2015 Proceedings of PICMET '15: Management of the Technology Age
Cooperation Models as Success Factor for Interdisciplinary,
Inter-Organizational Research and Development in the Automotive Industry
1
Eva Maria Grochowski1, Peter Ohlhausen2
University of Stuttgart, Graduate School of Excellence advanced Manufacturing Engineering (GSaME)
and Institute of Human Factors and Technology Management (IAT), Stuttgart, Germany
2
ESB Business School, Innovation and Technology Management, Reutlingen, Germany
and Fraunhofer IAO, Stuttgart, Germany
Abstract--The automotive industry faces three major challenges – shortage of fossil fuels, politics of global warming and
rising competition from new markets. In order to remain competitive companies have to develop more efficient and alternative fuel vehicles that meet the individual requirements of the
customers. Functional integration combined with new technologies and materials are the key to stable success in this industry.
The sustaining upward trend to system innovations within the
last ten years confirms this. The development of complex products like automobiles claim skills of various disciplines e.g. engineering, chemistry. Furthermore, these skills are spread all over
the supply chain. Hence the only way to stay successful in the
automotive industry is cooperation and collaborative innovation.
Interdisciplinary and interorganizational development has high
demands on cooperation models especially in the automotive
industry. In this case study cooperation models are analyzed and
evaluated according to their applicability to interdisciplinary,
interorganizational development projects in the automotive
industry. Following, the research campus ARENA2036 is analyzed. ARENA2036 is an interdisciplinary, interorganizational
development project housing automobile manufacturers, suppliers, research establishments and university institutes. Finally,
based on interviews with the partners and the precede analyses
of cooperation models, suggestions for implementation are given
to ARENA2036.
I. INTRODUCTION
Automobile manufacturers and the entire value chain in
the automotive industry are subject to major structural changes due to changing markets and competition. In the future, the
share of labor will shift from automobile manufacturers towards suppliers. This is caused by an increasing variety of
models, options and technologies demanded by the market
[30]. The proportion of the manufacturer's own performance
in product development will likewise decrease from currently
60 to 47 percent by 2025 [31]. In contrast the research and
development (R&D) share of suppliers as well as contract
researchers will increase from approximately 32 to 36 percent
respectively from nine to 17 percent [31]. Regarding these
facts strategic partnerships become more and more important
to ensure early access to new technologies, new markets and
new business models [2, 35]. Especially important for future
success of a company is R&D since “ … the development
system is the very backbone of an efficient and effective
automotive manufacturer.[27] “. The challenges arising from
the increasing technical complexity of automobiles and shortening product cycles can only be managed by interdiscipli-
nary, interorganizational project teams. These interdisciplinary projects more and more claim for network structures in
cooperative activities [17].
The industry knows a lot of different cooperation approaches and models. The question is the applicability and
transferability of individual features of cooperation models to
the particularities of interdisciplinary and interorganizational
development projects in the automotive industry. Therefore
advantages and disadvantages of different cooperation forms
have to be taken into account.
The aim of this work is to analyze and evaluate the known
cooperation models regarding the requirements of cooperative R&D in the automotive industry. The analysis and evaluation is based on literature and expert interviews. The results
are applied to a case example “Active Research Environment
for the Next Generation of Automobiles” (ARENA2036).
In a first step the cooperation models stated in the literature are limited by a selection based on the requirements of
general R&D projects in the automotive industry. Cooperation models that fulfill at least half of the criteria are considered for further evaluation.
In a second step evaluation criteria are developed and the
cooperation models are scored by a utility analysis. According to the result of the utility analysis ARENA2036 is classified. For the requirements of the ARENA2036 partners that
are not sufficiently met recommendations are given based on
the preliminary literature analysis.
II. CASE EXAMPLE - ARENA2036
ARENA2036 is a new cooperation that started July 2013
after it won the competition for “public-private partnerships
for innovation” of the Federal Ministry of Education and
Research. The cooperation focusses on the future automobile
especially on function-integrated lightweight construction
and sustainable, flexible and adaptable production.
The manner of collaborative R&D in ARENA2036 is unprecedented. Divers partners from research establishments,
universities and the industry, including small and medium
sized companies do research on innovative future topics concerning manufacturing and lightweight construction under
one single roof. The design of the cooperation model of
ARENA2036 matches largely the characteristics of a research
campus.
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2015 Proceedings of PICMET '15: Management of the Technology Age
TABLE 1: OVERVIEW OF STRUCTURES OF COOPERATION AND THEIR FOCUS
Cooperation models
Focus
Source
HARZER [8]
Contractual cooperation, cooperative, strategic alliance, joint venture, franchise, incorporated company, value system
Strength of commitment and purpose of the
cooperation
KÖHNE [13]
Virtual company, sub-contracting, agency, licensing, franchise, capital
venture, contractual cooperation, civil law association
Cooperation structures of strategic company networks
MORSCHETT [19]
Non-contractual cooperation, contractual cooperation, equity commitment
Formal aspects of cooperation
STRIETZEL [32]
Licensing, franchise, joint venture, contract manufacture, strategic alliance,
minority holding, subsidiary, merger
Market entry strategies
THOMMEN [34]
Participation, consortia, syndicate, community of interest, joint venture,
strategic alliance, group
Strength of commitment of the cooperation
ZILLIG [36]
Process organization, in-/outsourcing, joint venture, strategic alliance, project organization, network, virtual company
Process oriented and structural organization
For cooperation research the research campus is an interesting field. Collaborative innovation of competitors, suppliers, customers, private research establishments and university
institutes has high potential but also brings up new challenges
for cooperation. At this very early stage of the cooperation
the recognition of the potentials and challenges is inevitable
in order to develop action alternatives. Moreover, such cooperation bring basic research, applied research and final application closer together. This enables for faster innovation and
technological leadership.
The ARENA2036 partners start a new epoch of cooperative work with this kind of partnership. From basic to applied
researchers of the University of Stuttgart to non-university
research establishments to industry partners from small to
large companies they follow a strategic path from research to
the final product together in one research factory.
intensity of cooperation, legal aspects and economic integration. According to this, respective specifications of cooperation can only be defined by configured approaches. Hence
model based approaches cannot be evaluated for their adequacy for R&D cooperation in the automotive industry. They
do not imply enough information about the cooperation model that is necessary to analyze eligibility for interdisciplinary,
interorganizational R&D. In the following cooperation model
means a configured approach.
The following table categorizes the identified cooperation
models in model based and configured approaches. As explained above only configured approaches will be considered
for the evaluation.
TABLE 2: CATEGORIZED COOPERATION MODELS
Model based approaches
Configured approaches
Cluster


Working partnership

Co-opetition

Industry cluster

Community of interest

Research campus

Outsourcing

Research cluster

Simultaneous engineering

Franchise

Virtual company

Joint venture

Consortia

Licensing

Strategic alliance

Business association
III. THEORETICAL BACKGROUND
A. Cooperation models
Cooperation is defined as an organized economic structure
of, according to certain criteria selected, enterprises that are
legally independent. Based on a negotiated and defined
common purpose subtasks are determined for each party by
the involved parties [1].
There are various cooperation models that support the
achievement of the common purpose of a cooperation. Analyzing numerous scientific sources written by different authors, diverse structures of cooperation can be observed. The
following table gives an overview of the most common structures and the focus within cooperation research.
It is not possible to explicitly define each cooperation
model based on the characteristics described in the literature.
Furthermore, the complexity of R&D projects in the automotive industry forces to consider more than a single structure
characteristic in order to identify eligible cooperation models.
Analyzing the descriptions and foci in the literature model
based approaches and configured approaches.
Model based approaches describe a general structure
that frames and structures the cooperative work of the parties.
Based on these approaches, configured approaches accomplish the frame and structure by form and content. This allows conclusions to be drawn as to strength of commitment,
B. Description of selected cooperation models
The selection for detailed descriptions of the cooperation
models bases on the final results of the evaluation analysis.
Only the three most appropriate approaches will be described
because of the limited space.
Industry clusters consist of regional companies that belong to the same industry. Cooperation in a regional context
is supposed to support industry growth and competitiveness.
Nevertheless different industry clusters show different characteristics. Industry clusters that consist of companies that
produce similar products within the same tier of the supply
chain are driven by competitive incentives. These incentives
foster product diversification and therefore an innovation race
in the industry. Vertical industry clusters consist of a network
of customers, suppliers or service providers. The cooperation
model benefits e.g. coordination efforts within collaborative
R&D by the regional environment [3]. The essential characteristics are shown in the Fig. 1.
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2015 Proceedings of PICMET '15: Management of the Technology Age
Attribute
Direction
Expansion
Liability
Financial integration
Duration
Time horizon
Target identity
Number of partners
Company size
Substantive limits
Function link
Characteristics
Horizontal
Vertical
Diagonal
Local
Regional
National
Global
Agreement
Rules
Contract
No equity participation
Equity participation
limited
Unlimited
Long-term
Medium-term
Short-term
Redistributive
Reciprocal
2
3-10
> 10
Inhomogeneous
Homogeneous
Limited
Unlimited
Merger
Vote
Figure 1: Characteristics of industry clusters [26]
Attribute
Direction
Expansion
Liability
Financial integration
Duration
Time horizon
Target identity
Number of partners
Company size
Substantive limits
Function link
Characteristics
Horizontal
Vertical
Diagonal
Local
Regional
National
Local
Agreement
Rules
Contract
No equity participation
Equity participation
limited
Unlimited
Long-term
Medium-term
Short-term
Redistributive
Reciprocal
2
3-10
> 10
Inhomogeneous
Homogeneous
Limited
Unlimited
Merger
Vote
Figure 2: Characteristics of research campus
Attribute
Direction
Expansion
Liability
Financial integration
Duration
Time horizon
Target identity
Number of partners
Company size
Substantive limits
Function link
Characteristics
Horizontal
Vertical
Diagonal
Local
Regional
National
Local
Agreement
Rules
Contract
No equity participation
Equity participation
limited
Unlimited
Long-term
Medium-term
Short-term
Redistributive
Reciprocal
2
3-10
> 10
Inhomogeneous
Homogeneous
Limited
Unlimited
Merger
Vote
Figure 3: Characteristics of strategic alliances
According to a study of the Harvard Business School regions with successful industry clusters show higher economic
growth, more employment, higher growth of wages, more
entrepreneurship and more intellectual property like patents
as other regions [29].
Research campus is a new cooperation model that unifies
industry and research in order to elaborate complex long-term
R&D questions beyond single technological aspects. The
combination of companies, non-university research establishments and at least one university allow the handling of
complex topics with a high research risk and the potential of
radical innovations [4]. Regarding the characteristics of a
research campus it is comparable to a cluster approach. The
crucial difference is that in a research campus the collaborative work is done at one place that offers e.g. offices or laboratories. The essential characteristics are shown in the Fig. 2.
Strategic alliances consist of economically independent
companies along the same supply level. Such cooperation can
be national or international [14]. The collaborative work is
focuses on agreed strategic business fields and lasts at least in
the medium-term. Companies that have complementary
strengths and resources usually build strategic alliances in
order to improve their competitiveness and market position.
The pursuit of common goals leads to a trustful cooperation.
The essential characteristics are shown in the following Fig.
3.
C. Motives for cooperation in R&D
In order to select cooperation models that are relevant for
R&D in the automotive industry, essential aims of R&D
cooperation are illustrated. It has to be considered that several
objectives compete with each other and cannot be clearly
defined or determined [18].
The most important motive for R&D-cooperation is to
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2015 Proceedings of PICMET '15: Management of the Technology Age
achieve synergy effects by exploiting complementary technical know-how [23].
Controlled exchange of experience and knowledge leads
to greater expertise and generally to an optimal result [24].
Knowledge and know-how extension in order to enhance
quality and competitiveness are as well intentions for cooperation. Besides cost reduction and shorter R&D-periods, higher quality can also be quoted as motive for cooperation.
Capital intensive R&D projects often cannot be realized
by a single company, since in-house resources might not be
sufficient [33]. Capacity extension therefore is the second
motivation for collaboration [21]. The aim is to use external
machinery, human resources or financial means.
The bundling of resources can also affect cost of R&D
projects. Possible cost reduction can be realized by a learning
curve concerning know-how, economies of scope and economies of scale [16]. Shared R&D cost in the partnership also
result in cost reduction for each party compared to discrete
R&D projects.
Increasingly shorter product life cycles and concurrent
rise of complexity of the products force to accelerate R&D
processes. Besides bundling of resources a trust base accomplished by contractual agreements can promote time reduction [20].
Despite contractual agreements there is a risk of opportunistic behavior and abuse of confidence by any party. In contrast risks arising from R&D are reduced since they are
shared within the cooperation.
Facilitation of market entry is the final motive for cooperation. The relationships to the cooperation partners might
support market entry nationally as well as internationally.
IV. METHODOLOGY
A. Analysis of expert interviews
Studies about cooperation like ARENA2036 do not exist
so far. The focus of this study is to gain new insights rather
than to confirm existing knowledge. Therefore guideline
interviews are used to collect data. The aim is to receive an
overview from all perspectives of the ARENA2036 partners.
The interviewees were selected from all of the three subprojects. Table 3 shows the distribution.
TABLE 3: SAMPLE STRUCTURE
Number of interviewees
Automobile manufacturer and Suppliers
5
Research organizations
6
ARENA2036 Management
1
Sum
12
The interviews are analyzed by the qualitative content
analysis [6]. The interview text is systematically categorized
according to the research questions. The research questions
are overall purpose of the cooperation, motivation of the
partners, legal aspects, strategic focus, chances and risks of
the cooperation. Based on these categories the extracted text
blocks are analyzed. The requirements for interdisciplinary,
interoganizational R&D are evolved.
B. Utility analysis
The selection of the relevant cooperation models is realized by a utility analysis [9]. Evaluation criteria are developed by preliminary theoretical considerations and the results
of the interviews. The scores for each cooperation model and
evaluation criteria are created by the literature analysis combined with the results of the interviews. The weightings for
each evaluation criteria are results from the expert interviews.
After a pairwise comparison, the weightings are tested for
consistency by an analytical hierarchy method approach [12].
V. APPLICABILITY OF COOPERATION MODELS
A. Applicable cooperation models according to research and
development requirements
The evaluation process selects in a first step cooperation
models that fulfill the motives for engaging in R&D cooperation, in order to obtain a shortlist.
All motives are categorized by “fulfilled” (+), “might be,
but does not have to be fulfilled“ (0) and not fulfilled (-) for
each cooperation model. Only cooperation models that fulfill
at least half of the motives will be observed for interdisciplinary, interorganizational R&D in the automotive industry.
The categorization bases on the literature descriptions of the
cooperation models. Franchise for example does not produce
synergy effects. Franchising is basically a distribution concept that builds on a contractual agreement between the franchisee and the franchisor. The franchisee assigns fixed payments to the franchisor in order to sell his product under
certain conditions [4]. There is no creation of additional value
that is caused by the cooperation. All other below mentioned
cooperation models, except business association, produce
synergy effects because of the complementary know-how and
extension of capacity. Together e.g. they can create interdisciplinary solutions that were not possible without the cooperation.
In the table 4 the selected cooperation models are highlighted.
For further analysis research cluster will not be considered
even though the model meets the requirements for R&Dprojects. It is largely identical to industry cluster regarding
cooperative characteristics but does not sufficiently include
industry partners for cooperation in the automotive industry.
Therefore, industry cluster, research campus, joint venture,
licensing and strategic alliance will be considered in the further analysis.
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2015 Proceedings of PICMET '15: Management of the Technology Age
TABLE 4: SELECTED COOPERATION MODELS
Working partnership
Industry cluster
Research campus
Research cluster
Franchise
Joint venture
Consortia
Licensing
Strategic alliance
Business association
synergy
effects
capacity
extension
gain of
know-how
cost
reduction
time
reduction
risk
reduction
market
entry
+
+
+
+
+
+
+
+
-
0
+
+
+
+
0
0
+
-
+
+
+
+
+
+
+
+
-
0
+
+
+
+
0
-
+
0
0
0
+
0
+
+
0
+
+
+
+
0
+
+
+
+
+
+
0
0
+
0
+
0
-
B. Evaluation criteria for interdisciplinary, interorganizational R&D in the automotive industry
The relevant cooperation models selected in chapter IV A.
will be evaluated quantitatively by a utility analysis regarding
interdisciplinary, interorganizational R&D in the automotive
industry.
The evaluation criteria are developed from diverse influencing variables [9]. All of them are premised on the requirements on cooperation models observed in the literature
and the motives for engaging in a cooperation. Moreover the
practical actuality of ARENA2036 is already at this stage
taken into account.
Intellectual property issues will not be considered, since it
is such a large research area that is beyond the scope of this
paper.
For the definition and classification of the evaluation criteria categories are built. The evaluation criteria are formulated as objectives and assigned to a matching category. Cultural aspects were not considered since they are beyond the
scope of this paper. Table 5 introduces the categories and
evaluation criteria.
 High investments by the partners (1): High investments
are positive for R&D projects because they rely on free
resources for innovative results.
 High public funding (2): Public funding reduces the
financial risk for the partners and facilitates the entrance
of new partners, e.g. SMEs. Besides, public funded pro-






jects can improve the reputation of the involved companies.
Low financial risk for the partners (3): Low financial
risk means comparatively low financial obligations such
as non-liability agreements of the partners. In general absolutely clear formulated responsibilities lower unexpected financial risk.
Possible exit before contractual agreement (4): Exiting
the cooperation without any legal or financial risk should
be possible. Therefore, a form of organization should be
chosen that allows early exit.
Low consequences for early exit (5): In order to guarantee flexibility within the cooperation contractual clauses
that complicate early exit should be avoided.
High infrastructural investments (6): Access to the
infrastructural resources of the partners benefits
knowledge transfer and interorganizational team work.
Simple contract agreements (7): Cooperation contracts
should be simple and open. The base of a successful collaboration is mutual trust of the partners.
High knowledge and know-how transfer (8):
Knowledge and know-how transfer allows intense cooperation between different disciplines. That allows e.g.
functional integrated solutions that might cause competitive advantage.
TABLE 5: CATEGORIES AND EVALUATION CRITERIA
Evaluation criteria
(1)
High investments by the partners
(2)
High public funding
(3)
Low financial risk for the partners
Exit barriers
(4)
Possible exit before contractual agreement
(5)
Low consequences for early exit
Entry barriers
(6)
High infrastructural investments
Legal aspects
(7)
Simple contract agreements
(8)
High knowledge and know-how exchange
Local aspects [22]
(9)
Proximity of the partners
Temporal aspects
(10)
Possible long-term arrangements
Communication [22]
(11)
Short communication paths
(12)
Low institutionalization of communication
R&D in the automotive industry
(13)
Simple acquisition of new partners
(14)
High correlation of research and industry
(15)
High focus on R&D and technology leadership
(16)
Flexible adaption of tasks
Category
Financial aspects
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2015 Proceedings of PICMET '15: Management of the Technology Age
 Proximity of the partners (9): Proximity helps to develop a common spirit and eases knowledge transfer. Moreover common resources can be easier accessed by all partners.
 Possible long-term arrangements (10): Long-term relations between R&D partners should be promoted so that
collaboratively created know-how can be obtained and
used further on.
 Short communication paths (11): Short communication
paths result in accelerated knowledge transfer. Technical
or bureaucratic barriers should be avoided. A common ITinfrastructure will also improve communication.
 Low institutionalization of communication (12): Low
institutionalization of communication means that meetings
for example can be organized fast and flexible. This kind
of communication needs a flat hierarchy within the cooperation.
 Simple acquisition of new partners (13): The integration of new partners along the value chain should be flexible and easy. The integration of new partners increases
the knowledge and know-how within the cooperation.
 High correlation of research and industry (14): The
collaboration of research and industry makes different
views and approaches possible. This makes innovative solutions more probable.
 High focus on R&D and technology leadership (15): In
order to promote creativity and motivation of the partners
the cooperation should focus on a leadership position
within the R&D field.
 Flexible adaption of tasks (16): Especially long-term
cooperation should be flexible in order to react on a
changing environment and future trends.
C. Weighting of the evaluation criteria
The weighting of the evaluation criteria is based on the
analytic hierarchy process and accomplished by a pairwise
comparison of the criteria [25]. A scoring system determines
which evaluation criteria is “important” (2), “equal” (1) or
“not important” (0). After scoring the criteria the weightings
are verified by a consistency factor regarding logic and quality of the weighting. As a result conflicts of the individual
evaluations can be prevented. The intended consistence of 85
% is achieved for this evaluation system. Therefore it meets
the scientific requirements for objective consistency [5, 28].
The following tables show on the left side the example of the
weightings of one expert and on the right side the consistency
of his weightings. The numbers in the left column and the top
line refer to the evaluation criteria explained and numbered in
the preliminary chapter B.
Based on the arithmetic average (two decimal digits) the
weighting factors of the evaluation criteria can be determined. The result of the pairwise comparison is achieved by
the ratio of the totalized scores within the lines and the total
sum of scores. The following table shows the average
weighting of five of the twelve experts. Only five were chosen since only these persons have insights to the total process
of cooperation.
High focus on R&D and technology leadership (11.56 %),
high knowledge and know-how exchange (10.94 %) and high
correlation of research and industry (10.31 %) received the
highest weightings. The weightings for short communication
paths (9.58 %) and proximity of the partners (9.69 %) are
slightly lower. These five criteria cover more than half (52.08
%) of the total weighting. High investments by the partners
(3.33 %), high public funding (3.13 %) and possible exit
before contractual agreement (2.08 %) show low weightings.
Besides these three criteria another five criteria are
weighted slightly below or above 5 %. Consequently half of
the criteria (8 out of 16) cover only about one third (28.65 %)
of the total weighting. The remaining three criteria are low
financial risk for the partners (6.36 %), high infrastructural
investments (6.46), flexible adaption of tasks (6.46 %).
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14
15
16
SUM
CONSISTENCE FACTOR
1
1
1
0
1
1
1
1
1
1
0
1
1
1
1
13
86.67%
1
1
1
0
0
1
1
1
1
1
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
0
1
1
1
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
1
1
0
1
1
0
1
1
1
1
1
1
1
1
0
1
1
0
0
1
13
14
15
11
14
14
14
14
14
13
12
14
13
12
12
212
86.67%
93.33%
100.00%
73.33%
93.33%
93.33%
93.33%
93.33%
93.33%
86.67%
80.00%
93.33%
86.67%
80.00%
80.00%
88.33%
1
1 1
1 1
0 0
1 1
1 1
1 1
1 1
1 1
1 1
0 0
1 1
1 1
1 1
1 1
10 10
1
0
1
1
1
1
1
1
1
1
1
1
1
6
1
1 1
1 0
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
1 1
15 13
1
1
1
1
1
0
1
1
1
1
7
1
1
1
1
1
1
1
1
1
14
1
1
1
1
1
1
0
1
1
1
0
1
1
1
1
1
5
1
1
1
1
1
0
12
1
1
1
0
1
3
1
1
1
1
8
1
1
0
16
0
0
3
1
2
8
RANK
13
0
12
1
1
11
2
2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Rank
9
0
2
2
1
1
10
0
0
1
1
0
10
10
6
15
13
7
14
1
5
12
3
8
16
3
2
8
8
2
2
0
2
2
1
4.58%
4.58%
7.92%
0.83%
3.33%
7.08%
2.08%
12.08%
9.58%
3.75%
10.42%
5.83%
0.42%
10.42%
11.25%
5.83%
100.00%
7
0
1
0
0
2
2
0
11
11
19
2
8
17
5
29
23
9
25
14
1
25
27
14
240
6
0
0
2
0
0
2
0
2
2
1
2
1
1
1
2
1
0
0
0
0
0
0
0
1
0
0
1
0
0
1
5
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
2
0
0
1
0
0
4
2
2
2
2
2
0
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
3
0
2
2
0
2
1
0
2
2
0
1
1
2
0
0
1
0
2
2
0
2
Consitency evaluation
"consistent" (1)
"inconsistent" (0)
2
2
1
2
2
2
2
2
0
2
2
2
0
0
0
0
0
0
0
2
1
0
RANK
2
2
2
2
2
2
2
2
0
2
2
2
2
2
2
0
0
2
0
2
2
WEIGHTING
0
1
0
0
2
2
0
2
0
0
2
2
0
16
2
0
1
2
0
2
2
0
2
1
0
2
2
2
0
0
0
0
0
0
0
2
SUM
0
0
0
0
0
0
0
15
2
2
2
0
1
2
14
8
0
0
2
0
0
13
7
1
1
1
0
12
6
2
2
2
11
5
0
0
9
4
1
10
3
1
2
0
1
2
0
2
2
0
2
1
0
2
2
2
2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
1
TABLE 6: EXAMPLE FOR EXPERT WEIGHTING AND CONSISTENCY EVALUATION OF THE EVALUATION CRITERIA
Weighting of the line
relative to the column
“important” (2)
“equal” (1) or
“not important” (0)
10
10
6
15
13
7
14
1
5
12
3
8
16
3
2
8
2015 Proceedings of PICMET '15: Management of the Technology Age
High investments by the partners
1.50 0.50 1.50 0.25 0.25 1.00 0.00 0.00 1.00 0.00 1.00 1.00 0.00 0.00 0.00 8.00
3.33%
0.50 1.00 1.25 0.50 0.75 0.00 0.00 1.00 0.00 1.25 1.00 0.00 0.00 0.25 8.00
3.33%
High public funding
0.50
Low financial risk for the partners
1.50 1.50
Possible exit before contractual agreement
0.50 1.00 0.00
14
14
2.00 1.00 1.00 1.50 0.00 0.00 1.25 0.50 1.50 1.25 0.00 0.00 1.75 14.75
6.15%
7
0.75 0.50 0.25 0.00 0.00 0.00 0.00 1.00 1.00 0.00 0.00 0.25 5.25
2.19%
16
0.50 0.75 0.00 0.00 0.00 0.00 1.00 1.00 0.50 0.50 0.00 9.00
3.75%
13
1.50 0.75 0.25 1.50 0.25 1.50 1.00 0.00 0.00 1.50 15.50
6.46%
6
0.25 0.00 0.75 0.25 1.00 2.00 0.00 0.00 0.25 10.75
4.48%
10
1.50 2.00 1.50 2.00 1.75 1.50 0.75 2.00 26.00
10.83%
2
2.00 1.50 1.00 2.00 0.50 0.25 1.75 23.25
9.69%
4
0.25 1.00 1.50 0.25 0.00 0.75 12.25
5.10%
9
1.75 1.75 0.75 0.75 2.00 22.75
9.48%
5
1.00 0.00 0.00 1.00 10.00
4.17%
11
0.25 0.25 1.00 9.25
3.85%
12
0.75 1.00 24.00
10.00%
3
2.00 26.75
11.15%
1
14.50
6.04%
8
240
100.00%
Low consequences for early exit
1.75 0.75 1.00 1.25
High infrastructural investments
1.75 1.50 1.00 1.50 1.50
Simple contract agreements
1.00 1.25 0.50 1.75 1.25 0.50
High knowledge and know-how exchange
2.00 2.00 2.00 2.00 2.00 1.25 1.75
Proximity of the partners
2.00 2.00 2.00 2.00 2.00 1.75 2.00 0.50
Possible long-term arrangements
1.00 1.00 0.75 2.00 2.00 0.50 1.25 0.00 0.00
Short communication paths
2.00 2.00 1.50 2.00 2.00 1.75 1.75 0.50 0.50 1.75
Low institutionalization of communication
1.00 0.75 0.50 1.00 1.00 0.50 1.00 0.00 1.00 1.00 0.25
Simple acquisition of new partners
1.00 1.00 0.75 1.00 1.00 1.00 0.00 0.25 0.00 0.50 0.25 1.00
High correlation of research and industry
2.00 2.00 2.00 2.00 1.50 2.00 2.00 0.50 1.50 1.75 1.25 2.00 1.75
High focus on R&D and technology leadership
2.00 2.00 2.00 2.00 1.50 2.00 2.00 1.25 1.75 2.00 1.25 2.00 1.75 1.25
Flexible adaption of tasks
2.00 1.75 0.25 1.75 2.00 0.50 1.75 0.00 0.25 1.25 0.00 1.00 1.00 1.00 0.00
D. Results of the utility analysis
The utility analysis shows that the cooperation model research campus fulfills largely the evaluation criteria respectively the requirements (86 %). The criteria are scored 4.3 of
5 on average. The industry cluster also shows a high degree
of performance (72 %). In comparison the cooperation mod-
els joint venture, strategic alliance and licensing meet fewer
requirements for interdisciplinary, interoganizational R&D.
Joint ventures meet slightly more than half of the criteria (51
%) whereas strategic alliance (48 %) and licensing (48 %)
marginally reach 50 %. Table 5 presents the detailed results.
TABLE 8: RESULTS OF THE UTILITY ANALYSIS
Research campus
Research cluster
Licensing
Joint venture
Strategic alliance
Weighting factor
Calculation of total utility
Research campus
Research cluster
Licensing
Joint venture
Strategic alliance
Evaluation of the alternatives
Comparison of alternatives - criterium … met:
1 = not sufficient
2 = sufficient
3 = satisfying
4 = good
5 = very good
RANK
WEIGHTING
SUMME
Flexible adaption of tasks
High focus on R&D and technology leadership
High correlation of research and industry
Simple acquisition of new partners
Low institutionalization of communication
Short communication paths
Possible long-term arrangements
Proximity of the partners
High knowledge and know-how exchange
Simple contract agreements
High infrastructural investments
Low consequences for early exit
Possible exit before contractual agreement
Low financial risk for the partners
Weighting of the line relative to the column
“important” (2)
“equal” (1) or
“not important” (0)
High public funding
High investments by the partners
TABLE 7: WEIGHTING OF THE EVALUATION CRITERIA
(1)
High investments by the partners
1
4
3
2
2
3.33%
0.03
0.13
0.10
0.07
0.07
(2)
High public funding
1
1
1
3
4
3.33%
0.03
0.03
0.03
0.10
0.13
(3)
Low financial risk for the partners
2
3
5
4
5
6.15%
0.12
0.18
0.31
0.25
0.31
(4)
Possible exit before contractual agreement
3
3
3
3
3
2.19%
0.07
0.07
0.07
0.07
0.07
(5)
Low consequences for early exit
2
1
2
4
4
3.75%
0.08
0.04
0.08
0.15
0.15
(6)
High infrastructural investments
2
3
1
2
3
6.46%
0.13
0.19
0.06
0.13
0.19
0.18
(7)
Simple contract agreements
2
1
3
5
4
4.48%
0.09
0.04
0.13
0.22
(8)
High knowledge and know-how exchange
4
4
2
3
4
10.83%
0.43
0.43
0.22
0.33
0.43
(9)
Proximity of the partners
2
2
2
4
5
9.69%
0.19
0.19
0.19
0.39
0.48
(10)
Possible long-term arrangements
5
5
3
4
4
5.10%
0.26
0.26
0.15
0.20
0.20
(11)
Short communication paths
2
2
2
4
5
9.48%
0.19
0.19
0.19
0.38
0.47
(12)
Low institutionalization of communication
3
3
2
4
4
4.17%
0.13
0.13
0.08
0.17
0.17
(13)
Simple acquisition of new partners
2
2
1
5
5
3.85%
0.08
0.08
0.04
0.19
0.19
(14)
High correlation of research and industry
1
1
1
3
5
10.00%
0.10
0.10
0.10
0.30
0.50
(15)
High focus on R&D and technology leadership
3
3
5
4
5
11.15%
0.33
0.33
0.56
0.45
0.56
(16)
Flexible adaption of tasks
2
2
1
4
3
6.04%
0.12
0.12
0.06
0.24
0.18
2.38
48%
2.52
50%
2.37
47%
3.62
72%
4.29
86%
Sum
Share of fulfillment
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2015 Proceedings of PICMET '15: Management of the Technology Age
Research campus
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Industry cluster
High investments by the partners
High public funding
Low financial risk for the partners
Possible exit before contractual agreement
Low consequences for early exit
High infrastructural investments
Simple contract agreements
High knowledge and know-how exchange
Joint venture
Licensing
Strategic alliance
Evaluation criteria
(9) Proximity of the partners
(10) Possible long-term arrangements
(11) Short communication paths
(12) Low institutionalization of communication
(13) Simple acquisition of new partners
(14) High correlation of research and industry
(15) High focus on R&D and technology leadership
(16) Flexible adaption of tasks
Figure 4: Illustration of the applicability of the cooperation models for collaborative R&D
The research campus fulfills in particular the highest
weighted criteria, e.g. (14) high correlation of research and
industry, (15) high focus on R&D and technology leadership
and (8) high knowledge and know-how exchange. Moreover
research campus has high utility concerning (9) proximity of
the partners and (11) short communication paths. However
regarding (1) high investments by the partners and (16) flexible adaption of tasks the research campus is assessed with
low utility. Figure 4 gives a graphical overview of the evaluation results.
The industry cluster is comparable to research campus.
Only the criteria (14) high correlation of research and industry is not equally fulfilled.
Joint venture, licensing and strategic alliances are stiff cooperation forms in relation to industry cluster and research
campus. Therefore the criteria (13) simple acquisition of
partners and (7) simple contract agreements cannot be met
sufficiently. Further deficits appear especially in the (9) proximity of the partners.
Nevertheless the utility analysis reveals that some aspects
of these cooperation models are favorable. (10) Possible
long-term agreements and (8) high knowledge and know-how
exchange are evaluated as good on average. Moreover licensing focuses above average on (15) high focus on R&D and
technology leadership with a (3) low financial risk for the
partners.
In the further considerations the evaluation results have to
be reviewed in order to spot interesting aspects of the cooperation models that can be useful for ARENA2036.
VI. IMPLICATIONS FOR THE RESEARCH CAMPUS
ARENA2036
Comparing the criteria of the utility analysis with the results of the interviews regarding the current situation in the
research campus ARENA2036, we observe that twelve out of
16 evaluation criteria are already fulfilled. The other four
criteria need special attention in the future implementation of
the cooperation model.
Concerning high investments of the partners, simple contract agreements and flexible adaption of tasks there is potential for improvement. Even though the criterion short communication paths is sufficiently complied, the collaboration
of the heterogeneous partners in ARENA2036 is challenging.
The criterion high investments of the partners will be
stepwise fulfilled in ARENA2036. The investments will
become higher after the new building for the research factory
since real investments will accomplish the financial investments. Compared to the cooperation model joint venture,
joint venture has a better result however the funding of the
two cooperation models is not comparable. Research campus
as well as ARENA2036 are dependent on public funding
which is increased by the investments of the partners. Regarding ARENA2036, rising the investments of the partners
is possible and is individually negotiated. Irrespective of the
above innovative ideas are followed even if there is no public
funding available.
Simple contract agreements are also linked to the consequences of early exit. The contract regulates the exchange
of knowledge and know-how of the partners. Even though
there is no fee for early exit in ARENA2036 the possible
consequences will be caused by the knowledge transfer especially if partners do not respect the contractual agreements.
The lowest consequences for early exit are given in a community of interest since this is the simplest form of cooperation agreement. Simple contractual agreements with exit
terms would be sufficient if there is no other dependence
factor between the partners. However the stability and proximity of the partners might not be possible on such loose
agreements [7]. A similar approach has the virtual company.
This cooperation bases on a trustful relationship of the partners [10]. Regarding ARENA2036 a trustful base should
complement the contractual agreements.
The flexible adaption of tasks is limited by the agree-
277
2015 Proceedings of PICMET '15: Management of the Technology Age
ments of the public funding. The complimentary funding of
ARENA2036 consisting of public funding and investments of
the partners absorbs this effect. A flexible adaption of tasks is
also possible in a virtual company assumed that there is a
good foundation for collaboration and a trustful relationship.
If so the higher flexibility can lead to better effectiveness and
dynamic in the project. As before mentioned a special focus
should be on the trust of the partners in the cooperation.
The communication in ARENA2036 shows a low level
of institutionalization. The foundation of the collaboration is
a trustful relationship and “right” contractual agreements. The
heterogeneous manner of thinking and working of the industry partners and partners from research establishments challenges the collaboration [11]. In general the meetings should
be short and efficient. The project manager does not have to
be familiar with all details and processes since this is not
possible in such an interdisciplinary environment. He should
manage on an aggregated result level. Team building activities are one possibility to improve the team work and intensify the personal contact [15]. The introduction of shop floor
management is qualified to create transparency within the
projects in ARENA2036, especially to make important information available.
VII. CONCLUSION
try and their potential for innovation.
So far several successful cooperation models can be found
in the literature and industry. Regarding the requirements of
this new kind of collaborative innovation across the whole
supply chain, including research institutions the existing
cooperation models cannot totally meet all requirements
generated. It is important to keep on analyzing the requirements in order to create an optimal cooperation model for the
future R&D in the automotive industry. Only with a matching
cooperation model future success and technological leadership can be guaranteed.
REFERENCES
[1]
[2]
[3]
[4]
[5]
In this paper well-known and recent cooperation models
mentioned in studies and publications were reviewed. The
early focus on collaborative R&D in the automotive industry
involves the risk that promising cooperation models of other
industries are not further considered. For further research the
investigation of cooperation models for other industries might
give a broader view on the topic. The applicability of the
models for R&D in the automotive industry would not be
considered in favor of innovative cooperation models.
The evaluation represents a first objective view on R&D
collaborations in the automotive industry. It is necessary to
notice that the weightings of the criteria base on expert interviews only within ARENA2036. The heterogeneous partners
assure a representative result but might also be influenced by
their participation in ARENA2036. Further polls and interviews with experts with another background and expansion to
Delphie Method could reconfirm the results.
No matter from what perspective we look at the cooperation models, it is a fact that cooperation in the automotive
industry becomes rapidly more important and the research
campus with the possibility for collaboration at one place
with various partners is expected to become a success model.
In Germany the research campus ARENA2036 in Stuttgart
and Open Hybrid LabFactory in Wolfsburg are role models
for innovative R&D as well as innovative cooperation. Besides these two research campus focusing on lightweight
construction and manufacturing for the automotive industry
there were seven further research campus founded within the
last three years. We have to be curious about the further development of the R&D cooperation in the automotive indus-
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17]
278
Baum, H., Morphologie der Kooperation als Grundlage für das
Konzept der Zwei-Ebenen-Kooperation, 1. Aufl. Wiesbaden: Gabler
Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden, 2011.
Bernhart, W. and T. Schlick, Automotive Engineering 2025, 2011.
https://www.rolandberger.com/media/pdf/Roland_Berger_Automotive_
Engineering_2025_20110430.pdf.
Braun, B. and Schulz, C., Wirtschaftsgeographie, 1. Aufl. utb-studi-ebook 3641. Stuttgart: UTB GmbH, 2012.
Bundesministerium für Bildung und Forschung, Forschungscampus öffentlich-private Partnerschaft für Innovation, Berlin (HightechStrategie),
2014.
http://www.bmbf.de/de/16944.php?hilite=forschungscampus (accessed
March 30, 2015).
Gastes, D., Erhebungsprozesse und Konsistenzanforderungen im
Analytic Hierarchy Process (AHP). Informationstechnologie und
Ökonomie Bd. 42. Frankfurt am Main: Peter Lang, 2011.
Gläser, J. and Laudel, G., Experteninterviews und qualitative
Inhaltsanalyse: Als Instrumente rekonstruierender Untersuchungen, 4.
Aufl. Lehrbuch. Wiesbaden: VS Verlag für Sozialwiss, 2010.
Gürerk, Ö., Irlenbusch, B., and Rockenbach, B., “On cooperation in
open communities,” Journal of Public Economics, vol. 120, pp. 220–
230, 2014.
Harzer, K., Wie Sie Gewinn bringend Kooperationen schmieden, 1.
Aufl. Berlin: Cornelsen, 2006.
Hoffmeister, W., Investitionsrechnung und Nutzwertanalyse: Eine
entscheidungsorientierte Darstellung mit vielen Beispielen und
Übungen, 2., überarb. Aufl. Berlin: BWV, Berliner Wiss.-Verl., 2008.
Jarratt, D. and Ceric, A., “The complexity of trust in business collaborations,” Australasian Marketing Journal (AMJ), 2014.
Kalkowski, Peter and Otfried Mickler, “Kooperation in der
Produktentwicklung - Studie zu interorganisationalen F+E-Projekten erste Befunde,” Universität Göttingen, 2013.
Killich, Stephan and Holger Luczak, Unternehmenskooperation für
kleine und mittelständische Unternehmen: Lösungen für die Praxis.
Berlin, Heidelberg: Springer Berlin Heidelberg, 2003.
Köhne, T., Marketing im strategischen Unternehmensnetzwerk:
Erklärungsmodell
und
praktische
Anwendung
in
der
Versicherungswirtschaft, 1. Aufl. Neue betriebswirtschaftliche
Forschung 348. Wiesbaden: Dt. Univ.-Verl, 2006.
Krieger, C., Erfolgsfaktoren interkultureller strategischer Allianzen:
Am Beispiel von bilateralen Kooperationen zwischen deutschen,
französischen und japanischen Automobilunternehmen. Duisburg: Inauguraldissertation, 2001.
Liang, H.-Y., Shih, H.-A., and Chiang, Y.-H., “Team diversity and
team helping behavior: The mediating roles of team cooperation and
team cohesion,” European Management Journal, vol. 33, no. 1, pp. 48–
59, 2015.
Lozano, S., Moreno, P., Adenso-Díaz, B., and Algaba, E., “Cooperative
game theory approach to allocating benefits of horizontal cooperation,”
European Journal of Operational Research, vol. 229, no. 2, pp. 444–
452, 2013.
Meißner, H.-R., Globale Entwicklung in der Automobilindustrie.
http://m.igmetall-
2015 Proceedings of PICMET '15: Management of the Technology Age
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
bbs.de/fileadmin/user/Dokumente/2012/Automobil_Meissner_2012.pdf
(accessed January 26, 2015).
Michel, L. M., Management von Kooperationen im Bereich Forschung
und Entwicklung: Eine empirische Studie, 1. Aufl. Konstanzer
Managementschriften Bd. 7. Konstanz: Hochsch. Konstanz, Technik,
Wirtschaft und Gestaltung, 2009.
Morschett, D., “Formen von Kooperationen, Allianzen und
Netzwerken,” 2005. In Kooperationen, Allianzen und Netzwerke:
Grundlagen - Ansätze - Perspektiven, ed. Joachim Zentes, 377–404. 2.,
überarb. und erw. Aufl. Wiesbaden: Gabler.
Narayanan, S., Narasimhan, R., and Schoenherr, T., “Assessing the
contingent effects of collaboration on agility performance in buyer–
supplier relationships,” Journal of Operations Management, vol. 33-34,
pp. 140–154, 2015.
Nebl, T., Produktionswirtschaft, 5., unwesentlich veränd. Aufl. Lehrund Handbücher der Betriebswirtschaftslehre. München, Wien: Oldenbourg, 2004.
Nissen, H. A., Evald, M. R., and Clarke, A. H., “Knowledge sharing in
heterogeneous teams through collaboration and cooperation: Exemplified through Public–Private-Innovation partnerships,” Industrial Marketing Management, vol. 43, no. 3, pp. 473–482, 2014.
Oesterle, M.-J., “Kooperationen in Forschung & Entwicklung,” 2005.
In Kooperationen, Allianzen und Netzwerke: Grundlagen - Ansätze Perspektiven, ed. Joachim Zentes, 769–96. 2., überarb. und erw. Aufl.
Wiesbaden: Gabler.
Ortiz, A., Kooperation zwischen Unternehmen und Universitäten: Eine
Managementperspektive
zu
regionalen
Innovationssystemen.
SpringerLink: Bücher. Wiesbaden: Springer Fachmedien Wiesbaden;
Imprint: Springer Gabler, 2013.
Pöchtrager, S., Qualitätsmanagement in der Agrar- und
Ernährungswirtschaft:
Institutionen,
Strukturen
und
entscheidungsrelevante Faktoren. SpringerLink: Bücher. Wien:
Springer, 2011.
Porter, M. E., Wettbewerbsvorteile: Spitzenleistungen erreichen und
behaupten, 8., durchgesehene Auflage Auflage, neue Ausg. Frankfurt
am Main: Campus, 2014.
[27] Reiner, Jürgen and Fabian Brandt, “Next generation automotive engineering,”
Automotive
Manager,
pp.
12–14,
2013.
http://www.oliverwyman.de/5860.htm (accessed January 26, 2015).
[28] Saaty, T. L. and Vargas, L. G., Decision making with the analytic
network process: Economic, political, social and technological applications with benefits, opportunities, costs and risks, 2nd ed. International series in operations research & management science v.195. New
York: Springer, 2013.
[29] Sirkin, H. L., The Power of Industry Clusters, 2012.
http://www.bloomberg.com/bw/articles/2012-10-22/the-power-ofindustry-clusters (accessed March 30, 2015).
[30] Stolz, Lars and Johannes Berking, FAST 2025 - Future Automotive
Industrie Structure - Eine Studie von Oliver Wyman. Berlin, Frankfurt
am Main, 2012.
[31] Stolz, Lars and Johannes Berking, “Massive changes in the automotive
value-chain structure,” Automotive Manager, pp. 7–11, 2013.
http://www.oliverwyman.de/5860.htm (accessed January 26, 2015).
[32] Strietzel, M., Unternehmenswachstum durch Internationalisierung in
Emerging Markets: Eine neo-kontingenztheoretische Analyse. Schriften
zum
europäischen
Management.
Wiesbaden:
Deutscher
Universitätsverlag, 2005.
[33] Teirlinck, P. and Spithoven, A., “Research collaboration and R&D
outsourcing: Different R&D personnel requirements in SMEs,”
Technovation, vol. 33, no. 4-5, pp. 142–153, 2013.
[34] Thommen, J.-P., Betriebswirtschaftslehre, 4. Aufl. Zürich: Versus,
1996.
[35] Wu, J., “Cooperation with competitors and product innovation: Moderating effects of technological capability and alliances with universities,” Industrial Marketing Management, vol. 43, no. 2, pp. 199–209,
2014.
[36] Zillig, T., “Neue Organisationsformen - Theoretische Grundlagen Entwicklungstendenzen - Forschungszentren - Experteninterviews,”
2003.
279