Design of Success Criteria Based Evaluation Model for Assessing the Research Collaboration Between University and Industry

Abeda Muhammad Iqbal, Adnan Shahid Khan, Saima Iqbal & Aslan Amat Senin
International Journal of Business Research and Management (IJBRM), Volume (2) : Issue (2) : 2011 59
Designing of Success Criteria-based Evaluation Model for
Assessing the Research Collaboration between University and
Industry
Abeda Muhammad Iqbal abidaiqbal49@yahoo.com
Faculty of Management and Human Resource Development
Universiti Teknologi Malaysia, 81310,
Skudai, Johor, Malaysia,
Adnan Shahid Khan adnan.ucit@gmail.com
UTM-MIMOS Center of Excellence,
Faculty of Electrical Engineering,
Skudai, Johor, Malaysia
Saima Iqbal saimaiqbal_pk@hotmail.com
Preston Institute of Management,
Science and Technology, Karachi,
Pakistan
Aslan Amat Senin aslan@fppsm.utm.my
Faculty of Management and Human Resource Development
Skudai, Johor, Malaysia,
Abstract
Innovations and inventions are not outcomes of single activity of any organization. This is a result
of collaboration of different partners. Collaborated research of university and industry can
enhance the ability of scientist to make significant advances in their fields. The evaluation of
collaborated research between university and industry has created the greatest interest amongst
the collaborational researchers as it can determine the feasibility and value of the
collaboration. This paper intends to illustrate the evaluation metrics and success criteria- based
evaluation model within university-industry and their collaborated research. For bridging the
model, success criteria that is based on key evaluation metrics has been identified. A successful
Collaboration of university and industry is not dependent on any single metric but instead on the
confluence of multiple metrics from the growth of basic research to commercialization. This study
is intended to provide different evaluating metrics to impound the research collaboration
constraints between university and industry, and to design success criteria to upsurge the
successful linkage. For this purpose, we have developed constraints and success criteria based
evaluation metrics (CASEM) model. The proposed model is appropriate for almost all types of
collaborations, especially research collaborations between university and industry. By adopting
this model, any university or industry can easily cross the threshold in the grown-up research
collaborational community.
Keywords: University Industry Research Collaboration, Evaluation Metrics, Evaluation Model,
Technology Transfer, Success Criteria
1. INTRODUCTION
The accelerating antagonism in consumer as well as the commerce world is forcing industry to
discover the new ways to promote product and service innovations. To increase the number of

fundamental innovations and for the technological development frequent collaboration and
cooperation of university-industry is crucial. The importance of university-industry research
collaboration has risen steadily as a consequence of growing complexity, risk and cost of
innovation. The improvement in the relationship between science and technology, the integration
of science and industry, the appearance of industries based on science, the use of science as a
means to produce competitive advantages on the part of the firms, as well as the globalization of
the economy and internationalization of technology, are some of the reasons which justify the
cooperative relationship strong collaboration between firms and research organizations [1].
National economies somehow depend on research implication, that’s why most of the nations
reserve big amount of their annual budget for their education especially on research aptitudes. A
very huge number of research activities every year is going on in all developed and developing
countries, but all the researches are not commercialized thus leaving some weaknesses on the
university-industry collaboration (UIC).
For the evaluation of university-industry collaboration and for the maturity of any nation’s
technology transfer are no doubt very important and a powerful means discussed by practitioners
as well as by scholars [2]. University research centre is one of the most attractive external
sources of technology for the industry, in an industrialized country; there exist a strong
collaboration between university and industry to facilitate the exchange of technology [2]. The
mere presence of traditional economics inputs of land, labor and capital is no longer enough to
ensure economic growth in a nation. What is now important is the rationale application of these
resources to productive purposes by means of technology. Both the industrialized and developing
nations recognize the fact that university- industry research collaboration plays a significant role
in economic growth and the improvement of living standards of their countries. It is widely
acknowledge that transfer of technology has played a key role in the economic and industrial
development of any nation. Despite the great importance of university-industry collaboration,
there have been some major constraints in successful collaborations. Therefore it is necessary
for the developing countries to promote the relationship between university and industry and
should also identify and improve those elements in which they are weak, such as developing an
appropriate industrial and technological infrastructure. As the matter of fact, the first generation of
any commercialized product in infancy stage is always incubated in research center and the final
place just before commercialization is R&D of industries. University research is normally
education based but industry demands commercial based research, thus most of the research
seems to be useless and shelved in the library for the references leading towards the wastage of
resources every year [1]. To avoid such problems, we need strong collaboration. Periodically,
evaluation of the research collaboration is also one of the key steps to strengthen the linkage.
However other important techniques should also be adapted to develop their existing
collaboration.
This paper is organized as follows: section 2, describes related work Section 3, presents the
Research Method Section 4, CASEM Model, In section 5, key evaluation metrics with their
success criteria, In section 6, Performance analysis which is followed by conclusions and future
works.
2. RELATED WORK
The idea and concepts associated with university-industry partnerships are not new and it is
commonly agreed that universities are an important source of new knowledge for industry [2]. The
perspective of the university as a key contributor to wealth generation and economic development
[3] has increased in recent decades. The author of [4] states that academic research has become
“indigenized and integrated into the economic cycle of innovation and growth”. Within the current
knowledge based economy, the university acts as both “a human capital provider and a seed-bed
for new firms” and innovation [5]. In the USA some of the most prestigious universities (e.g., MIT)
were established more than one century ago to support close research relationships between
university and industry (U-I) [6]. The partnership (U-I) has been considered as one of the main
factors contributing to successful innovation and growth in the past two decades [7]. There is
plethora of research studies on identifying and analyzing cultural, technical, legal and macro-

organizational factors governing the success of U-I collaboration [8]. To increase the number of
fundamental innovations and for the technological development frequent collaboration and
cooperation of university-industry is crucial. Successful cooperation between university and
industry requires special kind of synergy. In this manner huge number of studies has analyzed
the interactions between the firms and research organizations that generate knowledge and
enable firms to transform it into tangible forms applicable by country. Several papers have
examined the relationships among university, industry and government agencies the so-called
triple helix metaphor [9]. Some authors focus on the technological progress, some focus on the
characteristics and their culture; most of the author addresses the implications of the metaphor in
the context of regional policy [10]. Some authors have tried to write the role of academic
organization on the development of economy based on the product development [11]. Some
author focus on the motivation highlighting the collaboration [12].
Many more studies has been analyzed about university-industry knowledge transfer and their
technological collaboration[13], [19] and up till now new research is going on to make this
collaboration stronger as this collaboration is crucial by social, economical, educational, industrial
as well as political point of view. Unfortunately a few numbers of researches has been attempted
for the assessing of research collaboration. In this paper, we have focused mainly on evaluation
metrics and success criteria to evaluate university-industry research collaboration and proposed
CASEM model that can be significant for all sorts of collaboration specially research
collaboration. For this purpose, we have investigated all the major constraints that always are a
conflict between these two important partners. At the end we have demonstrated the outcomes or
outputs that are the consequences of the best collaboration, and it shows the strength of their
relationship.
3. RESEARCH METHOD
To achieve the best success criteria of university-industry research collaboration, in the beginning
we proposed two types of research questions.
1.What are the constraints and tangible outcomes associated with establishing and maintaining
research collaboration between university-industry?
2.What are the evaluation parameters need to take in consideration to evaluate the strength of
research collaboration?
The first question aimed to explore the constraints and impeding factors which are commonly
associated with establishing and maintaining research and technological links between university
and industry. This research question seeks to describe the phenomenon and describe the cause
and effect between the phenomenon of university-industry links and a range of proposed factors.
The second research question seeks to identify the existing parameters for the evaluation of
university-industry collaboration. For this purpose some evaluation metrics already proposed to
the respondents to choose appropriate key metrics. In order to collect reliable information about
the university-industry research collaboration and their success criteria, a comprehensive
questionnaire was developed in order to get the detail description. For this purpose, the selection
of the respondents was the most challenging part of this research. After a long decision process,
our key respondents were the fresh PhD graduates, final year PhD students and research officers
from the “Research centers and Centers of excellence” have been selected from the universities
and actively participate with the industries and they have old experience of university-industry
collaboration.
Second step was to develop the questionnaire. For this purpose, we developed both type of
questionnaires quantitative and qualitative. The quantitative questionnaire was comprised of 60
questions. Respondents were asked to rate each request on a scale from 1 to 5 with being
strongly disagree and 5 strongly agree. To develop the survey questionnaire we conducted a
number of interviews with different research centers of the university that have strong research
alliances with their collaborated industries as well as reviewing the literature. In the survey, the

questionnaire has been developed about the constraints between university-industry
collaboration, evaluation metrics and success criteria to make this collaboration stronger and also
created a separate portion for the corresponding tangible outcomes of success criteria. The
process of data collection was completed in different stages with different modes. Our most
concern was face to face data collection which is done by structured interview and almost all the
respondent interview even for quantitative collection to get more and more reliable data and to
minimize the chances of missing data. For ensuring the reliability of the data, we have conducted
a number of tests like in the interviews, the reliability increases if the same question is asked
more than once in a similar way with different respondents and similar response prove the validity
of sample and data.
Moreover, we have compared the quantitative and qualitative data and we found very slightly
difference in both of them that shows the data is reliable. Finally after the data gathering through
our successful survey, the data has been analyzed to recognize the success criteria of university-
industry collaboration. The survey was very helpful for the development of evaluation model for
university-industry research collaboration.
4. CASEM MODEL
In our proposed CASEM evaluation model, the success criteria are directly or indirectly based on
core limitations and constraints of university-industry linkage via evaluation metrics. To evaluate
this linkage our key concern is to finalize the main constraints by using qualitative and
quantitative data as well as secondary data collection to finalize the constraints. Once the
constraints are tested we will again use above mentioned data collection scheme to finalize the
evaluation metrics. Success criteria will be described for evaluation metrics and the result of the
evaluation metrics will be compared with the tangible outcome. If the comparison shows almost
all the same parameters then we can say the type of collaboration is stronger.
Our model comprises of five steps.
1. Constraints
2. Evaluation Metrics
3. Success Criteria
4. Tangible Outcomes
5. Comparison of Success Criteria and Tangible Outcome
FIGURE 1: Overview of CASEM Model
Constraints
Tangible Outcome
Success CriteriaEvaluation Metrics
Strength of Research
Collaboration

In this research, during our initial survey we have sort out some crucial constraints that truly affect
the collaboration of research centers and their collaborated R&D or university and industry e.g.
Education and training, consultancy and technical service provision, conflict of intellectual
property right, lack of technological competency, cultural difference and public policies are the
key factors that can limit this linkage.
5. KEY EVALUATION METRICS
On the base of these constraints, we have generated key evaluation metrics like strong
communication, joint venture, cooperative R&D agreement, knowledge sharing, cultural
development financial support and communication. Each evaluation metrics have its own success
criteria. These success criteria produce tangible outcomes that always include master’s thesis
and doctoral dissertations, patent or non-patented, licensed or non-licensed product or process
are the evidence and the signal of successful collaboration between university and industry.
Following are the key evaluation metrics with corresponding success criteria and tangible
outcomes. Figure 2 shows the proposed CASEM Model with complete list of key evaluation
metrics with corresponding success criteria to evaluate the strength of the linkage.
5.1 Education and Training
Lack of education and training is a line of limitation between university-industry collaboration.
According to [13], [20] the failure of the transfer of computer technology to china was the small
number of personal trained in the computer field and also the lack of understanding of computer
software. Technology to be transferred and maintained in the firm appropriate education and
training must be developed. Most of the time universities do not collaborate and licensed their
technology to those firms who have not sufficient capabilities to maintain it. Because of this
hindrance technology cannot be transferred and become useless and shelved in the library just
for the references leading towards the wastage of resources. Consequently, universities have to
export their technology to any other country.
We can develop education and training and cover this problem by using appropriate and
corresponding evaluation metrics which is directly related to this constraint. Knowledge sharing
and flow of human knowledge is among the most important evaluation metrics of university-
industry collaboration and technology acquisition. There are certain success criteria that
combined together to make knowledge sharing as an evaluation metric for education and training
constraints. Amongst those success criteria, video conferencing, workshops, seminars, training,
personal interactions, group visit to universities or industries for formal or informal meetings.
During our survey we have analyzed that more the above events occur, stronger the linkage it is.
For example, “Training” as success criteria: If we have more training on recent technologies, the
more tangible outcome we generate. However wining of national or international funded projects
is the immediate tangible outcome for training.
5.2 Culture Difference
Every year university-industry scientists take more pressure to work with each other and it is
emphasized from the government to university-industry scientist to collaborate or cooperate to
each other for the development of the technology in the country. The major constraint between
university-industry collaboration is culture difference. University-industry fundamentally has their
own culture, which reflects in divergent goals, time, orientations, basic assumptions, and
characteristics. In our survey we have found some specific differences that are as follows.
University always focus on basic research but industry quite oppositely always focus on applied
research, the basic rationale of the university is to develop advance knowledge but industries
have to increase their efficiency, the aim of the university is to generate new ideas but on the
other side industries have to generate more profits, both characteristics is totally different,
university is known as an idea centre but industry is known as product centre, university have
open framework but industry want closed and confidential framework, university evaluation is
possible by peers but industry always evaluated by the boss. So, we can say that from dawn to
dusk they have different vision and opinion.

On the bases of these constraints and with the help of our survey some success criteria has been
found to make this collaboration stronger and for the development of this culture. Before the
agreement university-industry must have to identify their common goals, this is a prerequisite
incentive for both partner and then from the beginning of the project until development they must
keep the mutual perception. It can be helpful for achieving the goal and at the end they have to
promote entrepreneurial concept for distributing the benefit equally. Development of technology is
the tangible outcome of the same culture and the proof of the successful collaboration between
university and industry. Thus mutual perception, similar objectives, common goals and
entrepreneurial concept are indicating cultural development as an evaluation metrics of cultural
difference.
5.3 Conflict of Intellectual Property
According to our survey and endless literature which shows the issue regarding the ownership of
intellectual property, which always appears in the shape of quarrel between university-industry
collaboration. Researchers need protection of property rights of their inventions even before
proceeding with the partnership. But the acquisition of this right is very complex, difficult and
multifaceted because industry also expects ownership of intellectual property (IP) by virtue of its
huge investment. One of the findings of our survey, in vision of the university after the agreement
industry becomes able to stop the flow of information and they put the publication of research
result in delay. On the other hand industrialist commonly perceived that universities are too
aggressive in exercising intellectual property rights this result is a hard line on negotiations,
excess concern on the part of university administrators that they will not realize sufficient
revenue, and unrealistic expectations[13], [14]. However conflict of intellectual property right not
only damage the university- industry collaboration but also it is creating the monopoly and slowing
the innovations of the country.
Given birth to by the Federal Technology Transfer Act in 1986 Cooperative Research and
development Agreements (CRADAs) has ever since emerged as one of the popular and
successful university-industry collaboration and technology transfer mechanisms from public
research labs to industry and thus gained much interest of researchers [15]. In the developed
countries, the improvement of science and technology and inventions of discoveries are some of
the reason which justifies cooperative relationship and strong agreement between firms and
research organization. Cooperative research and development agreement (CRADA) reflects
close interactions through institutional agreement, group agreement, and strong commitment as
success criteria which can be used to diminish every type of conflict with intellectual property right
between university-industry collaboration. Before signing the Memo and starting the agreement
terms and conditioned must be defined between both partners. Strong commitment can play a
vital role to fulfill this term and condition. Commitment has a positive influence on the success of
cooperative agreements between firms and research organizations. Every cooperative
agreement requires a high level of commitment by the partners to make project and collaboration
successful.
5.4 Time Constraint
One of the finding of our survey is time constraint which is major and big constraints between
university-industry collaboration. The academic world always takes time to publish their research
result without concerning towards market condition and expectation of the industry. On the
corresponding side industries usually requires immediate solution of their problem and it’s not
ready to wait until the result of a particular research are available. Any specific time from the
academic world to the firm is the meaning of the lost of the investment and income. By facing this
problem industries always compelled to import the solution instead of cooperation or collaboration
with the local universities seems to be best alternative. It is the responsibility of the university to
provide the solution to the industry on time as they have signed the contract to work together in a
joined research collaboration field.
For many firms, universities are too tricky to work with, and they avoid any form of university
collaboration. Avoidance is not a solution, university-industry must create such calm environment

where they can work together patiently and smoothly, this process is called joint venture. Joint
venture is a successful evaluation metric of university-industry collaboration that provide close
working relationship concept. The basic collaboration process between academia and industry
usually starts with each party identifying what can possibly acquired from the alliance and the
potential needs of the other party. The strategy to develop the joint venture, both partners must
have to organize the chart for identifying their basic needs and recognizing their mission.
5.5 Fund and Financial Difficulties
From different survey and interviews we have analyzed that Fund and financial difficulty is a
major constraint between university-industry collaboration. University needs funds and equipment
from the industry for continuing their research, and the life of their research is highly dependent
on the financial support of the industry and government. No doubt industry provide fund to the
University for the Development of new research but alternately expects commensurate return on
the base of their investment [16]. This stringent perception of the industry always create problem
between their collaboration. On the other hand (80 per cent) respondents from the university cited
financial motivations for research and technological links with industries. Furthermore the fact that
many respondents were also willing to discuss in detail about financial matter. According to them,
90 per cent part of the day, they expend in the research so, for the survival of their lives they
need extra assistance.
Financial support is the contribution of both money and equipment made to universities by
members of the corporate community [17], [18], [21] that is very significant and beneficial for both
partners. Fund, grant, endowments, scholarship and internship are not only providing the
assistance to the researcher but also the best success criteria of university-industry collaboration.
Financial support is the one exclusive metric that can motivate the researchers to work with the
industry in an open, positive and friendly environment.

FIGURE 2: Proposed (CASEM) for the Evaluation of University- Industry Research Collaboration
5.6 Technological Competency
According to 60% respondents from different research centers of the universities, insufficient
technological competency in the industries is also a barrier of university-industry collaboration.
Technical assistance is usually required by a firm which has less experience in operation and
setting up of any productive activity. It normally contains maintenance and repair of machinery,
obtaining specification, assistance in setting up production facilities, advice on process know-
how, consultation with manufacturing, personnel training and testing of final products. In short
term, inadequate capabilities to tackle the situation and to maintain the technology in the firm
need technical assistance. Since universities have time constraints problems due to their a lot of

academic stuffs, they don’t want more complexities in their tough schedule that’s why they do not
collaborate with such industries where they have to provide technical assistance with the
technology as well. Sometimes universities licensed their technologies to foreign countries where
they feel free from every type of obligation for providing the technological assistance.
Licensing of the technology to foreign countries leaves a bad impact on the country image as well
down the economy. University-industry can save the country image and the economy by the
strong communication between them. Consultancy and technical service provision always
depend on the frequent communication. Frequent communication allows both parties to share
their problems and get the technical information. Regular exchange of information and
interchange of concept and ideas is a success criteria of strong communication. The process of
communication between two or more different organizations must be taken into high
consideration within the context of inter-organizational relationships. Transmission of information,
prompt decision taking, coordination of activities, and execution of power these all entities are
possible in the existence of strong communication.
6. PERFORMANCE ANALYSIS
In our proposed Model, we used constraints, evaluation metrics, success criteria and tangible
outcomes as parameters for evaluation of any sorts of collaboration between university and
industry and especially for research collaboration. We have evaluated our proposed Model and
response of key researchers from different research centers provide different statistics to the best
candidates to be included in the evaluation metrics.
TABLE 1: The Constraints
FIGURE 3: Constraints of University and Industry
In Figure 3, we have tried to analyze all those constraints that always are the major barriers
between university-industry collaboration. Our first target was to explore the constraints of (U-I)
ET Education and Training
CD Cultural Difference
COIP Conflict of Intellectual Property
FD Financial Difficulties
CT Technological competency
TC Time constraints

collaboration. The Figure 3 provides strong evidence that majority of respondents (researchers)
agree that education and training (ET), Cultural difference (CD), Conflict of intellectual property
(COIP), Financial difficulties (FD) are the best candidate to be measure at its priority to evaluate
the strength of collaboration of university and industry. According to Figure 3, we can analyze
financial difficulties, cultural difference, education and training and conflict of intellectual property
right are the main constraints of university-industry collaboration. However, Technological
competency (CT), and Time constraints (TC) are less chosen candidates by the respondents
which were one of our hypothesis but cannot be ignored. Figure 3 shows that financial difficulty
and cultural difference are going up to 93% and 92% respectively, while education and training
and conflict of intellectual property right are going up to 82% and 80% respectively that is a clear
picture of major constraints.
Figure 4 shows the graphs about the evaluation metrics and respondent behaviors on
corresponding metrics. In this Figure, we can see how firmly respondent agree to joint venture
(JV), Knowledge sharing (KS), Cultural development (CD), Cooperative R&D agreement (CRDA),
Financial support (FS) and communication (C ) respectively to be the best evaluation metrics.
According to the graph, almost 93%, 91% and 89% respondents agree for FS, CD and KS
respectively to be included in the category of best evaluation metrics while 81% and 79% of the
respondents gave vote for JV and CRDA respectively and 70% respondents showed their interest
towards C to be included in the evaluation metrics. However, many other evaluation metrics were
also proposed initially in our hypothesis but majority of the respondents highly ignored their
importance that is why those are not included in our measurement scale.
TABLE 2: Evaluation Metrics
JV Joint venture
KS Knowledge sharing
CD Cultural development
CRDA Cooperative R&D agreement
FS Financial support
C Communication
FIGURE 4: Evaluation Metrics for university-industry collaboration
Figure 5 provides the graphs about the important parameters to be included in the tangible
outcomes from the collaborated research between university and industry. According to Figure 4,

majority of the respondents (researcher) agrees that Licensed or non licensed product, patent
non patent application and commercialized product should be evaluate on their priority to analyze
the strength of tangible outcomes. They are in the range of 91%, 90% and 87% of the scale
respectively. while doctoral thesis (DT), ISI- Scopus research paper, and Masters dissertation
(MD) are the second best candidate for the evaluation of strength of tangible outcomes of this
collaboration because they are in the range of 81%, 80% and 71% of the scale. However, we
have included conference research paper (CP) that also the candidate for tangible outcomes but
small percentage shows the less priority of the respondents.
TABLE 3: Tangible Outcomes
FIGURE 5: Outcomes of University-Industry Collaboration
Figure 6 is the most important part of the paper which provides broad ways to think or apply for
the establishing and maintaining the research collaboration. Respondents took much interest
during the survey and they agree with all the given points respectively to be the best success
criteria of university-industry research collaboration. In the perception of the respondents these
criteria are not only provide the equal benefit to both partner but also provide the commensurate
return to them. In the perception of the respondents criteria 1, 3 6, 7, 8, 10, 13, 16 are the best
candidate to be included in success criteria category while 2, 4, 5, 11, 12, 17, 18 are the second
best candidate to be the part of success criteria category of university- industry collaboration.
According to our structured interviewed process, we have analyzed that to finalize the parameters
to evaluate the linkage, the success criteria should be prioritized exactly according to their
percentage agreed upon by the key researchers as respondents
DT Doctoral Thesis
MD Masters Dissertation
CP Commercialized product
L or NLP Licensed or non Licensed product
ISRP ISI Research paper
CRP Conference Research paper
PNPAPP Patent and non Patent application

TABLE 4: Success Criteria
Number of Project 1
Number of technical staff per
project
2
Number of research paper 3
Workshops 4
Seminar 5
Hiring of recent graduates 6
Similar objectives 7
Mutual perception 8
Common goals 9
Strong commitment 10
Flexible and informal
interaction
11
Intuitional facilities 12
Scholarship 13
Fund 14
Trust 15
Exchanging information 16
Interchange concept 17
Interchange ideas 18

FIGURE 6: Success criteria for the Evaluation of University-Industry Collaboration
7. CONCLUSION
University-industry research collaboration is a sizeable subject not only for the scientist, business
analyst but also for the policy makers. Despite of this interest a very few attempts has been taken
for the evaluation of university-industry collaboration. In this paper, we have analyzed the major
factors that is key responsible for the hindrance between the important research collaboration. On
the base of these factors, evaluation metrics has been germinated that is not only helpful to
evaluate the collaboration in different aspects of research collaboration but also give significant
support to extract success criteria for each evaluation metrics. This success criteria helps to
evaluate the linkage in closely to generate tangible outcomes, that are the basic need to complete
evaluation process within collaborating partners. Later, in this paper, we have developed
CASEM Model that is comprises of four specific parameters which are, constraints, evaluation
metrics, success criteria and tangible outcomes. This model is exclusively responsible not only for
evaluation of research collaboration but also all sorts of collaboration between university and
industry can be evaluated.
ACKNOWLEDGEMENT
The authors would like to thanks Faculty of Management and Human Resource Development
(FPPSM) and Research Management Center (RMC), Universiti Teknologi Malaysia (UTM) for
their partial funding contributions.
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