time to prepare it. Following such principles required great effort from Greg;
sometimes, he barely had time to implement recommended corrections.
Greg tried to compensate from vague community support by sharing details
of the game development and releasing upgraded versions. He discussed personalities of the characters and possible scenarios, as those were aspects of the
community gamers’ interest. Participating in the decision-making process engaged community members even more in the game.
10. Findings
For many game producers, the knowledge about product details is guarded
by security systems. Employees are obliged not to disclose information about
ongoing projects. Nevertheless, some small firms as Cubicon consciously take
the opposite strategy and share their project with a wide range of people by engaging gamers’ communities in testing from the very beginning of production
process. Yet, a company risks loss of their product. As discussed in prior sections, the company possesses little control over the behavior of community
members. The relation between the company and community is based on mutual
risk and trust bets.
The risk that Greg took was quite immense. Seasoned community members
could have easily stolen, copied, or illegally distributed his game idea. Although,
formally, his intellectual rights were secured, within the legal jurisdiction system
in Poland, potential misconduct of one of testers would most probably have gone
57
unpunished. Greg’s forum for discussion about The Snow White enabled him to
receive many helpful suggestions from the testing community. Moreover, he
compensated his lack of knowledge about the visual novel niche through community members who provided him with segment preferences. As a result, the
game won awards in various game contests and gained popularity among online
gaming players. Moreover, Greg gained valuable information about marketing
activities available for indie developers, like his company, Cubicon, and the
methods of negotiation within different publishers. Thus, he was able to plan the
promotion and effective development of the game, and he modified the overall
company strategy more efficiently.
Community members entrusted Greg with their time and online reputations.
Some of them even financially contributed to the game by pre-ordering his game and
paying online. When the company started lacking financial resources, they believed
that Greg would finish the project, and their contribution would not be wasted. Their
trust was put to the test a few times due to the often changing the project deadlines.
“Do you still plan to release the Snow White? As a customer, which purchased
the pre-order, i start to worry”. (online forum member, 12.04.2012)
When some of the forum members started to ask whether the game was going to be
published, Greg devoted a lot of attention to such posts and tried to explain the delays.
“We’re very sorry that the development takes longer that it was planned. […] We try
to make good game, and we are stuck with correcting it […] the money that you
gave us allowed to pay electricity bills in November”. (Greg posted on 12.04.2012)
In addition Greg started to publish on the company’s blog the descriptions of the
project progress and more screens from the game. He also was transparent with the
money he received providing financial information on how he spent the donations.
11. Discussion
As the case indicates, in the open innovation community that we studied,
the traditional mechanisms facilitating trust building were missing. The fieldwork, however, indicates that trust indeed existed between members of the
online gaming community, and the collaboration resulted in the successful
launch of the product, a visual novel named The Snow White.
In this concluding section of the paper, we would like to describe how elements from three concepts related to trust building were creatively used by
58
community. First, as far as institutions are concerned, the issue of reputation in
online interactions should be discussed. People participating in the forum had
risked their reputations online and offline, as they willingly disclosed their identities and made tangible contribution to the development of the game. Moreover,
through participation in the forum, they built their recognition and credibility
among other gamers occupying the niche (Jemielniak, 2013). The process was
time-consuming as the registration on the forum required no confirmed credentials, which in prior research were indicated as a condition of effectiveness of social
activities (Johnson, 1997). The recognition in the community is important from the
point of creating an expert position, but also, it provides insight into possibilities of
future cooperation within a team, such as in the case with Uwe and Bob.
Second, considering “generalized trust”, the phenomenon of choosing environments that are collaboration friendly, where the available context, (in the
case: Poland) is characterized by a low-level of generalized trust (Gerbasi and
Latusek, 2012). Transfer of interactions to virtual reality and international composition of the group allowed the online community to overcome difficulties
associated with the national cultural framework.
Finally, “swift trust” interactions within the community we studied were focused on a role-task approach, which reinforced the professionalization processes within the group (Meyerson et al., 1996). Specific behaviors displayed by
Greg and his online Cubicon gaming forum collaborators included keeping
short-term promises, applying quick response time to messages between members, and having a goal-orientation motivation bring to mind the tools facilitating
the emergence of “swift trust” within the online gaming community.
This research brought interesting insight to the debate about necessity of
development of interpersonal trust in online communities. While some authors
(Jones and Bowie, 1998; O’Leary, Orlikowski, Yates, 2002) underline its’ significance other researchers (Jemielniak, 2013) claim that in may be substituted
by bureaucratized procedures. The Cubicon case indicates that trust may be
formed as a mixture of institutional measures and norms ruling the cooperation
of open innovation community.
Presented study has some limitations. The chosen method of inquiry, i.e.
qualitative approach based on single case study method, does not allow for statistical generalization of the results. Therefore there should be conducted more
elaborated research that would operationalize the presented model of trust development in open innovation communities and verify it on a larger population.
Moreover, authors following this research method should be careful with pre-
59
senting recommendations for practitioners as the obtained results may be context
sensitive. Therefore, researcher should supply readers with dense description
(Lincoln and Guba, 2009), in order to allow them to measure the degree of transferability of findings into particular context.
Nevertheless, the result of this comprehensive literature review and analysis
will help future virtual team leaders and gaming founders fully understand respond to the leadership challenges of trust and communication in open innovation communities, further opening expansive networks of connective solutions
that encourage an inspire innovation.
Acknowledgements:
The project was financed from the funds of National Center of Science upon
decisions no DEC- 2011/01/N/HS4/04414.
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62
Frédéric Le Roy University of Montpellier 1 and Groupe
Sup de Co Montpellier
Famara Hyacinthe Sanou University of Montpellier 1 – ISEM
DOES COOPETITION STRATEGY
IMPROVE MARKET PERFORMANCE?
AN EMPIRICAL STUDY IN MOBILE
PHONE INDUSTRY
F REDERIC L E ROY , F AMARA H YACINTHE S ANOU
Abstract
A central question about coopetition is its impact on performance. Past researches on this question obtained mixed results. No past researches have attempted to evaluate the impact of coopetitive strategies on performance compared with other strategies vis-à-vis competitors: aggressive, cooperative or
coexistence strategies. In addition, there have been few studies that attempt to
establish a relationship between coopetitive strategy and market performance. In
order to fill these gaps, this research studies the impact of coopetitive strategy on
market performance, compared to the impact of aggressive, cooperative and
coexistence strategies. An empirical study is conducted in the mobile telephony
industry. The method is a structured content analysis that identifies the strategic
movements of mobile operators from different countries and geographical regions.
The results show, first, that only three strategies may be identified in the industry:
aggressive, cooperative and coopetitive. The results show, second, that the market
performance depends on the strategy adopted toward competitors. A coopetition
strategy seems to perform better than either an aggressive or a cooperative strategy.
An aggressive strategy is more effective than a cooperative strategy.
Keywords: aggressiveness, cooperation, coopetition, market performance, mobile telephony industry.
Introduction
Since the seminal book of Brandenburger and Nalebuff (1996), coopetition
has been the subject of an increasing amount of research in the field of strategic
management. Researches on coopetition have been developed in many directions, to the point that today it is difficult to make a complete synthesis (Yami et al.,
2010; Bengtsson and Kock, 2014; Czakon et al., 2014). An essential question asked
about coopetition is that of its impact on performance. From the beginning,
coopetition theory has been resolutely normative. For Brandenburger and Nalebuff
(1996), coopetition is a strategy that will lead to better performance. This normative
point of view has not been questioned and is always considered as relevant in
coopetition theory (Bengtsson and Kock, 2014; Czakon et al., 2014).
Some past researches are dedicated to establish empirically a relationship
between coopetition strategies and performance. In this way, some first studies
64
DOES COOPETITION STRATEGY IMPROVE MARKET PERFORMANCE?…
highlight the impact of alliance between competitors on economic and financial
performance (Luo et al., 2007; Ritala et al., 2008; Oum et al., 2004; Kim and
Parkhe, 2009). Other studies attempt to show the impact of cooperation among
competitors on innovation (Belderbos et al., 2004, Neyens et al., 2010; Nieto and
Santamaria, 2007; Peng et al., 2011). Latest studies directly use the concept of
coopetition to attempt to establish its impact on economic performance (Marques et
al., 2009; Morris et al., 2007), on innovation (Quintana-Carcias and BenaviedsVelasco, 2004; Le Roy et al., forthcoming) or on market performance (Ritala, 2012).
Researches which study the link between coopetition strategy and performances are however far from exhausting the subject. They do not identify the
impact of coopetition strategies on performance compared with the impact of
other strategies. The supposed superiority of coopetition strategies over other
strategies, like aggressive strategy or cooperative strategy, has therefore never
been tested empirically. It is also noteworthy, except Ritala (2012), that none of
this research addresses the impact of coopetition on market performance. In the
original definition of Brandenburger and Nalebuff (1996), coopetition is supposed to allow rivals to increase the overall value they create for the customer,
which should enable them to develop their sales. But just one empirical study is
dedicated to this central point of coopetition theory (Ritala, 2012). The present
research therefore aims to fill this double gap, by trying to establish empirically
the impact of coopetition strategy on market performance, and by comparing it
with the impact of cooperative and aggressive strategies.
To this end, this research analyzes the strategies implemented by firms in
the sector of mobile telephony. The method used is structured content analysis.
This method makes it possible to identify the strategic movements of mobile
operators from different countries and geographic regions. The results show that
mobile operators adopt different strategies in the same industry, deciding to follow an aggressive strategy, a cooperative strategy or a coopetitive strategy.
There is also the possibility that firms might adopt a strategy that we describe as
the coexistence strategy, where show neither a strong tendency to cooperate, nor
a strong tendency to aggression. However, in our sample there appear to be no firms
that adopt this strategy. The results also show that the market performance of a firm
depends on the strategy it adopts toward its competitors. A coopetitive strategy appears to perform better than either an aggressive strategy or a cooperative strategy.
An aggressive strategy appears to perform better than a cooperative strategy.
The results obtained in this research contribute significantly to the literature
on coopetition. This is the first study comparing the relative impact of aggres-
65
sive, cooperative and coopetitive strategies on market performance. It is the first
time that these three strategies have been clearly identified in a sector of activity.
It is also the first time that the superiority of coopetitive strategy over the other
two strategies has been shown empirically. Finally, this research shows that an
aggressive strategy is the second best strategy, while a cooperative strategy appears less effective for market performance.
1. Theoretical background
Coopetitive Strategies
Coopetition theory was first formulated by Brandenburger and Nalebuff in
the mid-1990s (Brandenburger and Nalebuff, 1996). Authors use game theory to
propose a first model of coopetition centered on the “value network”.
Coopetition appears when two rival actors decide to cooperate together to create
value for customers. Coopetition is a reconciliation of interests between
“complementors” who cooperate while remaining competitors. In this way of
thinking, coopetition is “a dyadic and paradoxical relationship that emerges
when two companies cooperate in some activities, and at the same time compete
with each other in other activities” (Bengtsson and Kock, 1999).
The idea of cooperating while remaining competitive is a break from the
dominant conception (Yami et al., 2010; Czakon et al., 2014; Fernandez et al.,
2014). In this dominant conception, competition and cooperation are seen as
opposites, implying that as competition increases, cooperation decreases, and
vice versa. The concept of coopetition introduces a cognitive revolution in which
cooperation and competition can occur simultaneously between actors who become partner-adversaries, in other words coopetitors. The simultaneity of competition and cooperation is thus the foundation of the concept of coopetition
(Czakon et al., 2014; Fernandez et al., 2014).
This new conception and its implications have been initially developed by
Lado et al. (1997), although, paradoxically, these authors did not use the term of
coopetition. Lado et al. (1997) observe that more and more companies are combining aggressive and cooperative strategies. They rely on game theory, the Resource Based View and social network theory to show that, although competition
and cooperation have been previously regarded as opposite ends of a continuum,
they must now been understood as two independent dimensions. So firms could
choice their cooperative orientation, high or low, independently from their com-
66
petitive orientation, also high or low. In the same way, for Luo (2007), firms could
choice independently to maintain or not a strong competition with rivals and to
maintain or not a strong cooperation with these rivals.
The recognition of this independence between competition and cooperation
is fundamental because it leads to the idea that a company can have four types of
strategies (Table 1). It could decide to be aggressive toward its competitors
while limiting cooperation with them. This strategy is called “competitive rent
seeking behavior” by Lado et al. (1997) and “contending situation” by Luo
(2007). We called here this strategy “aggressive strategy”. Conversely, the company could decide to be less aggressive as possible with its competitors while
cooperating strongly with them. This strategy is called “collaborative rent seeking behavior” by Lado et al. (1997) and “partnering situation” by Luo (2007).
We call this strategy “cooperative strategy”. The company may also decide to be
as less aggressive and as less cooperative as possible with its competitors. This
strategy is called “monopolistic rent seeking behavior” by Lado et al. (1997) and
“isolating situation” by Luo (2007). We call this strategy “coexistence strategy”.
Finally, the company may choose to be very aggressive toward its competitors
while cooperating also strongly with them. This strategy is called “syncretic rent
seeking behavior” by Lado et al. (1997) and “adapting situation” by Luo (2007).
We call this strategy “coopetitive strategy”.
Table 1. Strategies vis-à-vis competitors
Propensity to
aggressiveness
low
high
high
cooperative
strategy
coopetitive
strategy
low
coexistence
strategy
aggressive
strategy
Propensity to
cooperation
Sources: Adapted from Lado et al. (1997) and Luo (2007).
The concept of propensity to aggressiveness has its roots in competitive dynamic researches (Smith et al., 1992; Young et al., 1996; Ferrier et al., 1999;
Ferrier, 2001; Ferrier et al., 2002; Offstein and Gnyawali, 2005). This school of
thought considers competition from a behavioral point of view. Strategy is defined as a set of competitive actions and reactions. The competitive aggressiveness of a company is a multidimensional concept that is defined as the propensity of the company to proactively and intensively initiate competitive actions and
67
respond quickly to competitive actions of its rivals (Ferrier et al., 2002). According to competitive dynamic researches, a company will be considered as having
a high propensity to aggressiveness if it initiates many competitive actions, more
varied and faster than competitors, and respond rapidly to competitive actions
initiated by competitors (Ferrier, 2001).
The concept of propensity to cooperation has its roots in networks researches (Granovetter, 1985; Burt, 1992; Miles and Snow, 1992; Nohria, 1992; Baum
and Dutton, 1996; Gulati et al., 2000). According to Granovetter (1985), Thorelli
(1986) or even Jarillo (1988), firms are considered as part of a network of relationships that influence their behavior. The network itself refers to two or more
organizations involved in cooperative relationships (Thorelli, 1986). This network provides a number of resources and allows the company to develop its own
stock of resources and skills. The position in the network is considered as the
key element that determines the resources and expertise that the company can
control. This position is expressed in the form of centrality within the network.
According to Faust (1997), centrality is defined as the ability to be active in a network, or “degree centrality”, the capacity to intermediate the flow of resources
between actors, or “betweenness centrality”, or the ability to be in relationships
with other actors who are themselves central, or “eigenvector centrality”. According to network researches, a company will be considered as having a high
propensity to cooperation if occupy a central position in the network by multiplying formal and informal exchanges with competitors.
Once defined propensity of aggressiveness and propensity to cooperation it’s
possible to define aggressive, cooperative, coexistence and coopetitive strategies.
1. The aggressive strategy consists in having a high propensity to aggressiveness and a low propensity to cooperation. In this strategy firms 1) initiate
many competitive actions, more varied and faster than competitors, and respond rapidly to competitive actions initiated by competitors while 2) not occupying a central position in the network and minimizing formal and informal exchanges.
2. The cooperative strategy consists in having a low propensity to aggressiveness and a high propensity to cooperation. In this strategy 1) firms initiate
competitive actions on fewer occasions, with less variety and less rapidly
than competitors while 2) occupying a central position in the network by
multiplying formal and informal exchanges with competitors.
3. The coexistence strategy consists in having a low propensity to aggressiveness and a low propensity to cooperation. In this strategy 1) firms initiate
68
competitive actions on fewer occasions, with less variety and less rapidly
than competitors while 2) not occupying a central position in the network and
minimizing formal and informal exchanges.
4. The coopetitive strategy consists in having a high propensity to aggressiveness and a high propensity to cooperation. In this strategy firms 1) initiate
many competitive actions, more varied and faster than competitors, and respond rapidly to competitive actions initiated by competitors 2) while occupying a central position in the network and increasing formal and informal
exchanges with competitors.
In our framework, aggressive strategies, cooperative strategies, coexistence
strategies and coopetitive strategies are conceptually distinct. However, there
was no empirical study that determines if these strategies are significantly different in an industry. In line with theories of coopetition, we assume that the
coopetitive strategy is a specific strategy, distinct from aggressive, cooperative
and coexistence strategies. We formulate, thus, the following hypothesis:
Hypothesis 1: Coopetitive strategy is a strategy significantly distinct from
aggressive, cooperative and coexistence strategies.
2. Coopetitive strategy and performance
The pioneering researches on coopetition consider that this strategy should
become an alternative to strategies based on pure cooperation and strategies based
on pure competition. Brandenburger and Nalebuff (1996), Lado et al. (1997) and
Bengtsson and Kock (1999, 2000) agree that coopetition is a strategy that holds
the greatest potential for firms’ performance or, at least, has the greatest impact
on variables clearly identified as likely to make them more efficient. Cost savings, sharing of resources and stimulation that promote innovation are among the
potential gains from this strategy.
A company that follows a coopetitive strategy is in a position where it can
benefit from the advantages of both competition and cooperation. Competition
pushes firms to introduce new product combinations, to innovate, to improve
products-services, and so on. It is therefore a progressive factor for companies.
In addition, it enables companies to improve their market position and their performance at the expense of rivals (Lado et al., 1997). Cooperation, in turn, allows the company to have access to almost-free resources, skills and knowledge
that are necessary or indispensable (Lado et al., 1997).
69
Coopetition is therefore intended, in its foundations, as a normative theory
which promises superior performance to the companies that adopt it as a strategy. This fundamental assertion of the theory of coopetition has resulted in some
empirical verification. Several studies attempt to determine the impact of strategies of alliances between competitors on economic and financial performance
(Luo et al., 2007; Ritala et al., 2008; Oum et al., 2004; Kim and Parkhe, 2009).
Other studies try to determine the impact of cooperation between competitors on
innovation performance (Belderbos et al., 2004, Neyens et al., 2010; Nieto and
Santamaria, 2007; Peng et al., 2011; Le Roy et al., forthcoming). Last past researches directly use the concept of coopetition to attempt to establish its impact
on economic performance (Marques et al., 2009; Morris et al., 2007), on innovation (Quintana-Carcias and Benavieds-Velasco, 2004) and on market performance (Ritala, 2012).
Some studies establish a negative relationship between coopetition and performance (Nieto and Santamaria, 2007; Ritala et al., 2008; Kim and Parkhe,
2009). Nieto and Santamaria (2007) show that cooperation with competitors has
a negative impact on the newness of innovation. Ritala and colleagues (2008)
show that a relatively high number of alliances within a group of competing
firms contributes negatively to performance. Kim and Parkhe (2009) show that
competing similarity between alliance partners is negatively related to alliance outcomes. Another previous study shows first a negative, then a positive link between
cooperation with competitors and innovation performance (Luo et al., 2007). Luo
and colleagues, (2007) show that the impact of company alliances with a company’s
competitors on performance is curvilinear. Oum et al. (2004) show that horizontal
alliances have a positive impact on productivity but not on profitability.
Some studies find a negative or positive impact depending contingency variables. Ritala (2012) shows that the relationship between coopetition strategy
and market performance is moderated by market uncertainty, network externalities and competitive intensity. Le Roy et al. (forthcoming) found that, for French
firms, coopetition strategy has a deep impact on innovation when coopetitors are
located in other countries in Europe or in USA, and no impact when coopetitors
are located in France.
Other researches show a positive relationship between cooperation with
competitors and performance (Belderbos et al., 2004; Quintana Carcias and
Benavieds-Velasco, 2004; Marques et al., 2009; Morris et al., 2007; Neyens et
al., 2010; Peng et al., 2011). Quintana Carcias and Benavieds-Velasco (2004)
show that coopetition strategies increase technological diversity and the devel-
70
opment of new products. Morris et al. (2007) show that there is a strong and
positive relationship between coopetition strategies of SMEs and their performance. Belderbos et al. (2004) show a positive impact of coopetition on labour
productivity and the number of sales per employee. Marques and colleagues
(2009) show that coopetition between french football clubs does not improve
their athletic performance, but does improve their economic performance.
Neyens and colleagues (2010) show that there is a positive impact of “continuous strategic alliances” with competitors on the performance of radical innovation. Peng et al. (2011) show that cooperation with competitor leads to better
performance.
If investigations on the link between coopetition and performances lead to
conflicting results, they share two limitations. On the one hand, they do not distinguish the impact of coopetition strategies on performance from the impact of
other strategies toward competitors. The assumed superiority of coopetition
strategies over pure cooperation and pure competition has never been tested
empirically. On the other hand, these studies, at the exception of Ritala (2012),
didn’t include market performance. However, in the initial definition of
Brandenburger and Nalebuff (1996), coopetition is supposed to allow rivals to
increase the overall value they create for customers, which should allow both
partners to increase their sales. It is therefore necessary to try to establish empirically the impact of coopetitive strategies on market performance compared with
the impact of aggressive, cooperative and coexistence strategies.
3. Coopetitive strategy and market performance
One of the key issues raised by research on competitive aggressiveness is
that of its impact on performance (Ferrier et al., 1999; Ferrier, 2000). It is argued
that a company needs to be more aggressive than its competitors. In this purely
competitive vision, only the most aggressive companies can hope to achieve
market leadership and maintain their position. The most successful companies
are those that have the greatest number of competitive actions; that respond
more quickly to competitive actions of their rivals and are more unpredictable in
their behavior. Conversely, the least successful are those that introduce the fewest competitive actions, which take more time to respond to competitive actions
of their rivals and initiate predictable competitive actions and reactions (Mac
Crimmon, 1993; Miller and Chen, 1996; Ferrier et al., 1999; Ferrier, 2001).
71
In contrast, researchers on cooperative strategies agree that membership in
a network has a significant impact on performance, mainly because the networks
create asymmetrical access to resources (Granovetter, 1985; Nohria, 1992; Baum
and Dutton, 1996; Gnyawali and Madhavan, 2001). Research suggests that the
links between companies help to develop and “absorb” technology (Ahuja,
2000), to withstand environmental and technological shocks (Powell, 1990) and,
most importantly, to increase performance (Hagedoorn and Schakenraad, 1994;
Singh and Mitchell, 1996; Zaheer and Zaheer, 1997; Baum et al., 2000). For a company, making many alliances in the sector is a source of competitive advantage
(Eisenhardt and Schoonhover, 1996; Galaskiewicz and Zaheer, 1999). The company will be more successful because it is involved in cooperative relationships
and is located in the heart of cooperative relations that take place in its industry.
By being central in the network, that is to say, at the centre of cooperative actions taking place in the industry, the company benefits from more resources
than companies that are not central, and consequently they should perform better
(Ibarra and Andrews, 1993).
Research on aggressive strategies considers that these strategies have a positive impact on performance (Young et al., 1996; Ferrier et al., 1999, Ferrier,
2001; Ferrier et al., 2002; Offstein and Gnyawali, 2005). However, these strategies benefit the company only through the advantages of aggressiveness. Research on cooperative strategies also considers that cooperative strategies have a
positive impact on performance (Granovetter, 1985; Nohria, 1992; Baum and
Dutton, 1996; Gnyawali and Madhavan, 2001). However, these strategies benefit
to the firm only through the advantages of cooperation. Coopetitive strategies
combine aggressive and cooperative strategies. A priori, they should therefore
allow the company to benefit simultaneously from the advantages of these two
strategies. Overall, companies that follow coopetitive strategies should, thus,
have better performance than companies that only follow aggressive strategies or
cooperative strategies. So we formulate the following hypotheses:
Hypothesis 2: Firms that follow coopetitive strategies have better market
performance than firms that follow aggressive strategies
Hypothesis 3: Firms that follow coopetitive strategies have better market
performance than firms that follow cooperative strategies
72
4. Method
The mobile operators
A mobile operator is a company that provides communications services remotely. It is a company that sells services using telecommunication infrastructures. It can be an independent company or a subsidiary of a constructor of
a public company. There are several types of mobile operators. A simple classification contrasts traditional operators, who have telecommunication networks
with virtual operators, who use the networks of traditional operators. It is not
easy to identify the activity of virtual operators. The study therefore focuses only
on traditional operators.
Mobile telephony is a rapidly evolving industry, experiencing a spectacular
development. The mobile operators industry is also a multi-market industry.
Competition in the industry of mobile operators is both localized and globalized.
Before 2000, national telecommunications markets were closed to competition
and national operators were directly controlled by the state. However, the deregulation of the early 2000s resulted in the entry of new operators into domestic
markets. These new operators are either creations ex nihilo or foreign competitors wishing to expand outside their domestic markets. These foreign competitors usually enter national markets by forming alliances with national operators.
This situation makes it difficult to understand the relationship of competition in the industry. A narrow vision suggests that all the operators present in
a single domestic market should be thought of as competitors. However, this
vision does not take into account existing agreements between competitors in the
national market, and ignores competitors that are not present on the market. Cooperation provides technology and product innovation for the competitors present in the domestic market. So there is indirect competition through cooperation
agreements between operators who are not present on the same national markets.
To take into account this specific characteristic of the sector, we have adopted
a broad view of competition, considering that all the mobile operators are in
situation of potential and indirect competition.
Data collection
Secondary data are widely used to observe companies’ competitive actions
and their cooperative relationships. Here, we mainly use secondary data to detect
companies’ strategic actions. As a first step, we conducted four semi-structured
interviews with experts in the telecommunications sector and mobile telephony.
73
We interviewed four IDATE (Institute of Audio Visual and Telecommunications
in Europe) consultants, three consultants who had a thorough knowledge of mobile telephony and telecommunications and the head of studies on telecommunications. This enabled us to establish a list of periodicals that identify all the relevant strategic moves in the sector.
Data on strategic actions were obtained from issues of Global Mobile and
3G Mobile or 3GWireless. All the strategic movements which took place in the
sector have been identified. About 6,300 pages of documentation were analyzed.
Mobile operators considered in the analysis are traditional operators who initiated at least one cooperative and/or competitive action between 2000 and 2005.
During this period, the mobile industry experienced major changes with digitization and liberalization in the telecommunications sector.
Around the period 2000 to 2005, a series of mergers and acquisitions had
a particular impact on the industry. These changes led us to study this industry
during this period. We considered the strategic actions of mobile operators,
whatever their original focal/domestic market place (Europe, Asia / Pacific, Africa / Middle East, etc.). Indicators on the country and on the measures of performance were obtained from World Telecommunications International Data.
Identification of strategic actions
A competitive action is a direct external movement, specific and observable,
initiated by a firm to enhance its competitive position or defend it (Smith et al.,
1991; Smith et al., 1992; Miller and Chen, 1996; Grimm and Smith, 1997). Cooperative action is defined as any type of action that establishes a link between at
least two firms and involves exchange, sharing, co-development, and so on
(Gulati, 1995). It includes strategic alliances, joint ventures, research and development, national and international roaming agreements, participating in trade
associations and technological consortia, and the like.
To detect strategic movements, we proceeded by structured and detailed
content analysis (Jaugh et al., 1980; Ferrier and Lyon, 2004) of each issue of
Global Mobile and 3G Wireless. This method is effective and recommended for
exploring the strategic processes of a large multivariate sample (Ginsberg, 1988). In
a first step, we developed an annual directory of traditional operators in each country. We then distinguished between the strategic actions of mobile operators and
those of their controlling telecom operators. For example, we recorded the competitive actions of Telefonica de Espana and not those of Telefonica, which is its
74
controlling telecom operator, who also has a fixed line network, and provides
other services.
Then we made the distinction between cooperative and competitive actions
by searching for keywords in articles (Grimm and Smith, 1997). For instance,
“price cut”, the “launch of new service or new product”, have been associated to
competitive actions while “roamings’deals”, “joint venture”, “alliances” have
been associated to cooperative actions (see appendix for more details). Then,
706 cooperative actions and 2.595 competitive actions, divided between 190
mobile operators, were detected. Competitive actions were classified into six
categories of competitive actions in accordance with the classification existing in
previous research (Ferrier and Lee, 2002).
Regarding cooperative actions, we considered only cooperative actions between two or more mobile operators. Those between a mobile operator and its controlling telecom operator, or with another telecom operator, were ignored. A cooperative action including several operators was recorded as a cooperative action of
each of the operators involved (Fjeldstat et al., 2004). For operators who
changed names during the period of study, we used the new name of the operator, while recognizing the competitive and cooperative actions that were carried
out under the former name.
Measurement of variables
Aggressive Propensity
The measure of the aggressive propensity of the operator includes the three
main measures of competitive aggressiveness, namely the volume of competitive
actions and reactions, the time it takes between each consecutive competitive
action and reaction and the complexity of the competitive actions and reactions.
The volume of competitive actions (CONC) of the operator is measured by
the number of competitive actions initiated by it during the period of study (Ferrier, Smith and Grimm, 1999) and the number of responses to competitive actions of other operators.
Competitive activity of the firm = Σ NT*
The time of the competitive actions (TIME) is the average time it takes the
firm between two consecutive competitive actions and/or reactions. We calculated it for a given operator by the annual average number of days separating two
*
Where NTL = number of competitive actions of the firm.
75
actions and/or reactions. Actions and reactions were treated as equivalent
(Young, Smith and Grimm, 1996). The dates of competitive actions selected are
those that were explicitly given in the articles. Where this was not available, we
used the date of publication of the journal. When there were two dates for the
same competitive action, we selected the earlier.
Average time = ∑ (t – t’) / NTL*
The complexity of competitive actions (COMPLEX) of the firm is evaluated using the method used by Ferrier and Lee (2002), Ferrier et al. (1999) and
Nayyar and Bantel (1994), by a Herfindahl-type index.
Complexity
= 1 − ∑ ⎛⎜⎜ N a / NT ⎞⎟⎟ 2
L⎠
⎝
**
A high score indicates that the operator initiates complex sequences of
competitive actions while a low score indicates that the competitive actions of
the firm varied very little.
Cooperative Propensity
The cooperative propensity of the firm was calculated by measuring the
centrality of each mobile operator in the network of cooperative actions that
occurred in the sector. The concept of centrality has several meanings in network
analysis. Our concept of centrality is adopted from Faust (1997), which is the
most complete. Faust (1997) defines the centrality of an actor as its ability to be
active in the network, or “degree centrality”, its ability to intermediate the flow
of resources between actors, and its capacity to be in relationship with other
actors that are themselves central, or “eigenvector centrality”.
Three measures of centrality were identified: “degree centrality” (DC),
“betweenness centrality” (BC) and “eigenvector centrality” (EC). The measurements were obtained from 6,178 observations and Ucinet Netdraw 2.069.
Degree centrality reflects the direct relational activity of the firm with other
members of the network. In this study, it is measured by all the direct links
forged by a mobile operator with other mobile operators during the study period.
*
**
Where t and t’ are the dates of two consecutive competitive actions of the firm and NTL is the
total number of competitive actions/reactions of the operator during the year.
We first calculated the ratio that represents each type of competitive action as a proportion of all the
competitive actions of the firm. Then, to take into account the weight of the distribution of each type
of actions initiated (Na), these ratios were squared. Finally, we calculated the sum of the mean
squares obtained, which gives us the complexity of the competitive actions of the firm.
76
We considered one of the most widely used measure of centrality (that of Freeman, 1979) to capture it.
C D = d ( ni ) = X i + = ∑ X ij .
j
Betweenness centrality reflects the intermediate position occupied by a mobile
operator in relationships between several other mobile operators; the ability of the
operator acting to facilitate interactions between other operators. The Betweenness
centrality of firm (ni) is obtained following Faust (1997) by the equation:
C B ( ni ) =
∑
j<k
*
g jk ( n i ) / g
jk
.
The eigenvector, seeks to highlight the number of direct connections of an
operator, as well as the centrality of the operators with whom it has links
(Bonacich et al., 2004). Denoting the eigenvector centrality of node ni in a onemode network by CE(ni), eigenvector centrality is expressed as:
CE(ni) = CE (nj).xij**.
The measurements were obtained from 6,178 observations and Ucinet
Netdraw 2.069.
After the identification of cooperative actions, we proceeded first to the
codification of all operators who participated in at least one cooperative action
with another mobile operator during every year from 2000 to 2005. Each mobile
*
**
With gjk the number of geodesics between nj and nk; gjk (ni) the number of geodesics between nj
and nk that contain ni and CB(ni) = the betweenness centrality of firm ni. According to Faust
(1997), an intermediate step in calculating betweenness centrality is to find the 'partial
betweenness' of nodes in the network (Freeman, 1979). Node ni 's partial betweenness counts
the number of pairs of other nodes whose geodesic(s) contain node ni. If there is more than one
geodesic between a given pair of nodes, then ni receives fractional credit, where the fraction is
reciprocal of the number of geodesics between the pair. Let gjk be the number of geodesics between nj and nk, and let gjk(ni) be the number of geodesics between nj and nk that contain ni. If
all geodesics are equally likely, then the probability that a geodesic between nj and nk contains
node ni is equal to gjk(ni) / gik.. The betweenness centrality of node ni, denoted by CB(ni), is defined as the sum of these quantities across all pairs of nodes. For a graph with g nodes, CB(ni)
reaches its maximum value of (g – 1)(g – 2)/2. when node ni is on geodesics between all other
pairs of nodes.
According to Faust (1997), the centrality of a node is proportional to the centrality of the nodes
to which it is adjacent, weighted by the value of the tie between the nodes. Finding centrality
values, CE(ni) that satisfy this equation for all nodes in a graph involves solving a system of
simultaneous linear equations. This standard eigenvectoreigenvalue problem is expressed by
the equation Xc = λc where X is a g × g sociomatrix, λ is its largest eigenvalue, and c is a vector
of centrality scores (the eigenvector corresponding to the largest eigenvalue).
77
operator has been assigned a code. We introduced these codes into Ucinet who
performed the calculation of the centrality for each operator from the code that
had been assigned.
Market performance
In general, in the mobile industry, data on financial performance are few or
not available. In addition, because they derive from very different accounting
systems, comparing operators’ financial performances would not make sense.
Similarly, measures of market share are not available for all operators. The most
common and available measures of market performance are: 1) the number of subscribers of the operator, in thousands or millions of subscribers, and 2) the annual
increase in the number of subscribers of the operator, which is obtained by averaging the annual differences in the number of operators during the period of study.
Average annual variation in the number of subscribers
Number of subscribers
Data treatment
To define groups of strategically similar operators and formalize a typology
of strategies in the sector, we conducted a principal components analysis and
a K-Means clustering. A nonparametric test of comparison, the Kruskall Wallis’s test, was used to highlight the differences that exist between the performances of operators according to their strategy.
The classification into groups of mobile operators according to their strategy
was obtained experimentally by two methods conducted concomitantly: a principal
component analysis (PCA) and a K-Means clustering. We proceeded by back
and forth between the two methods (PCA and K-Means), in order to identify the
smallest number of groups of operators constituted in terms of possible elements
that might explain the greatest proportion of total variance, while at the same
time comprising, for each group, at least 10% of the observations.
Because the number of variables varied between two and six, we carried out
PCA with two to six components, as well as K-Means Clustering using the same
components, focusing on the total variance explained in PCA, and on the number of observations in each class for K-Means clustering.
To compare the groups obtained, we opted for a nonparametric comparison test,
which does not assume specific probability distribution of the variables. A test of
78
normality of distribution (Kolmogorov–Smirnov’s test), indicated that the performance data were not normally distributed and influenced our choice of test.
We adopted a Kruskal Wallis’s test, which is a comparison of medians, and is an
alternative non-parametric test for analysis of variance (ANOVA), and in particular makes it possible to compare more than two groups simultaneously. We
tested the null hypothesis that there were no differences at the level of performance of operators according to the strategy adopted, the alternative hypotheses
being the research hypotheses. The results of the factor analysis, K-Means Clustering and the comparison tests are presented and interpreted in the next section.
5. Results
Categorization of operators
Table 2 presents the descriptive statistics of the variables used and Table 3
presents the correlation matrix of the variables used. The results in Table 4, with
a Kaiser–Meyer–Olkin (KMO) index equal to 0.774 (> 0.6) and a significant
Barlett’s test of sphericity at 5% (with a value of 0.000), make the method of
factor analysis appropriate for treatment of the data.
Table 2. Descriptive Statistics
Mean
1.64
278.06521627
.8903114200
1.12
33.48696
2.77606
8579383.76
.71
CONC
TIME
COMPLEX
DC
BC
EC
SUBS
SUBSINC
Standard Error
2.968
131.444623094
.22057832564
2.281
151.622667
9.268697
2.019E7
6.356
N
1140
1125
1107
1138
1140
1139
925
300
Table 3. Correlation matrix
3
4
COMPLEX
5
1
–,735**
,629*
,03
,040
,000
,000
1125
1107
1138
1140
CONC TIME
1
CONC
TIME
2
Pearson
Correlation
Sig.
(two–way)
N
Pearson
Correlation
1140
**
–,735
1
–,745
DC
BC
6
7
,513*** ,481**
**
–,389
8
9
SUBSIN
C
10
,304*
,331***
,032**
,000
,000
,021
1139
925
300
EC
*
–,297
SUBS
***
–,233 –,290
–,033**
79
table 3 cont.
1
2
Sig.
(two-way)
N
COMPLEX Pearson
Correlation
Sig.
(two-way)
N
DC
Pearson
Correlation
Sig.
(two-way)
N
BC
Pearson
Correlation
Sig.
(two-way)
N
EC
Pearson
Correlation
Sig.
(two-way)
N
SUBS
Pearson
Correlation
Sig.
(two-way)
N
SUBSINC
Pearson
Correlation
Sig.
(two-way)
N
3
4
5
,700
,042
1125
1104
1123
**
**
,03
1125
*
,629
-,745
,040
,700
1107
1104
***
**
6
1
1107
**
-,389
,299
,000
,042
,031
1138
1123
1105
**
*
**
,513
,481
-,297
,209
,000
,076
,047
7
8
9
10
,076
,340
,000
,042
1125
1124
910
295
**
,299
,209
,242
,262
,013
,031
,047
,530
,048
,519
1105
1107
1106
901
293
***
1
,721
1138
***
,721
**
,733
***
,347
,051***
,000
,026
,000
,001
1138
1137
923
300
*
1
,481
,000
***
,105
,080
,000
,025**
,032
1140
1125
1107
1138
1140
1139
925
300
,304*
-,233
,242
,733**
,481*
1
,099*
,192
,000
,340
,53
,026
,080
,092
,592
1139
1124
1106
1137
1139
924
299
1
,029**
***
***
**
***
*
,,262
,000
,000
,048
,000
,000
,092
925
910
901
923
925
924
**
**
,032
-,033
,013
,021
,042
,519
300
***
***
,051
001
295
293
300
. P < 0.01, ** p < 0.05, *p < 0.1
,105
1139
-,290
,331
,347
***
**
,099
,612
925
**
,025
,192
,029
,032
,592
,612
300
299
300
300
1
300
Table 4. Index KMO and Bartlett's test
KMO and Bartlett's Test
Kaiser–Meyer–Olkin Measure of Sampling Adequacy
Bartlett's Test of Sphericity
Approx. Chi-Square
df
Sig.
.774
732.360
15
.000
The PCA presented in Table 5 identifies three groups of mobile operators
with at least 10% of the total number of operators in each group and explaining
the greatest proportion of variance (88%). This first result allows us to identify
three types of strategy toward competitors among companies in the mobile telephony industry.
80
Table 5. Total variance explained
Initial eigenvalues
Component
1
2
3
4
5
6
total
3.507
1.016
.800
.379
.222
7.653E
-02
% of
variance
58.446
16.929
13.333
6.317
3.699
1.276
cumulative
%
58.446
75.375
88.708
95.025
98.724
100.00
0
Extraction sums of squared
loadings
% of
cumulatotal
varitive%
ance
3.507
58.446
58.446
1.016
16.929
75.375
.800
13.333
88.708
Rotation sums of
squared loadings
% of
cumutotal
varilative%
ance
3.190 53.163
53.163
1.110 18.499
71.662
1.023 17.046
88.708
Tables 5 and 6 present the results of the K-Means Clustering. Table 6
shows that each group of operators consists of at least 10% of the total number
of operators. Table 6 shows the final cluster centres and gives the “profiles” of
the three groups of operators. It shows an allocation of operators according to
their propensity for cooperation and/or aggressiveness.
Table 6. Number of observations in each group
1
2
3
Group
49.000
18.000
120.000
187.000
3.000
Valid
Missing
Table 7. Final cluster centres
Group
CONC
DC
BC
EC
TIME
COMPLEX
1
1
1.566
1
3.8
2160
1
2
3
.875
0
3.2
865
2
3
16
1.975
2
7.8
197
3
The first group is composed of 49 operators (Group 1). These operators are
not very aggressive. They have the lowest number of competitive actions and
reactions of the three groups (CONC = 1). These are also the operators who take
most time to respond to competitive actions of their rivals (TIME = 2160) and initiate the simplest competitive actions and responses (COMPLEX = 1). On the other
hand, they obtain relatively high centrality scores (DC = 1566, BC = 1, EC = 3.8)
81
compared to the second group of operators (DC = 875, BC = 0, EC = 3.2). The
operators of the first group are therefore considered to be operators that follow
a cooperative strategy.
The second group is composed of 18 operators (Group 2). These operators
are more aggressive than the first group. They have a greater number of competitive actions and reactions than those of the first group (CONC = 3). They
also initiate more frequently, and have more complex competitive actions and
reactions (TIME = 865, COMPLEX = 2) than the first group. Conversely, the operators in the second group are less cooperative than those of the first group. They have
measures of centrality (DC = 875, BC = 0, EC = 3.2) that are all lower than those of
the first group (DC = 1566, BC = 1, EC = 3.8). Operators in the second group are
considered to be the operators that follow an aggressive strategy.
The third group consists of 120 operators (Group 3). These operators are
more aggressive than those of the first or second groups. They have a greater
number of competitive actions and reactions (CONC = 3) than those of the first
or second group. They also initiate more frequent (TIME = 197) and more complex (COMPLEX = 3) competitive actions and reactions than the operators in
the first and second groups. The operators in the third group are also more cooperative than those in the first and second groups. They have higher centrality scores
than the operators in the other two groups (DC = 1.975, BC = 2, EC = 7.8). These
operators are both very aggressive and very cooperative. We therefore consider
them to be operators adopting a coopetitive strategy operators, according to the
definition of coopetition previously adopted.
In summary, three strategies have been identified: cooperative strategy which
corresponds to Group 1, aggressive strategy corresponding to Group 2 and coopetitive
strategy which corresponds to Group 3. Hypothesis 1 can be considered as partially
validated. A strategy of coopetition is significantly different from aggressive and
cooperative strategies, which are themselves significantly distinct from each another.
However, the coexistence strategy has not been identified.
Comparison of market performance of the three groups
Tables 8 and 9 show that in terms of the number of subscribers and variation of
the number of subscribers, the performance of the groups of mobile operators are
significantly related to the strategy adopted. In accordance with Hypotheses 2 and 3,
the coopetitive operators (i.e. those that are both very aggressive and very cooperative) perform better than simply aggressive operators or simply cooperative operators. These two tables also show the superiority in terms of market performance of
aggressive operators compared with cooperative operators.
82
Table 8. Strategy and number of subscribers
Ranks
N
36
14
112
162
Group
Cooperative Operators
Aggressive Operators
Coopetive Operators
Total
Mean Rank
45.81
51.75
96.69
Test Statistics*
38.222
2
0.000
Chi-squared
df
Asymp. Sig.
*
Kruskal Wallis Test; Grouping Variables: Classification (K Means).
Table 9. Strategy and number of subscribers
Ranks
N
35
14
110
159
Group
Cooperative Operators
Aggressive Operators
Coopetive Operators
Total
Mean Rank
59.89
66.64
88.10
Test Statistics*
Chi-squared
df
Asymp. Sig.
*
11.262
2
0.004
Kruskal Wallis Test; Grouping Variables: Classification (K Means).
6. Discussion
The aim of this research is to establish a link between coopetition strategies
and market performance. In coopetition theory, strategies that consist of simultaneously combining aggressiveness and cooperation are inherently considered
superior to purely cooperative or purely aggressive strategies (Bengtsson and
Kock, 1999; Brandenburger and Nalebuff, 1996; Lado et al., 1997). However,
there is little empirical evidence for this assertion. This research thus evaluates
the impact of coopetitive strategies on market performance compared with the
impact of aggressive, cooperative and coexistence strategies.
The results obtained in this research show, first, that aggressive, cooperative
and coopetitive strategies are statistically distinct and correspond to different
choices for firms in the sector of mobile telephony. As postulated by coopetition
theory, a range of strategic stances toward competitors can be adopted (Lado et al.,
83
1997; Luo, 2007). The aggressive and cooperative propensity may well be regarded as independent, which means that firms have to make choices.
When choosing the best strategy toward competitors, three main theories
disagree. In competitive dynamic theory, firms are considered to perform better
if they are aggressive (Ferrier, 2001). In network theory, firms have an interest
in searching for a relational advantage (Contractor and Lorange, 1988; Dyer,
1997). In coopetition theory, the most successful firms are those that benefit
from both aggressiveness and from cooperation (Brandenburger and Nalebuff,
1996; Lado et al., 1997; Bengtsson and Kock, 1999, 2000).
The results of the present study make clear, first, that coopetition strategies
are statistically significantly distinct from aggressive and cooperative strategies.
The basic postulate of coopetition theory, that a firm can choose whether to be
highly aggressive and lowly cooperative, or to be lowly aggressive and highly
cooperative, or to be both highly aggressive and cooperative, is validated.
The results obtained then make it possible to decide on the best strategy in
relation to market performance. The results show that, in terms of number of
subscribers and variation in the number of subscribers, the strategy adopted toward competitors has an impact on performance. Coopetition strategies are
shown to be better than aggressive and cooperative strategies. This result is original, as there is no comparable previous research. It is the first time that the supposed superiority of coopetitive strategy over aggressive and cooperative strategies has been established statistically.
These results do not refute the findings of previous research on aggressive
strategies or on cooperative strategies. They simply show that each of these
strategies, conducted in opposition with each other, leads to lower levels of performance than strategies that combine them. In this sense, the results confirm
previous work, indicating that coopetition is a successful strategy (Belderbos et
al., 2004; Quintana Carcias and Benavieds-Velasco, 2004; Marques et al., 2009;
Morris et al., 2007; Neyens et al., 2010; Peng et al., 2011; Ritala, 2012; Le Roy
et al., forthcoming).
The research results should be considered as a confirmation of the validity
of coopetition theory as defined by its founders (Brandenburger and Nalebuff,
1996; Lado et al., 1997; Bengtsson and Kock, 1999, 2000). The strategy of being
both aggressive and cooperative appears as the most frequent adopted and most
profitable for mobile operators. This result is consistent with challenging western ways of thinking, which tend to perceive competitive and cooperative behaviour as two ends of a continuum, rather than as two independent dimensions
84
(Lado et al., 1997; Luo, 2007). The results lead to a new conception of strategies
toward competitors, which cannot be reduced to a simple choice between competition and cooperation, but is conceived as a complex combination of competition and cooperation. In this complex combination, strategies that combine high
levels of aggressiveness and cooperation are the most successful.
Another original result obtained in this research is that an aggressive strategy is better than a cooperative strategy. In previous research, the merits of these
two strategies have been never been directly compared. Past researches focused
on the impact of one or the other, namely on the impact of cooperation or competitive aggressiveness (Gnyawali et al., 2006). We show here that an aggressive
strategy performs better than a cooperative strategy. This result points to the
success of the theory of competitive dynamics, which recommends firms to
adopt an aggressive behavior for better performance (Young et al., 1996; Ferrier
et al., 1999; Ferrier, 2001; Ferrier et al., 2002; Offstein and Gnyawali, 2005).
Conversely, this result challenges studies that affirm that social networks and
embeddedness have a direct and positive impact on performance (Granovetter,
1985; Nohria, 1992; Baum and Dutton, 1996; Gnyawali and Madhavan, 2001).
If cooperative strategies appear to be unavoidable in the industry, this strategy
should be considered a necessary condition for success rather than a discriminating factor that promotes better market performance.
This result can be partly explained by the characteristics of the sector. The
mobile industry requires a high level of compatibility between the services and
products offered by competing operators. This compatibility is both enforced by
legal frameworks and decisive for customers. Competitors must necessarily cooperate with each other to provide this compatibility. Another feature of the
industry is that products are combinations of several basic components. The
different components belong to separate markets but are highly interdependent.
Market players must work together to provide complex products-services to
customers. Cooperation is therefore an almost inevitable strategy in the mobile
telephony industry. In this context, the difference is created not only by the ability to cooperate more than other operators, but also by the ability to develop an
aggressive strategy. An aggressive strategy can be implemented by reducing the
necessary cooperation to a minimum and maximizing aggression. This is achieved
by operators in Group 2. This strategy then performs better than the strategy that
relies only on cooperation in terms of market performance. An aggressive strategy
can also be established, not by reducing the cooperative effort, but by increasing
it. This is the case of the operators in Group 3, which follow a coopetitive strategy. This strategy performs better than the other two strategies.
85
Conclusion
Researches on coopetition are more and more common in the field of strategic management (Yami et al., 2010; Bengtsson and Kock, 2014; Czakon et al.,
2014). These researches are developed even though the normative aspects of
coopetition theory have not been fully confirmed. Past researches highlight the
impact of alliance between competitors on economic and financial performance
(Luo et al., 2007; Ritala et al., 2008; Oum et al., 2004; Kim and Parkhe, 2009),
the impact of cooperation among competitors on innovation (Belderbos et al.,
2004, Neyens et al., 2010; Nieto and Santamaria, 2007; Peng et al., 2011; Le
Roy et al., forthcoming) and the impact of coopetition on economic performance
(Marques et al., 2009; Morris et al., 2007), on innovation (Quintana-Carcias and
Benavieds-Velasco, 2004) and on market performance (Ritala, 2012). Among these
researches, none compares the effects of coopetition strategies on performance with
those of aggressive, cooperative and coexistence strategies. Similarly, none of these
researches, except Ritala (2012), deals with market performance.
To fill this gap, the present research studies the relative effects of relational
strategies toward competitors on market performance. An empirical study is
conducted, using secondary data in the mobile telephony industry. This study
shows that the three strategies of aggressiveness, cooperation and coopetition are
well represented in this industry. No firm was identified that adopted the coexistence strategy. The study also shows that a coopetitive strategy performs better
in increasing market share than the other two detected strategies. This result is
original and has not been shown previously in the literature. Finally, the study
shows that aggressive strategies perform better than cooperative strategies,
which is also an original result.
The managerial implications of this research are important. They lead to
specific recommendations for companies in this industry that aim to increase the
number of their subscribers. Indeed, the results lead us to recommend that companies should adopt a strategy that is simultaneously cooperative and aggressive
rather than a purely aggressive strategy or a purely cooperative strategy. To increase the number of subscribers, being more active both on the aggressive and
on the cooperative front is clearly the best strategy. The second best strategy is
an aggressive strategy. Finally, an essentially cooperative strategy is advisable
for companies who wish to have a large number of subscribers.
These results should be considered in relation to the limitations of the research. One of the major limitations is that the various operators whose perfor-
86
mances we are comparing are of very different sizes, and are operating in different geographical areas and different domestic markets, which means they do not
necessarily deal with identical environmental situations and conditions of competition. It would therefore, in future research, be better to study the impact of
environmental conditions on strategic choices and performance.
A second limitation is that we focused in this study on volume measures of
performance, because they are the measures of performance of reference in the
industry, and the only measures that are completely accessible. This raises the
problem of operators that do not adopt strategies of volume, but rather have niche
strategies with high added value. This is very often the case with virtual operators.
An extension of the research would consist of looking for other measures of performance and consideration of all operators (traditional and virtual).
Another limitation is that we have restricted this study to a single industry.
This choice has certain advantages, but the results obtained certainly depend
partly on the characteristics of the industry. It would therefore be beneficial to undertake similar studies in other industries, to determine if there is invariance in terms
of results or if these results can only be observed in the mobile telephony industry.
A last limitation concerns the use of the method of analysis, namely structured content analysis based on articles published in journals dedicated to the
sector (Smith et al., 1992; Chen et al., 1992; Ferrier, 2001; Gnyawali and
Madhavan, 2001). This is a highly specific method that, although it makes it possible to observe the behaviour of firms, has the weakness that it is difficult to replicate,
making it difficult to generalize the results. Further research could use a different
method of analysis, such as using primary data, to see if similar results can be observed and to establish more precisely the scope of the results of this research.
Generally, the results obtained here are as good as those of many contributions to the literature, which require further confirmation and, therefore, argue
for further research. Comparing the merits of different strategies to adopt toward
competitors is still a relatively unexplored field of research. The relative performance of aggressive, cooperative and coopetitive strategies is not well established. There are probably multiple contingent factors that should be introduced
for a better theoretical explanation. Only further research will support these theoretical developments.
87
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Appendix
Examples of categorization of competitive actions/reactions
Types of actions
1
Competitive Action
Date
2
Example
3
Pricing Action
08/05/02
E-Plus cuts prices to boost i-mode.
German operator E-Plus has slashed the price of its
i-mode handset.
Marketing Action
05/03/02
EuroTel offers free usage:
Czech operator EuroTel is offering customers three
months of free data usage when they sign up for its
GPRS service.
Product Action
31/07/02
Wind launches mobile video.
Italian cellco Wind announced that subscribers can
now watch moving film pictures on their handsets,
with content supplied via the LIBERO mobile portal.
Capacity Action
28/02/01
Telekom Italian Mobile paid Real 1.54 billion for
PCS operating licenses in Sao Paolo and the region of
southern Brazil. The third license, covering the northern
region, was awarded to telemar for US$ 556 million.
Service Action
23/03/05
MobilTel launches EDGE:
Leading Bulgarian operator MobilTel announced last
week that it had launched EDGE services in Sofia and
is working on a nationwide rollout.
Signalling Action
02/02/05
Mobilkom makes 3G push:
Austrian operator Mobilkom says it expects to offer
nationwide 3G coverage by the summer using UMTS
and EDGE
Cingular Wireless
and VoiceStream
05/07/00
Cingular finds Voice.
Cingular Wireless (formerly BellSouth/SBC) and
Voicestream last week exchanged spectrum that will
allow Cingular to gain access to New York City, and
VoiceStream to obtain additional spectrum in Los
Angeles and San Francisco.
Telstra (Australia)
and PCCW (Hong
Kong)
31/01/01
Telefonica Moviles
(Spain)
17/04/02
Cooperative
Actions
Telstra, PCCW launch JVs.
Australian operator Telstra and Hong Kong‘s PCCW
have launched their Asia-Pacific alliance with three
50/50 joint venture companies.
Telefonica signs roaming deals.
Telefonica Moviles has signed a roaming agreement
93
appendix cont.
1
NTT DoCoMo
(Japan)
SK Telecom (South
Korea)
94
2
3
with SK Telecom and NTT DoCoMo, enabling
Telefonica’s subscribers to send and receive voice,
SMS and data services in time for this summer’s
soccer World Cup
Patrycja Klimas University of Economics in Katowice, Poland
MULTIFACETED NATURE
OF COOPETITION INSIDE
AN AVIATION SUPPLY CHAIN
– THE CASE OF THE AVIATION VALLEY
P ATRYCJA K LIMAS
Abstract
In recent years the concept of coopetition becomes more and more popular
in both economy and literature. The growing interest in coopetition strategies,
their characteristics and adaptation stems from the fact that it may be perceived
as a significant factor for leveraging effectiveness and performance of modern
organizations.
Drawn from existing literature this paper attempts to present the character
of coopetition inside the aviation supply chain. By identifying different levels,
scopes and fields of both cooperation and competition four types of coopetition
were identified. In the light of the obtained results particular members of considered supply chain may be characterized by: national coopetition, global
coopetition, hybrid coopetition and multidimensional coopetition. The identified
types of coopetition are varied in terms of: a) the market scope of coopetition,
b) types organizations engaged (i.e. subsidiaries / parent organizations), c) stages
of supply chain maintaining coopetitive relationships, and d) complexity and
directedness of coopetition.
Keywords: coopetition, cooperation, competition, networks, supply chain, aviation, case study.
Introduction
Even though supply chains are not new phenomena, nowadays they are attracting greater and greater attention of researchers from management sciences
(Wilhelm, 2011). The growing interest in supply chains within the management
field may be justified by the fact that today in business practice supply chains as
well as supply chain management are at the heart of successful business strategy
(Houé and Guimaraes, 2013). In a traditional, management-based view supply
chain is understood as a set of cooperating – in deferent aspects of activity –
production, trading and service companies together with their clients among
which the flows of products, information and money are realized (Witkowski,
2010). However, in more recent literature supply chains are perceived as a network of cooperating organizations engaged – through a set of relationships – in
joint processes and activities creating values like products and services provided
to the final clients (Christopher, 1998). Modern supply chains take the form of
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MULTIFACETED NATURE OF COOPETITION…
network of companies, including suppliers and their suppliers, if they exist, and
clients and their clients, if they exist (Lambert, 2008). Moreover, supply chains
are co-created by a network of resources, materials, information and services
(Chen and Paulraj, 2004) provided by all the above-mentioned organizations. To
conclude, the newest view on supply chains assume that it is “a set of three or
more entities directly involved in the upstream and downstream flow of products, services, finances, and information from a source to the customer”
(Mentzner et al., 2001, p. 4) together with the dense network of diversified relationships among them.
The aim of this paper is to present and discuss the results of the research on
coopetition conduced within a supply chain. The coopetition as well as
coopetitive relationships are explored from the perspective of the behaviors and
strategies adopted by particular members of supply chain rooted in a Polish aviation industry. First, this research provides the diagnosis of coopetition features
recognized among particular members of considered supply chain. Second, the
research identifies the types of coopetition existing within the considered network of cooperation. The findings suggest that the particular members of supply
chain are diverse in terms of coopetition strategies adopted.
This paper is divided into four parts. The first section outlines the theoretical underpinnings for the phenomena of coopetition and coopetitive relationships
in supply chains. The second part raises the methodological issues including
research design and research methods. The third section presents the research
results reflecting the coopetition phenomena inside one, purposefully chosen
innovation network i.e. Aviation Valley. The findings are presented with a distinction into national coopetition, global coopetition, hybrid coopetition and
multidimensional coopetition. Finally, the fourth section summarizes the outcomes of the conducted study, outlines the possible directions for further research, and points out the most important limitations of the research.
1. Theoretical background
In business practice, modern supply chains function as multifaceted and
highly complex networks (Houé and Guimaraes, 2013). They consists of multitude of stakeholders (Houé and Guimaraes, 2013) and dense network of complicated, strong, long-term, and interdependent relationships among partners developed and fostered through strategic collaboration (Cheh and Paulraj, 2004). Such
a high level of complexity and diversity of links and organizations involved
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P ATRYCJA K LIMAS
causes that modern supply chain more often can be labelled as a multi-stage,
multi-member, or multi-product (Sepehri and Fayazbakhsh, 2011). From the
strategic management point of view it should be emphasized that supply chains
are formed to achieve a greater level of sustainable competitive advantage for all
parties involved (Cheng et al., 2008; Sepehri and Fayazbakhsh, 2011) what is
attainable by combining individual strengths and unique resources of particular
organizations through collaborative and dense relationships.
In the literature on supply chains and supply chain management, there is
emphasized not only the existence but also the significance of beneficial collaborative relationships (Christopher, 2001; Houé and Guimaraes, 2013) among particular partners. However, besides these purely collaborative links, members of supply
chains are connected by – more or less – competitive relations, or coopetitive ties. In
other words, among supply chain members we are able to identify three different
conditions, namely: cooperation, competition and coopetition. It is worth noting that
while the prior literature expressed rather the first situation only the latest research points out that today “members of a supply chain more often compete
fiercely” (Sepehri and Fayazbakhsh, 2011, p. 61). It shows indirectly, that between partners maintaining collaborative relations more often appear coopetition
and coopetitive relationships (Wilhelm, 2011). Indeed, in supply chain firms
more willingly and more often simultaneously compete and co-operate in order
to maximize their profits (Gurnani et al., 2007) and competitive advantage
reached. Therefore coopetition can be perceived as a one of the distinctive feature of
modern supply chains (Li et al., 2011) while their members are characterized by an
inherent tension of cooperation and competition (Wilhelm, 2011).
Furthermore, from the managerial perspective coopetition can be perceived
as a factor of competitive advantage created by the whole supply chain as well
as by its particular members (Li et al., 2011). This leveraging effect of
coopetition for supply chain performance results from the development of many
different cross-functional aspects of cooperation strengthening the cooperative
intensity and influencing especially customer performance and financial performance (Luo et al., 2006). Moreover, the prior research on supply chains pointed out
that coopetition among supply chain members provides greater results than their
only competitive or collaborative approach (Sepehri and Fayazbakhsh, 2011). In
other words the more coopetitive than only collaborative relationships within the
supply chain the greater value and supply chain performance is reached.
Most of prior research on relationships conducted under the conditions of
supply chains has been focused on collaborative ties and cooperative connections. Unfortunately, only very few studies were conducted on the coexistence of
98
the cooperation and competition relationships between supply chain members
(e.g. Chen, Paulraj, 2004). To the authors’ knowledge the coopetitive relationships perceived in theoretical papers as so significant (Li et al., 2011; Sepehri
and Fayazbakhsh, 2011; Wilhelm, 2011) remain poorly and fragmentally recognized (Cheng et al., 2008) contributing to the fact that our knowledge about
coopetitive relationships seems to be ambiguous, or even a little bit blurry. Firstly, previous research on coopetition among members of supply chains has investigated coopetition at the dyad level (i.e. coopetition has been considered between two organizations only – Gurnani et al., 2005) while the literature stresses
that in case of coopetition inside supply chain we need a much wider, holistic
approach considering coopetitive relationship inside the whole network of ties
and connections among – and not only between – members of supply chain
(Wilhelm, 2011). Secondly, prior research related to the phenomena of
coopetition inside supply chain was devoted to comparisons of the collaborative,
competitive and coopetitive behaviors of the supply chain members (Sepehri and
Fayazbakhsh, 2011). Thirdly, the majority of prior research was explorative in
nature (Gurnani et al., 2005; Lejeune and Yakova, 2005; Wilhelm, 2011; Sepehri
and Fayazbakhsh, 2011) and only a slight research efforts were directed on explanation of the significance of coopetition for knowledge management and knowledgeintensive processes inside supply chains (e.g. Cheng et al., 2008; Li et al., 2011).
To conclude, prior literature does not provide research on the specifics and
nature of the coopetitive relationship in the strict sense, do not provide holistic
perspective undertaken from the whole supply chain point of view. Generally the
coopetition and coopetitive relationships still remain poorly recognized under
the conditions of supply chains (Cheng et al., 2008). Therefore, further and
deeper, theoretical and empirical, exploratory and explanatory research in exploring coopetition and coopetitive relationships is needed (Gurnani et al., 2005;
Cheng et al., 2008; Wilhelm, 2011). Lack of comprehensive and holistic research on
coopetition and coopetitive relationships among members of supply chains as well
as the limitations of prior research outlined above seem to justify the existence of the
research gap. This gap ought to be filled in by exploratory and qualitative research
(Houé and Guimaraes, 2013). Therefore the goal of this paper is to present and discuss the results of the research on coopetition relationships within one, purposefully
chosen supply chain. The results are presented and discussed though investigation of
most important characteristics, main types and different variants of the identified
coopetitive relationships among members of the networked supply chain.
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P ATRYCJA K LIMAS
2. Research design*
The Aviation Valley case aims at identifying the coopetition features and
types related to the particular members of supply chain. Research on coopetition
strategy and coopetitive relationships was grounded in Polish aviation industry.
The choice of this industry is justified by several reasons: highly developed inter-organizational cooperation (caused by strong pressure to constantly be innovative and tremendous level of expenditure on R&D), high level of networking,
above-average level of complexity and modularity of products manufactured by
Polish aviation companies, specificity favouring establishment of supply chains
(aviation industry consists mainly of SME subcontractors and suppliers, there
are only several large, key companies manufacturing final products like aviation
engines). In Poland, there are more than 120 companies working for aviation
industry, employing over 25 thousand employees. Most of Polish aviation companies are members of Aviation Valley (AV) which was chosen for our investigation. The Aviation Valley is a registered association of companies and organizations active in the field of aeronautical manufacturing, research, training or
exploitation. It has been founded very recently, yet has proven since 2003 to be
a very effective horizontally integrated supply chain implementing cutting edge
aviation technologies and providing state of the art aviation products including
aeronautic engines, gliders, light planes, and helicopters. Between its creation
date in 2003, where 17 founding members laid grounds for a formal industry
association and now a sharp increase in membership can be noticed – it is now
topping 95 firms and organizations.
Since 2003, Aviation Valley functions as a cluster (as it is geographically
concentrated), as an association (as it is registered as a NGO), as a supply chain
(as it provide co-created and co-produced aviation engines, planes, gliders and
other aviation products), as a chain in global value and supply chain (as it provides modular components for final producers like Boeing, Airbus, or Embraer).
In 2013 there was more than 90 members, 23 500 employees and turnover exceeded 1 billion € – covering more than 80% of Polish aviation industry in terms
of total employment and turnover. The size and obtained results locate Aviation
*
Research leading to the achievement of these results is conducted under FRIDA project (Fostering Regional Innovation and Development through Anchors and Networks) and has received
support from the 7th European Commission Framework Programme (Socio-Economic and Humanities Sciences, contract number 225546). Furthermore, the preparation of this paper was supported
also by a research grant from the National Science Centre under the project titled: Organisational
Proximity in Innovation Networks (contract number: DEC-2011/03/N/HS4/00372).
100
Valley among the most important European aviation and aerospace clusters
(Niosi and Zhegu, 2005). The Aviation Valley operates as a supply chain – especially – for three global aviation corporations: United Technology Corporation,
Avio Group and Augusta Westland. The Aviation Valley is a cluster with three
major geographical concentration areas: around the city of Rzeszów in southeastern Poland; around the city of Bielsko-Biała in southern Poland; around the
city of Świdnik in eastern part of Poland, the distances between these subclusters exceeding 250 km in each direction. It should be added that every one of
sub-clusters operate around one large company being the subsidiary of abovementioned global corporation. First, WSK Rzeszów owned by United Technology
Corporation is the core for organizations operating around the city of Rzeszów. Second, Avio Polska owned by Avio Group is the core for organizations operating
around the city of Bielsko-Biała. Third, PZL-Świdnik owned by Augusta Westland
is the core for organizations operating around the city of Świdnik.
The investigation of the coopetition and coopetitive relationships was restricted to a strategic alliance organized as a formal association as the literature states
that coopetition can be identified and should be explored inside cooperative and
formal supply chains (Sepehri and Fayazbakhsh, 2011). On the other hand, the restriction of research perspective to one, purposefully chosen supply chain can be
justified by the qualitative, exploratory and explaining nature of the research aims,
as well as by the existence of cognitive gaps and lack of previous research. Therefore, the research adopts a qualitative approach and applies an interpretative case
study method (Stake, 2009) aiming at theory building (Andrade, 2009).
Research design aims at identifying key features of coopetition characteristic for Aviation Valley members. Therefore a three step approach has been
adopted. Firstly, secondary data (including articles, annual industry reports, purchasing information, and websites) was collected in order to briefly describe
cluster relevant members, and identify their business profiles. Secondly, in order
to identify coopetitors the analysis of the area of competition and cooperation
within the supply chain has been conducted. In general, among the supply chain
members 27 coopetitors were identified. Thirdly, primary data was collected to
distinguish the most important features of coopetitive relationships maintained
by identified coopetitors. In that part of the study 22 direct, semi-structured interviews were conducted. The owners, directors, vicepresidents and top managers
played the role of our interlocutors. As an additional source of information the researchers run two observations during annual meetings of Aviation Valley members.
It should be added that both, the primary and secondary data for the purposes of the
research was collected between September 2010 and December 2011.
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P ATRYCJA K LIMAS
3. Coopetition within a supply chain – the case
of Aviation Valley
Aviation Valley is the biggest, the oldest, the most developed formally registered supply chain in Polish aviation industry. Taking the perspective of its
objectives and main characteristics it is an innovation network consisting of
intensive, strong, and close cooperative and coopetitive relationships. Aviation
industry – which creates the context for the activity of Aviation Valley – is
knowledge intensive sector (Niosi and Zhegu, 2005), where innovations provide
competitive advantage, and the processes of knowledge creation, knowledge
acquiring, and knowledge sharing seems to be the overriding objectives (Broekel
and Boschma, 2009; Dos Santos and Neto, 2009). Very often, the processes of
searching new knowledge require external collaboration with customers, suppliers, science representatives and even with competitors (Niosi and Zhegu, 2005).
Therefore the majority of collaborative initiatives, inter-organisational networks
as well as formal and informal supply chains within aviation industry function as
innovation networks inside which competition is accompanied by collaboration.
Cooperation with competitors creates possibilities to improve quality, invent
innovations (Hagberg-Andersson and Tidström, 2010), foster innovation and
knowledge sharing (Osarenkhoe, 2010), and stimulate efficient knowledge management (Dos Santos and Neto, 2009). Furthermore, coopetition provides access
to complementary resources (Luo and Slotegraaf, 2006) and to competitors’
skills and capabilities (Gnyawali and Park, 2009) which would be otherwise
unavailable. All of the above-mentioned benefits of simultaneous cooperation
and competition are important for Aviation Valley and its members what is reflected in its statute indicating one of the basic rule and goal of the association –
“to combine healthy competition with cooperation in particular areas” (AV’s statute). The literature states that the significance of coopetition increases as products
become more complex and as competition becomes global (Gnyawali and Park,
2009), and then it provides higher value in a shorter time than competitive orientation (Dagnino and Padula, 2002). Due to that coopetition seems to play an important
role inside Aviation Valley being supply chain providing extremely complex and
modular final products like aviation engines, planes, gliders and helicopters.
The character of provided products and the existence of common goals of
particular partners do not result in the fact that members are connected only by
cooperative relationships. There are still some fields of activity reserved for
competition, and the relationships between partners are rather coopetitive than
102
cooperative only. It is interesting as the supply chain members pay great attention to the isolation mechanism, and opportunistic behaviors which may appear
during cooperation. That is why they delimit and respect the line between competition and cooperation – “We have non-aggression pact with companies from
the Valley, we are aware in which areas there are opportunities for fruitful cooperation and which fields of our activity should be protected against – even the
closest – our partners” (interviewee M); “We know where and how cooperate,
and where do not incommode or disturb each other” (interviewee R). In the light
of the above Aviation Valley can be described as a network being a dynamic
combination of collaborative and competitive relationships and the loci of
coopetition phenomena (Gnyawali and Park, 2009).
In technology intensive and global competitive markets like aviation and
aerospace adaptation of the coopetition strategy is often only one, possible way
of survival and development. In case of considered supply chain SMEs’ perceive
coopetition as a simple path to the innovativeness, technological improvements
(Gnyawali and Park, 2009) and leveraging capacity – “If we cannot realize the
order placed by foreign customer, we try to recommend some companies from
the Valley, even our direct competitors” (interviewee N, medium organization);
“Norbert Polska (one of the members of AV) has very similar machine park to
us, it is our the biggest competitor, but we sometimes buy their products and
services […] when we cooperate we do not show them some of our products
[…] we are afraid that they may steal them” (interviewee O, medium organization). Otherwise, from the largest members’ standpoint tight cooperation with
competitors beyond joint R&D activities gives the possibility of building a welldeveloped and efficient supply chain. Integration of widely dispersed subcontractors and suppliers provides shorter lead-time, higher level of suppliers’ specialization and transport costs reduction (Niosi and Zhegu, 2005). All those
coopetition benefits can be used for leveraging global competitiveness. Therefore the key network members introduce coopetition strategy to ensure efficient
managing supply chain (Bakshi and Kleindorfer, 2009).
The highly globalized nature of aviation industry causes that aerospace networks have not only strong internal, domestic connections but also are rooted in
a dense mosaic of international and global links (Dos Santos and Neto, 2009). In
case of AV its members carry out especially direct, cooperative and competitive
activity on the domestic market, maintaining interdependent relationships with national partners. However, the majority of the biggest players being subsidiaries of
global corporations (approximately 25% of AV – Table 1) maintain dense network
of well developed relationships on the global market at the same time.
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Table 1. AVA’s members owned by global corporations
Polish subsidiary
Avio Polska
CAV Aerospace
Goodrich Krosno
Kennametal Polska
M&M Aerospace
PZL Mielec
PZL-Świdnik
Hispano Suiza Polska
WSK Rzeszów
Zakład Kużnia Matrycowa
Siemens Polska
Vac Aero Kalisz
Kreisler Polska
King & Fowler Polska
MTU Aero Engines Polska
BorgWarner Turbo Systems Poland
Sandvik Polska
Hamilton Sundstrand Poland
Parent company
Avio Group
CAV Aerospace Limited
Goodrich Corporation
Kennametal
B/E Aerospace
Sikorsky Aircraft Corporation*
Augusta Westland
Safran Group
Pratt and Whitney*
Ladish Group
Siemens
Vac Aero
Kreisler Manufacturing Corporation
King & Fowler
MTU Aero Engines
BorgWarner Turbo Systems
Sandvik Group
Hamilton Sundstrand*
Headquarters
Italy
UK
USA
USA
USA
USA
UK/Italy
France
USA
USA
Germany
Canada
USA
UK
Germany
USA
Sweden
USA
* Owned by United Technology Corporation (UTC), USA.
Those companies cooperate within cluster on industry development on both
the national and global level. They build efficient supply chain consisting of
SMEs by supporting shared production processes, stimulating procurement processes, facilitating warehousing, and conducting joint research. Simultaneously,
they compete with other large and medium organizations in different fields of
activity, namely production processes, innovation processes, sales and distribution. Furthermore, it is possible that at the same time their parent companies
compete strongly at the global market of final products (e.g. engines, helicopters) or cooperate in the field of global R&D while the subsidiaries are connected through competitive, cooperative or coopetitive relationships on the domestic
market. Therefore, there are processes of simultaneous cooperation and competition at the national level (subsidiaries operating within a Polish cluster) and
coopetition taking place around the globe (parent companies operating at the
global market – Dos Santos and Neto, 2009). The complexity and parallelism of
coopetition trajectories (Bonel and Rocco, 2007) requires taking different levels
of coopetition into consideration (Fig. 1). Such bi-level (national-global), holistic view indicates the multidimensional nature of coopetition.
104
MULTIFACE
ETED NATUR
RE OF COOPE
ETITION…
Figure 1. Typology of coopetitioon in depend
dence of levvel of cooperration
and competition
First, natiional coopetiition – the simultaneity
s
of cooperatiion and com
mpetition at
the nationnal level. Thee first aspectt of national coopetition is
i related to the national cooperaation which – to the authoors’ knowled
dge – is two-ffaceted. Firstlly, it refers
to the relaationships bettween produccers and subccontractors (777% of AVA
A members
are small and
a medium suppliers andd subcontracttors – Fig. 2) which coopeerate tightly
as the final products are
a highly coomplex, extreemely moduular and technnologically
advanced – „It is impoortant to deveelop and expaand network of subcontracctors along
the supplyy chain, they should by loccated close to
o each other, in one particcular region
and be sim
milar in termss of competennces” (intervieewee W).
105
P ATRYCJA K LIMAS
Figure 2. Supply chaain in aviatioon industry
On the
t other hannd cluster’s members co
ollaborate inn research projects. In
general, the
t creation of aviation innovations,
i
new technoologies and kknowledge
takes placce mainly through coopeeration and coopetition
c
u
under
researcch projects
supportedd by Polish government and Europeean funds. Fast
F
changingg environment andd shortening product liffe cycles caall for collabboration (Am
mankwahAmoah annd Debrah, 2011)
2
even with
w direct co
ompetitors. Indeed,
I
moree often the
best partnner is the grreatest comppetitor (Chien
n and Peng,, 2005) becaause it has
appropriate and suitabble knowledge potential as well as technological
t
l advancement – thhe need of brridging the resource
initiatives.
r
gap
p stimulates cooperative
c
Moreoverr rapid and raadical technoological chan
nges cause inncrease of R
R&D costs.
Therefore more and more
m
market members
m
wass looking for financial suppport being
are more willing for collaboration
c
n on joint reesearch projeects (Niosi aand Zhegu,
petitive advanntage (Bonell and Roc2005) enaabling to achhieve collectiively a comp
co, 2007)). Joint impllementation of research projects alllows the suppply chain
members to shorten thhe time of development
d
of new techhnologies andd to divide
ng the most significant bbenefits of
significannt expenditurre on innovaations. Amon
joint impllementation of research projects listed by aviation organizattions there
are: natioonal and inteernational buusiness contaacts, access to
t expertise and trainings, intanngible resourrces of businness partnerss, exchange of
o knowledgee, technology and experience.
e
T above-m
The
mentioned imp
plications off collaborativve processes are nooticed mainlyy by SMEs while the biggest
b
and the
t strongesst cluster’s
members play the rolles of innovaation brokerrs. They suppport SMEs bby helping
them idenntifying theirr innovation and
a knowled
dge needs, seetting up andd managing
the inter-organisationnal cooperation processes (Batterink et al., 20100). Indeed,
n
archhitects, operaators and carretakers (Snoow et al., 19992) being
they are network
responsible for netwoork managem
ment (Möllerr et al., 20055) and leverraging network perfformance. On
O the other hand the big
ggest organiisations are aaware that
106
only very cohesive supply chain can be the real vehicles of knowledge spillovers
(Niosi and Zhegu, 2005) and innovation flows accelerators. It is also worth to
add that to the great extent the development and the cohesion of the cooperation
under the supply chain are mainly due to these large organizations and their
overlapping objectives. Basically their highly complex and modular activity
require a well operating vertical supply chain of diversified suppliers and subcontractors as long as they have to face extremely high coopetition at the global
market – “[…] We have to form and support well-functioning, vertical supply
chain […] to be able to provide final products” (interviewee W).
To sum up, the whole supply chain is developed though research projects,
among the most important AV’s projects there are AERONET, CEKSO, Technological Foresight, Joint Sky, Research and Development Laboratory for Aviation Materials (Table 2).
Table 2. Collaboration through research projects
AERONET
CEKSO
INTERREG IIIA
Technological foresight
Joint Sky
Enterprise Europe Network
Wings for Regions
Aeropolis
Projects
Centre of Advanced Technologies; development of aviation
technologies and materials
Regional Centre for Transfer of Modern Technology; education of
future workforce in Practical Training Centers for CNC operators
Development and promotion of cross-border
Polish-Ukrainian aviation cluster
Directions of development of material technologies
Development and integration of the innovating aviation cluster;
development of communication and knowledge exchange
Knowledge and state of the art technology transfer
Development of cooperation between leading European aviation
clusters
Technology Park
In general, collaboration is strong side of Aviation Valley. It is not surprising
since joint effort of different players in the value chain provide technological improvements and innovations (Cassiman, 2009). However, it should be noticed that
the collaborative processes refers – only – to the one side of national coopetition
within considered supply chain. Simultaneously, coopetition at the national level
covers the competitive processes connecting particular members of the supply chain.
Aviation Valley concentrates almost the entire Polish aviation industry.
Therefore it should not be surprising that within cluster there are some direct and
indirect competitors. Generally the considered supply chain consists of independent, loosely coupled organizations differentiated in terms of size and ownership. They can also be divided according to the level of value chain at which they
operate and according to the level of competition inside the network (Table 3).
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P ATRYCJA K LIMAS
Table 3. Example of competitors in the domestic market
Supply chain
raw materials
hardware
different types of machining
special processes
original elements
Example members
Alinox
Arkom
HSW Narzędziownia
Marco Expot-Import
TW Metals
BE Aerospace
Technology Management
Consultants-Poland
Admil
Aviomechanika
Creuzet
Fin
Iwamet
Remog
Ultratech
Cerel
EL Automatyka
King and Fowler
Vac Aero
Avio Polska
Goodrich Krosno
Hispano Suiza
MTU Aero Engines
Pratt and Whitney Kalisz
PZL Mielec
PZL Świdnik
WSK Rzeszów
Size
Competition level
local & national
local & national
small
and
medium
local & national
local & national
large
national & global
indirectly
Particular levels of value chain represent different areas of competition. The
competitive relationships are differentiated. Most of AV’s members are subcontractors and suppliers. Here the most intensive competition is reflected in the
field of raw materials and treatment processes. Moreover most of subcontractors
are Polish SMEs which compete on domestic market, within Polish part of global value chain. On the other hand AV’s members owned by global corporations
compete at the market of original elements (e.g. blades, turbines, engines) at
both domestic and global level.
The essence of coopetition is that companies compete and cooperate
parallelly in different fields of activity. The national coopetition arises from
overlapping national cooperation and national competition (Table 4) and can be
described as coopetition strategy on meso level (Dagnino and Padula, 2002).
108
Table 4. Review of coopetition areas at national level
Cooperation
Margański & Mysłowski
Rzeszów University of
Technology
Competition
testing of
composites
new composite
technologies
PZL Świdnik
Avio Polska
WSK Rzeszów
MTU Aero Engines
Rzeszów University
of Technology
Avio Polska
Technology
research on
aircraft turbines
energy-saving
turbines
WSK Rzeszów
Technology
research on aerospace propulsion
drive boxes
Technology
Warsaw University of
Technology*
multifunction
moto-glider
light and
ultra-light
gliders
Royal Star
Fly
WSK Rzeszów
PZL Mielec
testing new
products
and service
pilot trainings
Aviomechanika
Norbert Polska
thermal and
electro-chemical
treatment
machining
Avio Polska
WSK Rzeszów
Rzeszów University
of Technology
Warsaw University
of Technology*
PZL Mielec
Rzeszów University
of Technology
Royal Star
Fly
Aviomechanika
Norbert Polska
Arkom
Erkom
* Does not belong to the AV.
Considering coopetition strategy among the set of independent organizations the broaden view is needed. Particular member can compete in one area
with one network member (A) and at the same time it may cooperate within that
area with another (B). On the other hand that particular member can cooperate with
some network members (A or/and B) in one area and at the same time compete with
them within different fields of activity. Thus within the Aviation Valley the complex
network coopetition (Dagnino and Padula, 2002) can be identified.
The existing literature states that coopetition is a combination (Bengtsson
and Kock, 2000; Gnyawali and Park, 2009; Li et al., 2011) of appropriate level
of competition and cooperation. Base on the intensity of those relationships Luo
distinguishes four types of approach to coopetition (Osarenkhoe, 2010): alienator
(monoplayer), contender, partner and co-opetitor (adapter). These types differ also in
rent-seeking strategic behaviours (Lado, 1997). Within the Aviation Valley all of
these coopetition models are introduced (Fig. 3.) as the network members apply
differentiated configurations of competitive and cooperative relationships.
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P ATRYCJA K LIMAS
Figure 3. Example of different types of coopetition among AVA.
High
Cooperative
orientation
Low
Competitive orientation
Low
High
Partner displaying collaborative
Co-opetitor (Adapter)
rent-seeking behavior,
displaying syncretic
e.g. Rzeszów University
rent-seeking behavior,
of Technology
e.g. WSK Rzeszów
Alienator (Monoplayer) displayContender displaying
ing monopolistic rent-seeking
competitive rent-seeking
behavior,
behavior,
e.g. Cerel
e.g. PZL Świdnik
Source: Based on Luo’s typology [Luo et al., 2006]
First, monoplayer (or alienator) maintains low degree of both cooperation
and competition. For instance Cerel Energy Institute does not show strong engagement in cluster’s activity. It remains on the periphery of the cluster – “It is
good to have Valley’s logo on our website, it adds prestige, but nothing more”
said Cerel’s director. Second, contender maintains high degree of competition
and low degree of cooperation. Within Aviation Valley PZL Świdnik introduces
a contender model. It competes e.g. with WSK Rzeszów at the nacelles market
and with Norbert Polska at the engine cowling market. On the other hand it does
not show strong commitment in cooperation within the cluster – „We are a little
bit on the edge of the Valley […] we do not have the material advantages, in
case of projects we prefer cooperate internationally” (R&D manager, PZL
Świdnik). Third is partner that exhibits strong willingness to cooperate and aversion to compete and in case of AV the Rzeszów University of Technology
(RUT) displays this approach. It participates in most of the projects conducted
within AV, very often even as a coordinator or initiator – „We need urgently
good regional aviation, which is why we have to cooperate” (RUT professor).
On the other hand it is public university, so it does not engage in economic activity in the aviation sector. Therefore RUT does not display competitive behaviors. Last, but not lest is the co-opetitor which maintains high degree of cooperation and competition. In case of Aviation Valley the WSK Rzeszów could be an
example of the adaptation of this type of coopetition strategy. It is strongly engaged in the most of cooperative projects and initiatives and at the same time it
doggedly competes with Avio Polska (turbines, blades) or PZL Świdnik (engines) at the national level. As you can notice all of the considered above
coopetition models are differentiated in terms of coopetitive relationships
(Bengtsson and Kock, 2000). A cooperation-dominated relationship appears
when coopetitive relationships consist of more cooperation than competition
110
(typical for partner’s approach). A competition-dominated relationship occurs
when coopetitive relationship includes more competition than cooperation (standard
for contenders). Finally, an equal relationship appears when cooperation and competition are equally distributed (characteristic for alienators and co-opetitors).
Second, global coopetition – the simultaneity of cooperation and competition at the international level. While Polish branches and subsidiaries coopete,
cooperate or compete among Aviation Valley’s boundaries restricted to the borders of Poland (national level), their global owners cooperate, compete, coopete
internationally (global level) –Figure 1.
The global corporations represented in AV belong to the world's leading
aerospace and aviation companies. They are main suppliers and subcontractors
for such companies like Boeing, Airbus, Bombardier, Embrayer, or Lockheed
Martin. At the global level they also have to deal with global competitors like
Turbomeca, Snecma, Rolls-Royce, Renault, GE Aviation, Tusas Engine Industries, or Volvo Aero. The nature of the global aerospace industry motivate to (or
even impose) international collaboration, but at the same time high global competition cannot be removed. Therefore global aviation companies just as their
subsidiaries at national level, implement coopetition strategies at the global one. It
means that they maintain both, competitive and cooperative relationships with
members of global aviation industry (Table 5). For example MTU Aero Engines and
Avio Group cooperate with global leaders on new, high-efficient liquid fuel while
United Technology Corporation conducts research in the same area on its own. At
the same time UTC together with Avio Group is working on new low-pressure turbine and together with MTU Aero Engines on blades improvements.
Table 5. Review of cooperative and competitive fields of activity between aviation
world leaders
Global corporations
1
2
United Technology Corporation (Hamilton
Augusta
Sundstrand Power SysWestland
tems; Sikorsky)
United Technology Corporation (Pratt and WhitSafran
ney Canada)
United Technology Corporation (Pratt and WhitAvio Group
ney Canada)
United Technology CorMTU Aero
poration (Pratt and WhitEngines
ney Canada, Sikorsky)
Cooperation
3
Competition
4
Electric power, engine
control systems,
gearboxes
Helicopters
Shafts for aircraft
engines
Engine components –
gears, housings
Low pressure turbines
and the power transmission
Turbofan power plant
systems, blades and
turbines
blades and turbines
Liquid fuels
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P ATRYCJA K LIMAS
table 5 cont.
1
2
MTU Aero Engines
Avio Group
Avio Group
Augusta
Westland
3
Atomization and
combustion of liquid
fuels
Joint Technology
Initiative (JTI) – clean
sky
4
blades and turbine components
In the helicopter sector,
Avio cooperate with GE
Aviation and UTC –
main competitors of
Augusta Westland
Considering the nature of global coopetition, the configuration of cooperative and competitive relationships fully reflects the characteristic of national
coopetition (cf. Tables 4 and 5). Global corporations are more willing to cooperate in the first stages of global value chain and are more focused on competition
in the final ones (Bengtsson and Kock, 2000). However, there is significant difference between national and global coopetition. In case of global coopetition
strategy there are no restrictions on the selection of partner for collaboration. For
example Avio Group and MTU Aero Engines cooperate with the main competitors of UTC which remain outside the main and basic supply chain providing
e.g. aviation engines. But at the national level, within Aviation Valley it is forbidden to maintain collaborative relationships with the most dangerous competitors not belonging to the cluster and remaining outside the supply chain – “Cooperation with GE or Rolls-Royce is frowned upon. You know they are the great
trinity: GE, UTC and Rolls-Royce. If you provide something for UTC you can
forget about the other two and vice versa. We are in Rolls-Royce and GE’s databases but it is better to not cooperate with them” (vice president of medium enterprise). The difference between national and global coopetition inside aviation
industry results from the nature of relationships between companies. Global
corporations are completely autonomous. In turn, AV’s members form a cohesive cluster together and they have to adhere to the generally applicable principles of cooperation imposed by the whole strategic network. They have to comply even if they remain formally and officially independent. For instance, Zakład
Kuźnia Matrycowa supplies rugged, reliable, high-quality forgings for the most
important AV’s players (e.g. Avio Polska, Goodrich Krosno, Hamilton
Sunstrand Polska, WSK Rzeszów, MTU Aero Engines Polska and PZL Mielec)
and it does not provide (and according to the statute of AV it is not allowed to
provide) any services for aviation companies remaining outside the cluster. At
the same time its parent company (Ladish Corp.) is a subcontractor for GE Avia-
112
tion, Rolls-Royce, Tusas Engine Industries and Volvo Aero – the greatest competitors of UTC, MTU Aero Engines and Avio.
Third, hybrid coopetition – the simultaneity of national (global) cooperation
and global (national) competition at the international level. Hybrid coopetition
occurs when: a) subsidiaries at national level cooperate with each other, while
their parent companies compete at global level, or b) subsidiaries compete at
national level, while their parent companies cooperate with each other at global
level one. One of the common forms of hybrid coopetition in aviation industry is
coopetition in the sphere of R&D. At the national level AV’s members cooperate
though participation in wide range of research projects that aims at gaining financial support, inventing new components or developments of provided engines. At the same time their parent companies compete globally at the sphere of
R&D activity trying to win the race for new technologies and improvements of the
final products like aircraft and aerospace engines. For instance, MTU Aero Engines
and Avio Group cooperate separately with Turbomeca, Snecma, Rolls-Royce and
Renault together on new, high-efficient liquid fuel. In other words, at the global
market they realize separate, competitive research projects which aims are covering.
At the same time, the MTU Aero Engines and Avio's subsidiaries cooperate at the
Polish market for instance within AERONET on composite technologies.
Fourth, multidimensional coopetition – the simultaneity of national
coopetition and global coopetition. The most complex dimension of coopetition
refers to the situation when companies adopt coopetition strategies at both global
and national level. Within Aviation Valley the multidimensional coopetition
occurs when both subsidiary and its parent company adopt coopetition strategy.
Therefore multidimensional coopetition affects only the largest and the most
important members with international roots. From the perspective of particular
members of Polish supply chain multidimensional coopetition refer to the situation when they coopete directly at national level and simultaneously coopete
indirectly (through their parents companies) at the global one. For instance Avio
Polska (owned by Avio Group) is connected with PZL Świdnik (owned by Augusta Westland) by coopetitive relationships as they cooperate in the area of
production of the composites and compete in the field of energy-saving turbines.
At the same time Avio Group (owner of Avio Polska) is connected with Augusta
Westland (owner of PZL Świdnik) by coopetitive relationships as they cooperate
under the Joint Technology Initiative of and compete on the market of helicopters. In such situation Polish subsidiaries coopete directly at the national level
and coopete indirectly at the global level what is characteristic for multidimensional coopetion.
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Conclusions
Coopetition can be identified when cooperative and competitive relationships are identified at the same time, and competition and cooperation between
independent organizations are implemented in parallel. In case of considered
network, being complex supply chain and an integral part of global aviation/aerospace value chain, coopetition seems to be multifaceted phenomena. It
can be identified between varied stages of supply chain and observed at different
levels of aviation activity. In authors’ opinion it is possible to distinguish four
types of coopetition inside supply chain, namely: national coopetition, global
coopetition, hybrid coopetition and multidimensional ones. All of the abovementioned types of coopetition are varied in terms of: a) the market scope of
coopetition, b) types organizations engaged (i.e. subsidiaries/parent organizations), c) stages of supply chain maintaining coopetitive relationships, and d)
complexity and directedness of coopetition.
First, the simplest variant of coopetition is the national coopetition referring to simultaneous cooperation and competition at the national level and reflecting joint occurrence of cooperation and competition across functional areas
within cooperating members of supply chain (Luo et al., 2006). Inside Aviation
Valley national coopetition takes place at the early stages of the overall global value
chain (Figure 2). This level of coopetition applies to all cluster members implementing coopetitive strategy: all members of supply chain coopete in the field of R&D,
but the SMEs coopete also in the area of aviation components and special processes
provided for the largest organizations (subsidiaries of global corporations).
Second, the geographically distant global coopetition referring to simultaneous cooperation and competition at the global level. In case of Aviation Valley
global coopetition takes place at the final stages of the overall global value chain
(Figure 2) and is related to large organizations being subsidiaries of global corporations. These supply chain members owned by international corporations are indirectly related to each other by cooperative and competitive relationships at the global
level as they parent companies maintain cooperative and competitive relationships
with each other at the global market, at the stage of final products. In other words, in
global coopetition it is possible to identify indirect coopetitive relationships between
subsidiaries as they owners adopt coopetition strategy at the global level.
Third, hybrid coopetition referring to simultaneous cooperation and competition at the global and national level at the same time (a combination of national competition/cooperation and global cooperation/competition). Inside Aviation Valley hybrid coopetition takes place at the all of the stages of the overall
114
global value chain (Fig. 2). For instance, a parent company competes for final
product market at the global level (cooperates on research and development of
new products at the global level) while its subsidiary cooperates on joint implementation of the research project at national level (competes on sales of aviation
components at national level). It should be added that that type of coopetition
occurs rather temporarily and is related mainly to the largest and the most important members of AV having global owners.
Last, but not least dimension of coopetition within supply chain is a multidimensional coopetition referring to simultaneous cooperation and competition
at the global and national level at the same time. As opposed to the hybrid
coopetition it appears when both subsidiary and its parent company implement
coopetition strategies and coopetition takes place simultaneously on two levels:
global and national. It means that both the subsidiary and parent organization maintain coopetitive relationships. It is the most advanced and complex coopetitive interdependences related especially to the most important players within national supply
chain and global value chain. In case of Aviation Valley multidimensional coopetition
takes place across all of the stages of the overall global value chain (Fig. 2).
To summarize, the plurality of coopetition levels and dimensions makes
Aviation Valley intensive coopetition network (Chi et al., 2008) organized as
a formal cluster consisting of independent however interdependent supply chain
members. It is copious in both competitive and cooperative relationships. There
are many competitors connected by coopetitive relationships at several levels of
supply chain (Table 4 and 5). Within the domestic part of the global supply
chain the cooperative relationships appear mainly at the early stages, especially
in the R&D activity whilst the competitive ones occur especially at the final
stages, in the field of finished aircrafts and its finished components. The obtained results remain in line with prior research on coopetition pointing out that
the closer to the customer or final product, the stronger the competition between
business network partners (Bengtsson and Kock, 2000).
It should be highlighted that network members are varied by the complexity
and dimensionality of introduced coopetition strategy. The less complex and
more one-dimensional coopetition refers rather to SMEs than to large organizations. On the other hand the most advanced and sophisticated variants of
coopetition, i.e. multidimensional and hybrid coopetition are related to the most
significant companies with strong international connections and global roots.
The differences in the intensity and specificity of coopetition among and between particular members of supply chain are important as the nature of compe-
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P ATRYCJA K LIMAS
tition between coopetitors affects the level of cooperation they provide to each
other (Gurnani et al., 2007). Therefore it can be said that these differences are
reflected in the intensity and complexity of relationships, in the possible and
achievable benefits of cooperation, and in the level of competitive advantage
reached due to more or less intensive coopetition (Gurnani et al., 2007). Moreover there are both intentional and emerging coopetition (Czakon, 2009). Intentional coopetition refers rather to the largest and the most important network
actors, while emerging coopetition refers to the SMEs. It points out the differences in the approach to the coopetition strategy adopted. The largest organizations
seem to adopt intentional, purposefully benefit-oriented coopetition strategies while
the SME supply chain members rather prefer the emerging approach appreciating
and utilizing the benefits of coopetition strategy during its implementation.
In conclusion it should be said that the authors are aware of some limitations related to the presented considerations. The majority of them results from
the methodological approach adopted. The research was based on a single, interpretative case study (Stake, 2009) aimed at theory building what we see as a barrier to
generalization. Furthermore, all of the above-mentioned considerations refer to
the coopetition phenomena observed in one, purposefully chosen supply chain
functioning under specific, high-tech, networked and globalised environment. It
means that the process of drawing general conclusions and statements ought to
be careful, prudent and rather limited. However, the nature of the study was
rather exploratory than explanatory what justifies the conclusions drawn based
on one intentionally chosen case. To the authors’ knowledge and besides all of
the above-mentioned limitations the conducted research sheds some new light on
the coopetition concept – especially in the field of the characteristic and specificity of coopetitive ties between and among cooperating competitors. The studies
have proved the complexity and multidimensionality of coopetitive relations and
have pointed out that the particular connections between coopetitors can be not
only cooperative and competitive at the same time, but also that they can be
varied in terms of level and scope of coopetition including national, global and
multidimensional coopetition. Moreover the research has revealed some differences between large and SMEs in terms of the level of coopetition adopted. In
authors’ opinion that last aspect should be explored deeper in future research as
the differences between large companies and SMEs in their strategic approaches,
performances and scope of activity may be connected with the level of the maintained coopetitive relationships within supply chain.
116
Acknowledgements
The preparation of this paper was supported by a research grant from the
National Science Centre under the project titled: Organisational Proximity in Innovation Networks (contract number: DEC-2011/03/N/HS4/00372). I would like also
to acknowledge the help of all individuals who made important contributions to
improving this paper including: professor Czakon, who offered his comments about
the structure of the paper, and two anonymous reviewers of the last version of the
paper submitted to the “Journal of Economics and Management”.
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Wojciech Czakon, Karolina Mucha‐Kuś Mariusz Rogalski University of Economics in Katowice, Poland
COOPETITION RESEARCH
LANDSCAPE – A SYSTEMATIC
LITERATURE REVIEW 1997-2010
W OJCIECH C ZAKON , K AROLINA M UCHA -K UŚ , M ARIUSZ R OGALSKI
Abstract
This paper adopts the approach of systematic review and provides a synthesis of the literature on coopetition in management and strategy. Our review focuses first on the phenomenon features, and offers a consensus-based definition.
Next, we address the theoretical frameworks used by researchers and examine
the linkage with related fields in order to outline the distinctive lines of
coopetition versus alliances. Then we review the empirical foci adopted in published work to identify both preferred and underexplored areas of extant academic attention. We draw a research agenda oriented at further exploration of
coopetition morphology, elucidating the stability issue and examining
coopetition along the antecedents-process-outcomes trail.
Keywords: coopetition, management, strategy, literature review.
Introduction
Coopetition has enjoyed a sustained rise to recognition across management
literature (Yami, Le Roy, Castaldo, Dagnino, 2010; Gnyawali, Park, 2011). The
term refers to the simultaneous use of cooperation and competition in order to
achieve better collective and individual results. Coopetition helps firms to improve performance (Le Roy, Marques, Robert, 2009), increase market share
(Meade, Hyman, Blank, 2009) or to develop new technologies and products
(Quintara-Garcia, Benavides-Velasco, 1996). Theoretical contributions suggest
that coopetition is the best strategic option for firms (Brandenburger, Nalebuff,
1996), and takes account of both competitive and collaborative advantages
(Lado, Boyle, Hanlon, 1997).
There are several reasons for undertaking a literature review on coopetition.
Firstly, the term has been coined by managers who acknowledge the need to use
both cooperation and competition with the same set of firms. Hence, the relevance of coopetition is recognized since its inception. Secondly, the growing
body of literature offers the opportunity to develop coopetition theory further
than it has been done so far. Thirdly, the delimitation of coopetition versus related concepts remains unclear. Coopetition extensively draws on cooperative
interorganizational relationships literature, making the concept’s boundaries
blurry. Specifically, it remains unclear whether coopetition is a separate phenomenon, with distinctive conceptual and methodological grounds, or whether it
122
COOPETITION RESEARCH LANDSCAPE…
is a combinative concept. Combinations typically draw on concepts from root fields,
which inevitably leads to paradigmatic tensions. Fourthly, coopetition phenomenon
recognition remains relatively weak in mainstream management literature so that the
concept is still promising instead of becoming accomplished.
The aim of this research is to address the state of the art and emerging research topics within the coopetition literature. The paper is organized in four
sections. We begin by outlining the systematic review method (Tranfield et al.,
2003) and provide key bibliometrics on the reviewed literature. The second section highlights the concept’s origins and the features attributed to coopetition in
the literature. On that base we propose a coopetition definition, as a literature
based consensus. Then, we examine the theoretical lenses used by researchers to
study coopetition. This helps to point out to differences against alliances and
unveil under exploited approaches. Next, empirical studies extracted from citation databases are reviewed, and an up-to-date analysis of the emerging trend is
tabulated. This reveals a number of gaps in terms of industries, levels of analysis, and key topics of scrutiny. Finally, a critical discussion on the emerging research agenda is developed.
1. Method
A growing recognition of literature review rigour’s importance stems from
recent published works (Lee, 2009). Systematic literature review provides an
audit line for reviewing literature, as it uses databases selection and search in
order to explicitly demonstrate how relevant literature has been extracted from
the existing body of literature. Further, it uses bibliometrics to identify trends
and structures within the publications under scrutiny. In this study we draw on
frequency analysis in order to identify coopetition features and citation analysis.
Then, we exploit critical literature review techniques in order to draw conclusions on coopetition research, identify gaps in current understanding and recommend further research agenda.
Most of the existing contributions acknowledge coopetition as a complex
and dynamic phenomenon (Padula, Dagnino, 2007). Importantly for this study,
coopetition is being increasingly popular among scholars and managers because
it takes account of real-life complexity in inter-firm relationships and expands
the strategist's view beyond a single firm scope (Brandenburger, Nalebuff, 1996).
Table 1 illustrates the increasing number of coopetition published work ranked in the
Social Sciences Citation Index (SSCI) related to strategy and management.
123
Table 1. Coopetition research publication dynamics
Year
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Number of papers published
1
1
2
2
2
3
5
5
3
5
14
14
17
25
The selection of published work for our study consisted of three phases
(Tranfield et al., 2003). Firstly, we identified the databases that hold comprehensive citation lists for management and strategy: Ebsco, Elsevier/Springer, Emerald, Proquest, and ISI Web of Knowledge. Secondly, we searched the databases
for coopetition studies explicitly related to business and strategy by applying
a key word search: coopetition/co-opetition located in the title, abstract or key words
of papers published in English from 1997 to 2010. The use of different orthography
appeared necessary, as there is still no consensus around the spelling, and authors
use both. Thirdly, we have excluded papers which limited the use of coopetition to
the keyword, but did not refer explicitly to the concept or did not investigate it further. We have also excluded book reviews and editorial introductions from the initial
database, as well as duplicate papers appearing in the databases. Finally, we restricted the study to work published in English, even though a substantial literature is
available in French, Spanish, Polish or other European languages.
Table 2. Three-phase selection used in the research
Selection criteria
I selection
II selection
III selection
124
identified papers on coopetition
English and full text papers supplemented with additional
articles from the external databases
exclude duplicate papers
coopetition issues as a leading research problem
extensively described issues of coopetition
sector approach of the empirical research
Number of papers
523
245
167
106
96
82
Thus, from an initial dataset of 523 papers we retained 96 for our study,
while for 82 a clear industry focus could have been identified (Table 2). In order
to triangulate these results the selection procedure has been conducted independently by another researcher.
Coopetition has been particularly studied by European scholars, as 2/3 of
published work is affiliated to European research institutions. American papers,
come second with 24% followed by Asia was 9%, and Australia just 1% of all
the published papers.
Figure 1. Geographical distribution of coopetition papers affiliation
70%
percentage of the total number of paper
60%
50%
40%
2010-01.2011
2005-2009
30%
2000-2004
1995-1999
20%
10%
0%
Asia
Australia
Europe
North America
geographical region
A forward citation analysis allows to identify the most cited papers, and
definitions adopted by researchers (Table 3). While most papers come from the
EU, the top 5 papers in terms of citation display US affiliations, with the notable
Scandinavian exception (Bengtsson, Kock, 2000). The citation analysis mitigates
frequency analysis results. While European publications dominate in number,
they appear as less influential in the field. Also, the number of coopetition publications remains quite limited in the top journals.
Table 3. Top 5 cited papers and related views on coopetition nature
Authors
Year
published
Number
of citations
Coopetition definition
1
2
3
4
Tsai
2002
145
simultaneously cooperative and competitive behaviour
Gnyawali,
Madhavan
2001
117
the relevant network consisting of formalized cooperative relationships among competitors that involve flows of assets, information and status
125
table 3. cont.
1
2
3
4
Bengtsson,
Kock
2000
82
This relationship can include both economic and
non-economic exchanges. Power in the cooperative
side of the relationship is based on functional aspects in accordance with the value chain. In the
competitive side of the relationship, power is based
on the actor's position and strength.
Luo, Slotegraaf,
Pan
2006
28
Joint occurrence of cooperation and competition
across functional areas within a firm
Levy,
Loebbecke,
Powell
2003
21
Simultaneous cooperation and competition may aid
competitiveness by knowledge sharing, but any
exchanged knowledge may be used for competition.
2. Coopetition – a multifaceted concept
2.1. Coopetition term provenience
The etymology of the term coopetition refers to competition and cooperation appearing in the same time between the same actors. The literature widely
locates its origins in the 1990' when R. Noorda a former CEO of Novell (Padula,
Dagnino, 2007) used it to grasp the nature of relationships between competitors.
Others trace it back to 1913 (Cherington, 1976) when Kirk S. Pickett, who used
it to describe the relationships among his 35,000 oyster dealers, stated: “You are
only one of several dealers selling our oysters in your city. But you are not in
competition with one another. You are co-operating with one another to develop
more business for each of you. You are in co-opetition, not in competition”.
Then R. Hunt reintroduced “co-opetition” in the Los Angeles Times in 1937, but
none of these early introductions received any public attention (Hunt, 1937; Yami, Le
Roy, 2010). In the management literature A. Brandenburger and B. Nalebuff (1996)
have popularized coopetition. They claimed it to be more than a linguistic blend of
cooperation and competition. Inversely, coopetition is to be seen as a new mindset,
a process, or a phenomenon combining cooperation in order to create a bigger business pie, while competing to divide it up (Brandenburger, Nalebuff, 1996).
Importantly the term coopetition has been coined by managers, which indicates
an empirically grounded need to grasp the complexity of real life relationships between firms in an comprehensive way, beyond competition or collaboration alone.
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2.2. Elementary facets of coopetition
We have conducted a frequency analysis in order to identify the most popular facets of coopetition and provide a synthetic view. We focused on coopetition
definitions adopted in the papers. Authors’s descriptions allowed us to identify the
features of coopetition, and coded them. This part of our analysis was conducted by
two researchers separately. Finally, the frequency analysis was conducted. In result,
we have identified six distinctive features attributed to coopetition.
Interestingly all 136 authors of the 96 papers we analysed unanimously recognized (1) simultaneous cooperation and competition, and (2) mutual benefits
stemming from coopetition, as the key characteristics of the phenomenon of
coopetition. Authors also referred to additional features such as: (3) complexity
with 57% of indications, (4) dynamics with 47%, and (5) managerial challenges
scoring 46%. Finally, 28% of the authors identified (6) industry reshaping as a trait
of coopetition (Figure 2).
dimension of coopetition
Figure 2. Coopetition concept features frequency distribution
simultaneousness
100%
mutual benefit
100%
complexity
57%
variability
47%
managerial challenges
industry reshaping
46%
28%
percentage of authors' indications
The frequency analysis allows to draw a generally accepted definition of
coopetition, focused on the key traits of a distinct class of phenomena. We believe that our systematic literature review justifies the claim that coopetition is
a mutually beneficial for involved actors, simultaneous cooperation and competition
interplay. Our data indicate a shared view of researchers that coopetition is ontologically distinct through these two features, which are not found in any other concept. It
clearly delineates coopetition from other types of interorganizational relationships. We discuss each feature appearing in the literature below.
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A fundamental feature of coopetition is the simultaneity of competitive and
cooperative relations between actors (Luo, 2007). Coopetition is therefore distinct from sequential cooperation and competition, where one relationship follows the other between the same firms, but not in the same time. Consequently,
the study of horizontal relations between coopetitors dominate in number of
papers, achieving 74% of the total. Just 14% deal with vertical relations. This
stands in contradiction to M. Galvagno and F. Garraffo (2010), who claim that
“most research and theory building on coopetition has focused on vertical relationships among firms (that is, channel relationships), ignoring horizontal relationships (that is, direct competitors)”.
Figure 3. Type of coopetition under study
percentage of the total numer of research studies
120%
100%
80%
60%
horizontal
vertical
40%
horizontal/vertical
20%
0%
period
Extant research suggest that the competitive and collaborative behaviors
strongly impact each other (Mariani, 2007; Tidstrom, 2008). For instance, the
upsurge of competition within a collaborative agreement alters the relationship
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and hampers its performance. Similarly, fostering collaboration within competitive relationships alters market structure and provides advantages to competitors.
These influences follow each other. Untangling the reciprocal influence requires
a process approach, event story building and path identification.
The aim of coopetition is to provide benefits for all actors involved. Most
frequently, through pooling resources competitors are able to achieve a competitive advantage over other market actors (e.g. Bengtsson, Kock, 2000, p. 424; Wang,
Krakover, 2008, p. 128; Walley, 2007, p. 17; Luo, 2007, p. 130). “Coopetition emphasizes the mixed-motive nature of relationships in which two or more parties can
create value by complementing each other’s activity” (Bonel, Rocco 2007, p. 71).
Beyond the synergistic use of resources and complementarity advantages, restricted
access to resources provides coopetitors with an edge over other market players.
The strategic behavior of coopeting firms is oriented both at the advantages
of stimulating competition and complementary resources access through cooperation (Robert, Marques, Le Roy, 2007). This facet of coopetition captures rent
maximization by managers who basically want to 'have the cake and eat the
cake' at the same time. Game theory offers theoretical explanations that a repeated game can provide better yields for players if they exchange information and
align behaviors (Okura, 2007). While researchers provide theoretical insights and
empirical evidence on how coopetition generates value, the rent appropriation process remains relatively underexplored. Few studies (Bonel, Pellizzari, Rocco, 2008)
have explored the risks rising from coopetitive relationships. Yet, it holds promise of
a better understanding of the phenomenon. There is a clear gap in our understanding
on why coopetition is stable over time between the same actors despite rent appropriation related tensions, typical to interorganizational relationships.
Researchers have recognized in more than half of published work that
“coopetition strategy is a multidimensional and multifaceted concept that assumes a number of different forms and requires multiple levels of analysis”.
(Chin, Chan, Lam, 2008). Therefore, different levels of analysis can be identified
in the literature: network (Gnyawali, Madhavan, 2001), supply chains and value
networks (Peng, Bourne, 2009), firms level for direct competitors (LeTourneau,
2004), and groups/departments/subsidiaries (Tsai, 2002) for firms. There is also
a distinction of internal coopetition, within business ecosystems, versus external,
which occurs between business ecosystems (Gueguen, 2009).
A clear majority of existing research is focused on the interorganizational
level of analysis achieving 73% of analyzed papers, while dyadic relations are
introduced in 42% of the total. Both of them are also divided into simple or
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complex relations (Padula, Dagnino, 2007; Robert, Marques, Le Roy, 2009).
Inversely, network level studies, as well as firm level studies remain relatively
few. This leaves network roles, the impact of interfirm linkages structures or
processes beyond the scope of understanding. Also, intrafirm issues such as capabilities (Quintana-García, Benavides-Velasco, 2004), systems (Levy, Loebbecke, Powell, 2003) or communication are relatively underexplored. Interestingly coopetition
has been found at different levels of analysis, which suggests it is disconnected from
any particular type of relationship. This manifestation at various levels makes
coopetition a phenomenon, much more than just a relationship feature.
Coopetition relations are by nature constantly evolving, which generates
a significant interest in coopetition dynamics. This concerns both the intensity of
coopetition relationships and the length of period for which they are concluded.
The market environment in which firms operate has significant impact on
coopetition dynamics (Rusko, 2011; Luo, 2007). “[…] the content of a relationship
can change from competition at one point of time, to cooperation, or coexistence, or
co-opetition at another. Moreover, some relationships can grow stronger, leading to
a termination of weaker relationships” (Bengtsson, Kock, 1999).
The literature often labels coopetition as paradoxical (Lado, Boyd, Hanlon,
1997). Much more than a dialectical perspective of opposing forces balance or
coincidentia oppositorum (Padula, Dagnino, 2007), paradoxes refer to understanding what the equilibrium really is, and call for a clear identification of the
many different forces which may impact coopetition. The equilibrium approach
resides on an ideal state of relations between actors, in spite of forces impacting
on them. In fact “theorist focus on equilibrium arguments in order to more fully
understand the dynamics of systems that are not in equilibrium” (Barney, 2001).
For instance, the initially proposed nature of coopetition generates positive-sum
games for firms (Brandenburger, Nalebuff, 1996). Theoretical models offer ideal-type behaviors, allowing for empirical studies to unveil why this ideal equilibrium is not achieved (Okura, 2008), as well as descriptive empirical studies
which exemplify success stories of coopetitive action (Gueguen, 2009). In sum,
this approach assumes a predefined, or anticipated equilibrium which is compared to actual interorganizational relationship dynamics.
Interestingly, 23% of papers in our study highlight relationship dominance
within coopetition. No balance between competition and cooperation is implied,
be it in terms of intensity or magnitude. Notably coopetition has been categorized as: (a) cooperation-dominated relationship, (b) equal relationship, (c) competition-dominated relationship (Bengtsson, Kock, 2000; Rusko, 2011). Ritala,
130
Hallikas and Sissonen (2008) name them as intensive competition coopetition or
intensive cooperation coopetition. Luo (2005) expands this typology by adding
to dominated relationships two types of balanced ones: low-low and high-high
intensity of collaboration and competition (Table 4).
Table 4. Coopetition types regarding relationships intensity and balance
Collaboration
Competition
high
low
high
high intensity coopetition
competition dominated
low
cooperation dominated
low intensity coopetition
This simple classification of possible coopetition types calls for more detailed studies. One thread of research should focus on the relationship intensity.
Beyond a measurement challenge, more intermediate intensity degrees can be
identified: medium, semi-strong and so on. As a result the typology matrix
would expand to cover nine, sixteen or more theoretical coopetition manifestations, to be further empirically examined. A second thread of further scrutiny
needs to explore coopetition stability issue over time. While equilibrium perspective appears as useful for balanced (hi-hi or low-low) coopetition studies,
there is a gap in our understanding of unbalanced relationships. Dialectical perspective would suggest them to be inherently unstable or short-term, but empirical evidence shows that firms may remain in unbalanced or dominated coopetition for extended periods of time.
The nature of coopetition is reflected in considerable managerial challenges
that coopetitors have to face in order to succeed. Researchers (Zineldin, 1998) underline that adequate competence and ambidexterity of the management team are
key factors in the success of a coopetition strategy. “Co-opetition involves two different logics of interaction. On the one hand, there is a hostility due to conflicting
interests and, on the other hand, it is necessary to develop trust and mutual commitment to achieve common aims” (Quintana-García, Benavides-Velasco, 2004).
Moreover, some authors add a high level of managerial involvement in the
process of the preparation, implementation and coordination of the coopetition
as equally important factors in the success. What seems to be undervalued here
is the matter of trust between cooperating competitors. Despite their long-term
habits, on the one hand managers have to make room for cooperative relations
which, without trust and a general positive atmosphere, cannot exist, while on
the other they have to know the lines that they cannot cross as regards the close-
131
ness of the relationship (e.g. protection of trade secrets) (Chin, Chan, Lam,
2008). Similarly to firm level studies, managerial challenges address the relevance of coopetition research in terms of success or failure factors. While theoretical directions of scrutiny have been indicated in the literature, empirical studies remain few.
While coopetition has originally been related to the value creating network
(Brandenburger, Nalebuff, 1996), only a limited number of papers address this
feature. Coopetition strategies are used by firms in response to increasing environment volatility (Baumard, 2009). Also, if a firm creates coopetitive relations,
others imitate this strategy. Similarly, market/industry regulations, customs, or
the environment can push market participants to launch coopetition strategies
(Mariani, 2007; Zineldin, 1998). Thus, industry reshaping is characterizes
coopetition: “managers must monitor and analyze environmental changes to assess
the need to engage in co-opetition, and if co-opetition is intensifying in the industry,
they should explicitly consider competitors when pursuing technology intensive
alliances” (Gnyawali, Park, 2009). The creation of a new industry structure, either
by including complementors or by reshaping the inter-firm relationships is theoretically a feature of coopetition. However, very few studies adopt industry level of
analysis, leaving this particular coopetition feature underexplored.
3. Theoretical lenses for coopetition studies
Emerging research threads typically exploit more established theoretical
frameworks. Drawing heavily on related fields might be a ‘Faustian bargain’, “if
a research community fails to estimate the consequences of becoming too bound
with related fields” (Agarwal, Hoetker, 2007). We examine in this section the
links of coopetition studies with theoretical frameworks addressing relationships
between firms. We extend prior research on interorganizational literature (Oliver,
Ebers, 1998) by analyzing how coopetition studies exploit theoretical references.
The distribution of underpinning theoretical perspectives, according to what
authors themselves claim is illustrated in Table 5.
Table 5. Coopetition theoretical background
Background
%
of articles
Game
Theory
Alliances
TCE
RBV
Networks
Competition
Evolutionary
Economics
Other
39%
83%
8%
35%
23%
24%
9%
3%
Note: The sum exceeds 100% as authors typically use more than one theoretical framework in their studies.
132
A closer analysis of the theoretical frameworks adopted by authors shows
that three perspectives are prevailing: alliances, competition and network theory.
We devote less attention to the resource based view and to game theory, as authors use those reference frameworks as auxiliary. More specifically resource
based rationale is mostly deployed to justify interorganizational relationship
formation and competitive advantage achievement. Game theory in turn provides a strong rational choice assumption to model collaboration between rivals
as the best strategic option.
3.1. Alliances perspective on coopetition
The alliances literature appears a prevailing background, scoring more than
83% of all studies. Alliances are defined as voluntary arrangements between
firms involving exchange, sharing or co-development of products, technologies
or services (Gulati, 1998). The body of alliances literature focuses on: the formation rationale, the dynamics, and the outcomes – specifically performance of
alliances and involved firms (Oliver, Ebers, 1998). Coopetition research can be
analyzed along those axes.
The literature provides several explanations for coopetition use by firms,
such as resource access, joint resource exploitation or competitive pressures.
There is evidence of positive relationship between coopetitive strategies in jointly
bidding for resources and financial performance (Robert, Marques, Le Roy, 2009).
An empirical study of the soft drink industry promotions shows that brand interdependence shapes market structure. Strong bottlers’ promotions allow to increase
a relative market share of the promoted brand, and at the same time to increase
the relative market share of strong bottlers at the expense of weak bottlers
(Meade, Hyman, Blank, 2009). Technology development in the LCD panel industry has proven to yield for collaborating competitors: Sony and Samsung.
Their combined market share has risen from challengers into market leaders
thanks to coopetition in R&D (Gnyawali, Park, 2011).
The view that coopetition focuses on the interplay between cooperative and
competitive actions of the value network members implies a dynamic approach
and encourages the study of development patterns. Two distinct epistemological
stances are widespread in management studies for elucidating dynamic phenomena: equilibrium, and evolutionary theories (Barney, 2001). Equilibrium stance
has been described in detail in Section 2.2.
The evolutionary approach to coopetition dynamics study adopts a different
view, aiming at identifying patterns of change. Extant research has unveiled
133
evolutionary patterns in coopetitive relationships: a) deliberate development
characterized by intentional rent seeking at both individual and collective level,
where coopetitor’s actions are relatively clear or even announced; b) emerging,
which refer to the upsurge of unilateral rent seeking behaviors within cooperative settings, mostly unplanned before the cooperation start.
Figure 4. Coopetition strategy development patterns
deliberate
(Robert, Marques, Le Roy, 2009; Meade,
Hyman, Blank, 2009)
coopetition patterns
emerging
(Mariani, 2007; Tidstrom, 2008; Czakon,
2009)
The deliberate thread of research suggests that coopetition is a strategy aiming at above average earnings. Typically those are referred to as competitive
advantage, yet studies provide evidence that focal actors performance improve
whenever competitors are able to generate positive interdependency.
The second patterns of coopetition between firms is emergence. Development paths identification uses longitudinal case studies unveiling series of
events. Under this assumption competition emerges between partners when cooperative relationships are subject to tensions, conflict or discrepancies. There is
evidence that cooperation can be imposed on otherwise competing actors by the
institutional environment, such as a policy maker (Mariani, 2007). Actors find
themselves strongly encouraged to forge a more cooperative value chain model,
where resources and information are better shared. Inversely, there is also evidence that power asymmetries and unfair value distribution among actors, may
lead to the emergence of competition within a cooperative framework, as shown
in two Finnish industries: transportation and natural product (Tidström, 2008),
and the Polish banking franchise system case (Czakon, 2009). The starting point
of those empirical studies is a cooperative settings, where opportunism, conflict
134
or instabilities emerge. So far deliberate and emerging paths have been scrutinized, both from competitive and cooperative starting points which allows for
a matrix typology of coopetitive behaviors along those two axes (table 6).
Table 6. Coopetition strategies empirical typology
Development path
Deliberate
Emerging
Starting point
Competitive
Ó collective competition against
others,
Ó collective resource acquisition
Ó induced cooperation,
Ó resource sharing,
Ó cooperative value chain
model
Cooperative
Ó competition for the “share in the
pie”,
Ó value sharing agreement,
Ó rent appropriation
Ó opportunism,
Ó conflict,
Ó unilateral rent seeking
Coopetition outcomes have mostly been scrutinized from firm-level perspective, with some notable interorganizational level exceptions. At firm level
there is growing evidence that coopetition fosters innovativeness and technology
development (Ritala, 2011). Also, firms entering coopetitive relationships may
expect positive market performance impact (Meade, Hyman, Bank, 2009;
Gnyawali, Park, 2009), as coordinated action provides an edge over other firms.
The impact of coopetition strategies on financial performance has been found
positive (Robert, Marques, Le Roy, 2009), yet other studies suggest that it is
limited or even absent depending on the number of coopetitors (Ritala, Hallikas,
Sissonen, 2008). While firm level impact is generally positive with some ambiguities, the collective level of analyses has been used much less frequently.
There is a gap in the literature referring to what we call ‘the coopetitive advantage’, a clear benefit arising from coopetitive relationships.
3.2. Competition perspective on coopetition
Competition frameworks come second in reference frequency with 24% of
papers. Competitive relationships exist when firms seek out the same limited
resources, or target the same market or customers (Gimeno, 2004). If competition arises from niche overlap, then coopetition studies examine the extent and
the impact of this overlap (Kotzab, Keller, 2003). Studies referring to competition perspective display a less structured use of competition theory, than those
135
which refer to alliances. Instead of mobilizing a theoretical framework researchers rather use some concepts, such as: competitive manoeuvring, competitive advantage, value chain, and exploit game theoretical explanations (Brandenburger,
Nalebuff, 1996; Rusko, 2011). Competitive manoeuvring explores how intercompetitor dynamics intensity impact rivals and firms performance. Similarly,
coopetition studies show that competitive advantage captures above average earnings
achieved by one firm against its competitors. A restrained use of competition may be
linked to their narrow focus on one type of relationship between firms. The rise of
alliances in management literature has primarily been a reaction to this argument.
Several studies show coopetition to be a successful strategy when applied
by firms to individual value chain activities: procurement, marketing or R&D.
Firms are involved in various interorganizational relationships with others along
the value chain in the same time (Solitander, Tidström, 2010). Bengtsson and
Kock (2000) have shown that firms tend to cooperate within activities that are
more far away from the customer, while they compete within activities closer to
the customer. Some studies explore: a) upstream coopetitive activities: R&D,
buying, processing of raw materials; b) downstream coopetitive activities – distribution, services marketing (Bengtsson, Kock, 2003; Rusko, 2011), and Mariani
(2007) adds the midstream aspect – production. Coopetition is simultaneously cooperating and competing, but on different activities (Bengtsson, Eriksson, Wincent,
2010). Two or more competitors can cooperate in product development or technology upgrades and at the same time compete in taking orders, attracting customers, or
expanding market share (Gnyawali, Madhavan, 2001; Tsai, 2002). The separation of
activities that are jointly carried out from those, which are individually operated by
firms contributes to alleviate tensions between collaborative and competitive logic.
Therefore a clear thread of research is opened for further coopetition studies, focusing on each and every activity along the value chain: logistics, operations, human
resource or even firm infrastructure.
3.3. Network perspective
The network approach is referred to in 23% of papers. A distinctive feature
of network studies is that firm performance is dependent on whom interacts with
whom (Håkansson, Snehota, 2006). In other words the organization-environment
interface can be scrutinized along network structures, positions and roles played
by actors involved. We combine it with evolutionary economics because both
approaches adopt a collective level of analysis and use either structural variables
or descriptive dynamics (Luo, 2005; Czakon, 2009; Bengtsson, Kock, 1999).
136
Interestingly network related issues, such as ecosystem competition or internal
dynamics leading to coopetition strategies have been relatively popular among
scholars (Chien, Peng, 2002; Gueguen, 2009; M’Chirgui, 2005).
A majority of the existing research is focused on the network perspective of
coopetition (41%), while dyadic relations are introduced in 7% of the total. Interestingly, a substantial number of papers has opted for the interorganizational
level of analysis, which is broader than a dyad, but still not a network. Those
studies account for roughly half the sample (49%). Networks are assumed to
consist of participants trying to achieve common benefits, whether they are internal value networks (organizations) or networks between different organizations (Solitander, Tidström, 2010). Firms in networks develop a set of relationships through connected activities, linked resources and related actors, all of
these elements being interconnected and interdependent. Researchers focus on
the roles of the participants involved in the relations (Table 7). Theoretical models suggest that coopetitors can have different roles within value networks. Yet,
there is very few empirical studies examining them in detail. Notably, recent
empirical evidence shows that third-party actors may have an enabling role in
coopetition (Castaldo et al., 2010). A more detailed identification of coopetition
network roles and their impact on the coopetition process is therefore necessary.
Table 7. Roles in Network Coopetition – some examples
Source
Chin, Chan, Lam, 2008
Luo, 2007
Rusko, 2011;
Bengtsson, Eriksson, Wincent, 2010
Chien, Peng, 2005
Luo, 2005
Identified roles
in network coopetition
contender, adapter, monoplayer, partner
estranger, contender, partner, and integrator
competitors, complementors, suppliers, and customers
direct & indirect partners, current or potential competitors
aggressive demander, silent implementer, ardent contributor, network captain
Future research should undertake the challenge of studying the relationship
between network structural variables: density, betweenness, centrality or size
and coopetition. Also roles played in networks have impact on coopetition which
still calls for empirical investigation.
A diverse theoretical background suggests that coopetition has not been
scrutinized in a consistent way. Consequently, the research findings might be
difficult to integrate into a coherent body of knowledge. Similarly to the
interorganizational dynamics literature (Bell, Den Ouden, Ziggers, 2006), com-
137
peting hypotheses arise from a rapidly growing body of empirical findings. Yet,
those findings are drawn from various paradigms and therefore cannot be directly confronted in terms of better explanatory power. There is a growing need to
develop a more coherent theory of coopetition.
4. Empirical research foci
Recently a substantial increase in coopetition empirical studies number can be
noticed, bringing rich insights into the phenomenon. Using frequency analysis we
identify coopetition research by sector, semi-sector and industry (Figure 5).
Figure 5. Semi-sectors in papers over time
manufacturing
information and communication (ICT)
human health and social work activities
financial and insurance activities
arts, entertainment and recreation
semi-sector
wholesale and retail trade; repair of motor vehicles and
motorcycles
years
1995-1999
administrative and support service activities
2000-2004
2005-2009
transportation and storage
2010-01.2011
construction
agriculture, forestry and fishing
professional, scientific and technical activities
public administration and defence; compulsory social security
education
0%
5% 10% 15% 20% 25% 30% 35%
percentage of the total number of papers
40%
Firstly, following C. Clark and J. Fourastie, we distinguish three economic
sectors (Wolfe, 1955): agriculture, manufacturing, and services. For detailed
analyses, the European statistical classification NACE Rev2 has been adopted: it
specifies of 21 types of economic activity, which are henceforth called semi-
138
sectors (Eurostat, 2011). 72 papers have been identified in accordance with our
classification. Additionally, the High-Tech semi-sector has been added, classified in accordance with the product method of the classification of High-Tech
goods by the OECD. A substantial attention is paid to services sector, which was
dealt with by a total of 58% of research. Industry comes next with 39% of published work, while agriculture is last, capturing a marginal attention of 3%. An
imbalance in the research also emerged inside each sector, where extremely different proportions fall to particular semi-sectors, and strong variations of academic interest can be observed over time.
In the case of the first sector, half of the papers concern forestry (50%) and
the second one fishing (50%). In the second sector, a vast majority was focused
on manufacturing (93%), with just 7% dealing with construction. Research in the
third sector was dominated by the ICT semi-sector (26%) and human health and
social work activities (14%). A scant number of analyses concern activities relating to education and widely defined public administration.
Similarly, the High-Tech semi-sector receives the most of academic attention in coopetition studies with a total of 33% of published work (Figure 6). Within
high-technology, the most frequent research subject was ICT economic activity (this
represents 46% of the total number of papers) and then aviation industry (13%).
Pharmaceutical, chemical and biotechnological spheres were less explored by scientists. Nevertheless, the High-Tech semi-sector with a result of 33% of the research
has been the most scientifically explored business activity so far.
Figure 6. High-Technology industries in papers
ICT
aviation
branch
NDA
defense
electronic
biotechnological
chemical
advanced materials
0%
10%
20%
30%
40%
50%
percentage of the total number of papers
139
To summarise, coopetition appears here as an industry related phenomenon, especially concerned with complex products, rapid technology change (Ritala, 2011),
intensive competition (Robert, Marques, Le Roy, 2009), and high uncertainty
(Gnyawali, Park, 2009). Yet, there is a visible failure to address more established
industries, or less dynamic sectors of activity. Researchers so far opted for sharply
observable empirical settings leaving a substantial part of businesses beyond focus.
Further research needs to take an increasingly detailed account of the context, and
by expanding the focus of interest onto industries so far under-researched.
5. Discussion and implications for further research
Our study shows that coopetition is perceived by scholars as a novel object of
study. The rising interest in tackling coopetition displays a surge in total number of
studies, and more recently of empirical papers. For a century this phenomenon labelled by managers did not attract academic attention, then for a decade since its introduction in the literature it has been of interest for a restricted community of researchers. Yet, if the current trend would be sustained, coopetition has all chances to go
beyond a community of researchers into a key issue in management (Figure 7).
Figure 7. Estimated increase in number of papers on coopetition
450
400
y = 0.006x4 - 0.097x3 + 0.712x2 - 0.274x + 0.508
R² = 0.998
350
no. of papers
300
250
200
150
R² = 0,9986
100
50
cumulative number of all the papers
empirical papers
conceptual papers
projected trend of the delta of all the papers
projected trend - empirical papers
projected trend - conceptual papers
Source: Based on M. Rogalski (2011).
140
year
2015
2014
2013
2012
2010
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
-50
2011
R² = 0,9821
0
Most of expected growth should be attributable to empirical papers, where
authors refer to clearly defined features of coopetition. The upsurge of empirical
work and relative decrease in theoretical frameworks suggest a widespread
adoption of a grounded theory approach. Fragmentation and growing detail in
studies comes into light. Further insights will strongly be dependent on whether
authors opt for convergent theoretical stances, and thus avoid the pitfall of paradigmatic tensions, or not.
Our study suggests that the prolific heterogeneity reported in empirical studies
has not unveiled any clear morphology of coopetition so far. While a common understanding of what coopetition is clearly emerges in the literature, researchers have
failed so far to provide a detailed picture of the different forms coopetition may take.
One reason might be that coopetition is more a principle for human action than
a strategy. Theory building subsequent effort shall take account of different
coopetition contingencies, which suggests that formulating a set of principles for
coopeting could be a valuable starting point (Chin, Chan and Lam 2007).
The other reason might be connected with the need to expand the empirical
body of literature. For this effort to be fruitful to the discipline, some basic lines
of further inquiry should be drawn. A few key issues remain out of coopetition
studies scope, although in related fields those concerns have been investigated.
This refers to the reasons: a) why a firm does have ambiguous and opposing
behaviors? b) what makes managers so willing to act for the sake of others in
complex and risky ways? and c) what makes competitive and cooperative rents
achievable in the long run? We organize further research agenda following Oliver and Ebers (1998) in order to address those questions in Table 8. It allows
both to extend current research threads by filling existing gaps, and develop
some research foci so far missing in the literature.
Table 8. Further research agenda antecedents – processes – outcomes
Theoretical
challenge
1
Antecedents
Research question
Observation needs
2
3
Need for an evaluation of factors
which make coopetition a first choice
strategy, or more likely to appear in
specific settings.
Need to observe life cycle dependencies of interfirm coopetitive relationships
What exogenous factors induce or
facilitate coopetition?
What endogenous factors or propensities facilitate coopetition?
How do factors impact coopetition
behaviors?
141
table 8 cont.
1
2
Process
What coordination mechanisms
contribute to coopetition success?
What is the morphology of
a coopetition process?
What capabilities need to be mobilized for successful coopetition?
Why is coopetition stable over time?
How does the coopetition process
unfold over time?
Outcomes
What advantages come from bringing together competition and collaboration?
How to operationalise rents arising
from coopetition?
How does coopetition impact performance, innovation or survival?
3
Need to identify and evaluate governance forms and leadership roles in
coopetition behaviors.
Need to identify the sequences of
events that intertwine collaboration
with competition, as patterns.
Need to explore the dynamics and
balance within coopetition.
Need to discern coopetition development patterns
Need to identify and evaluate rents
attributable to coopetition.
Need to observe the extent to which
coopetition is difficult to imitate, in
sustainable advantage terms.
Need to examine the strength and
shape of the relationship between
coopetition and dependent variables
However up-to-date empirical investigation of coopetition antecedents has
so far been fragmented and descriptive, it has also the merit to open ways for
more articulate and systematic scrutiny. Industry related antecedents have been
outlined only very recently (Ritala, 2011), while the size of firms has been
brought only a few years earlier (Gnyawali, Park, 2001). In other words some
exogenous factors inciting managers to adopt coopetition have been examined.
Empirical evidence shows the pivotal role of regulatory bodies in adopting
coopetition in health care (Barretta, 2008). Further research can be expected to
deeper analyze such exogenous factors as deregulation (Depeyre, Dumez, 2010),
globalization (Luo, 2004) or social networks. One of key questions within
coopetition research is whether the rationale for anti-competitive legislation is
still valid. The regulators have long been assuming so far that any form of collaboration between competitors is anti-competitive, collusive and harmful for the
customer (Vonortas, 2000). Coopetition as a revolutionary mindset (Brandenburger,
Nalebuff, 1996) and the value network concept bring positive-sum into the competitive game, which stands in opposition with the traditional view. This calls for
research on the impact of regulation and deregulation on coopetition adoption by
firms. Globalization in turn brings an increased competitive pressure, which may
induce firms into coopetition as a response to perceived external threats. Similarly, social networks can convey mimetic pressures between managers, which
otherwise would not be adopted. All in all, extant research suggests that
142
coopetition is adopted as a reaction to external stimuli, but the list of those exogenous factors is far from being exhaustive.
Also, endogenous factors require further attention. More commonly resource or capability contingencies have been examined in the perspective or
resource interdependency of firms (Mariani, 2007). There is a substantial literature explaining why competitors collaborate, but it still fails to address the question why all competitors do not collaborate? Other endogenous factors such as:
managerial propensities for individual or collective action, corporate level strategies, communication issues can shed more light on coopetition.
Compared to antecedents, the coopetition process is far better understood.
However, the majority of authors use alliance references. This raises a key question on how different coopetition is from alliances? The epistemological assumptions are clearly different. While competition, tension or instabilities in alliances
literature are considered as nuisance and source of concern (Das, Teng, 2000), within coopetition studies they are sources of success. When a tension appears in the
time span of a collaborative agreement in the alliances literature, this very coincidence of competition and collaboration consumes the whole time span of coopetition
studies. If alliances consider a single, ideal-type relationship at a time and observe its
morphology empirically, coopetition studies deliberately opt for a complex, dialectical and holistic approach to consider a real life phenomenon and draw propositions
from the business reality. Also, the alliance literature on competitor collaboration is
ambiguous. Indeed prior studies show high failure rates which might lead to zero or
even negative-sum games (Ritala, 2011). For instance, direct competitor alliances in
the airline industry studies provide negative evidence on effectiveness (Gimeno,
2004). In our view, the synchronicity, ontological and epistemological stances clearly differentiate coopetition from alliances.
Coopetition manifestations differ across industries, levels of analysis and scope
of study. Within such a variety a more systematic recognition of distinct types has
been missing so far. We believe that coopetition typologies constitute a promising
thread of study. Theoretical typologies, based for instance on the collaboration and
competition relative intensity would unveil a set of ideal types. Further empirical
scrutiny would confirm that firms adopt them, or fail to. The degree of intensity
has also the merit to open ways for a detailed examination of internal balance
and dynamics within coopetition.
Nevertheless the majority of literature adopted an interorganizational level
of analysis has become the preferred level of scrutiny. Inversely, firm or activity
level considerations are relatively under researched. This opens ways for inves-
143
tigating both coopetition for each and every single value chain activity firms
carry out, but also articulations between cooperation on some activities while
competing on others. Managerial ambidexterity is to be found at this level of
analysis, as a critical success factor for coopetition. Ambidexterity refers to the
capability of organizations to simultaneously manage opposite competencies.
Initially ambidexterity has been introduced to capture the capability to explore
and exploit knowledge (Raich et al., 2009). A very similar challenge emerges when
firms are engaged in coopetition, as they need to display the capability to manage
competitive and collaborative behaviors (Herzog, 2010). Ambidexterity raises the
issue of developing different or opposite capabilities, and to handle the tensions
arising from its difference. Therefore, studies which focus on capabilities required to
successfully coopete may shed light on factors fostering coopetition process.
Similarly, network level or industry level empirical studies are still few.
This level of analysis takes account of market structure contingencies, ecosystems competition, collective growth strategies, etc. Therefore coopetitive dynamics
for competing ecosystems yield promise of providing important insights. Networks
imply various roles played by firms. The literature has very seldom used structural
variables to explain coopetition or even to identify it. A clear methodological gap
emerges here as far as the use of social network analysis techniques is concerned.
Finally, coopetition outcomes have so far been examined for a limited number of variables. Innovation performance (Quintara-Garcia, Benavides-Velasco,
1996), market performance (Meade, Hyman, Blank, 2009) and financial performance (Ritala, Hallikas, Sissonen, 2008) have been frequently used as outcome
variables to demonstrate, that coopetition strategies are beneficial for the firm.
However, those studies provide ambiguous results suggesting different impact
depending on the number of coopetitors. Empirical evidence unveils mediating
variables between coopetition strategies and financial performance, such as market
learning (Luo, Slotegraaf, Pan, 2006) or efficient consumer response (Kotzab, Keller, 2003). Prior findings encourages replication or larger sample studies in order to
confirm the positive impact and explore boundary conditions, such as the size of
firms involved, their heterogeneity, technology life cycles, etc. Also, it seems useful
to explore how coopetition can impact outcomes of interest at different levels of
management: market share, risk or entry for functional strategists; performance for
business unit managers; growth or profitability for corporate level managers. The
paradoxical nature of coopetition, coupled with ambiguous results of performance
studies suggests, that the role of coopetition may be different across the firm. Further
on, we can test the hypothesis that for some activities coopetition is advisable, while
for others it offers less interesting results (Bengtsson, Kock, 2000).
144
Similarly, there is few evidence on how coopetition is beneficial for the
whole value network. Firm level studies provide insights into how one coopetitor
can take advantage of collaborative rents. Thus, researchers address the question
why one firm should use coopetition, but fail to address the question why others
should follow. In other words, beyond sharp examples of focal firm success, more
attention is required to show how coopetition generates common benefits available
to all coopetitors. Such studies would aim at isolating the value generated by
coopetition for businesses, as a parallel to competitive advantage (Barney, 2001)
or collaborative advantage (Dyer, Singh, 1998). If the interplay between competition and collaboration is constitutive for coopetition, then we can expect that the
coopetition rent is more than the sum of competitive advantage and collaborative
rents. Importantly coopetition has so far been considered as a behaviour of choice,
and a difficult one. Should it also be difficult to imitate, then coopetitive rents
would have their very high rank in strategy literature.
Conclusions
The founding achievement of coopetition research community is much
more than coining a term for a complex phenomenon. Researchers agree on its
key features, and have identified some development paths: emergent and deliberate. Some initial typologies have followed. Also, coopetition has been presented as
optimal, equilibrium strategy, which facilitates the study of under-performing competitive or cooperative strategies. A systematic scrutiny of the literature sheds light
on several gaps. A first general issue in coopetition research is its morphology –
there is need to expand empirical research beyond high-tech industries, and examine it more in detail along the value chain activities. Secondly, further research should address the stability issue – what is the glue holding opposed or
unbalanced competition and collaboration together? Exogenous pressures, separation of collaborative and competitive activities, or organizational ambidexterity hold promise of better explanations than currently available. This thread of
research may shed additional light both on coopetition antecedents and on the
coopetition process. Thirdly, coopetition outcomes need to be brought into light –
what is in it for participating firms? Beyond innovation, market and financial
performance other variables such as growth, survival or speed to marked can be
explored. Future research may also isolate the coopetitive advantage, both as
common and appropriable benefit.
145
Coopetition remains of high managerial relevance and draws rapidly growing academic attention. However, it is still more of a recognized concept than
a theory, which can mostly be attributed to the early stage of research. For example,
alliances investigation started in early 1980's and brought in a consistent and seminal
body of research in late 1990's and so forth. By analogy, coopetition would need
about 10 years more to address empirical issues with a consistent theory.
Providing it with theoretical grounds or at least comparatively testing available theories for heterogeneous empirical settings may be viewed as a major
challenge. Rent seeking behavior seems promising, as it provides explanations
for the rationale of simultaneous competitive and cooperative behaviors, for
unilateral rent seeking and collective action, as well as for adaptive actions. Indeed coopetition has brought three concepts into strategy research which have
long been absent from it or have been considered separately: 1) value maximization in an interorganizational context, 2) rent appropriation as a simultaneous
concern, and 3) emergent adaptation to changing operation's circumstances. Given the intellectual challenge coopetition brings to researchers, and the high managerial relevance of the topic further research holds promise of gathering increasing audiences.
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