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An Exploratory Study on Technology Transfer in Software Engineering



Background: Technology transfer is one key to the success of research projects, especially in Software Engineering, where the (practical) impact of the outcome may depend not only on the reliability and feasibility of technologies, but also on their applicability to industrial settings. However, there is limited knowledge on the current state of practice and how to assess the success of technology transfer. Objective: We aim at elaborating a set of hypotheses on how technology transfer takes place in Software Engineering research projects. Method: We designed an exploratory survey with the participants of two large research projects in Germany, which involve both industrial and academic partners in the area of model-driven development for embedded systems. Results: Base on the extracted respondents answers of this survey , we defined a resulting theory which is based on the following set of main hypothesis: Most of the technologies developed in research projects are not mature enough for a direct application, but need post-project customisation to fit the industrial contexts (H1). Common models that represent technology transfer as a transaction of an object from a transferor to a transferee does not fit industrial reality (H2). Additionally, technology transfer takes place without an explicit process (H3). Regarding transfer mediums, most used mediums are human-intensive (H5) and industry organisations gain new knowledge mainly within their own confines (H4). Finally the motivations that drive the transfer in industry and academia are heterogenous (H6). Conclusions: From the theoretical perspective, this theory and set of hypotheses extracted from the survey results will be further explored and tested in different follow-up activities. This initial set, however, already may serve as a basis for independent assessments from other researchers to collaboratively shed light on a how technology transfer takes place in Software Engineering research projects, which are the barriers, and how to improve the transfer into practice. From the practical perspective, our results may be used as a basis for an evaluation framework for the transfer of the developed technologies in our projects. This would also help companies in getting new developed technologies transfer easier to their specific context.
An Exploratory Study on Technology Transfer in
Software Engineering
Philipp Diebold , Antonio Vetr `
o, Daniel M´
endez Fern´
Fraunhofer IESE, Germany
Technische Universit ¨
at M¨
unchen, Germany
Email: vetro/
AbstractBackground: Technology transfer is one key to the
success of research projects, especially in Software Engineering,
where the (practical) impact of the outcome may depend not
only on the reliability and feasibility of technologies, but also on
their applicability to industrial settings. However, there is limited
knowledge on the current state of practice and how to assess the
success of technology transfer.
Objective: We aim at elaborating a set of hypotheses on how
technology transfer takes place in Software Engineering research
Method: We designed an exploratory survey with the partici-
pants of two large research projects in Germany, which involve
both industrial and academic partners in the area of model-
driven development for embedded systems.
Results: Base on the extracted respondents answers of this sur-
vey, we defined a resulting theory which is based on the following
set of main hypothesis: Most of the technologies developed in
research projects are not mature enough for a direct application,
but need post-project customisation to fit the industrial contexts
(H1). Common models that represent technology transfer as a
transaction of an object from a transferor to a transferee does
not fit industrial reality (H2). Additionally, technology transfer
takes place without an explicit process (H3). Regarding transfer
mediums, most used mediums are human-intensive (H5) and
industry organisations gain new knowledge mainly within their
own confines (H4). Finally the motivations that drive the transfer
in industry and academia are heterogenous (H6).
Conclusions: From the theoretical perspective, this theory and
set of hypotheses extracted from the survey results will be further
explored and tested in different follow-up activities. This initial
set, however, already may serve as a basis for independent
assessments from other researchers to collaboratively shed light
on a how technology transfer takes place in Software Engineering
research projects, which are the barriers, and how to improve
the transfer into practice. From the practical perspective, our
results may be used as a basis for an evaluation framework for
the transfer of the developed technologies in our projects. This
would also help companies in getting new developed technologies
transfer easier to their specific context.
Index Terms—Technology Transfer, Transfer into Practice,
Empirical Software Engineering, Survey Research.
The success of research projects in Software Engineering
(SE) often depends on two aspects: (1) producing techni-
cally sound solutions that address their intended purpose, i.e.
achieve the project goals, and (2) transferring the results to the
community to foster innovation. The second part, Technology
Transfer (TT), might be as difficult as the first one due to
the challenges involved in adapting technological solutions
to specific organisational contexts, or even more, reshaping
an existing organisation to embrace a ground-breaking in-
novation [19]. Assessing the transfer entails also numerous
difficulties. In fact, traditional metrics used for this purpose,
such as number of patents or publications [6], do not capture
the effectiveness of technology transfer. Issuing a patent or
a publication might indicate that the solutions developed in a
project are technically sound, but it say little about the success
of the transfer of project results to industrial practice (e.g., as
reported in [17]).
To better understand the current state of practice of TT in
SE research projects, we designed an exploratory study which
should also reveal barriers for a successful transfer and how
to improve it1. Exemplary aspects we investigated are how
academic and industrial partners differ in their motivations [5],
through which type of dissemination the technologies are
transferred, which TT processes they follow in their respecting
research settings. We conduct our study in the context of
two large, heterogeneous research projects taking place in
Germany (ARAMiS2and SPES XT3), each involving more
than 20 partners from both academia and industry in the
context of model-driven development in embedded systems.
For the study, we applied an exploratory study realised via
survey research. The overall design of the survey and a few
preliminary results can be taken from our previously published
material in [10]. In the paper at hands, we now provide more
details on the design by focussing on the specific survey
questions, we report on the complete set of answers collected,
and we induce a set of research hypotheses to steer future
empirical evaluations.
The remainder of the paper is as follows: In Sect. II, we
introduce the study design. In Sect. III, we report on the
survey results, aggregated and segmented by both organisation
type and size. We induce a set of hypotheses in Sect. IV,
highlighting what is already supported by literature and what
differs. Finally, we analyse the limitations of the design and
of the findings in Sect. V, before summarise our contribution
in Sect. VI.
1Projects (partially) funded by the government, working in applied research
fields (e.g. automotive, avionics, railway, automation)
A. Goals
Technology Transfer (TT) is formally defined as ”the pro-
cess of sharing knowledge, machines, equipment, methods,
tools, techniques, processes, and facilities with the aim of
facilitating accessibility of scientific and technological devel-
opments from primary discoverers or transferors to potential
users or transferees/recipients, who will exploit the technol-
ogy into new products, processes, applications, and business
According to this definition, the TT process can be specified
as the transaction of a transfer object (such as knowledge
or machinery [25]) between the transferor (an organisation
seeking the transfer of an object) and the transferee (an
organisation adopting the transfer object) over a medium (e.g.,
guidelines). This specification was incorporated in the survey
with the intention of establishing a common understanding of
TT for all participants (see also the diagram in the center of
Fig. 2). in context of the survey, we decided to simplify two
things: (1) TT is a unidirectional activity from the transferor
to the transferee, and (2) is not mediated by further actors
between the transferor and the transferee. We are aware that
this model does not capture all the complex interactions
involved in transferring technology (see for instance [1]);
however, the survey context allowed us to safely approximate
the transfer with such a concept.
We provided the overall goals of the survey and basic
information on the design in a previous publication [10],
whereas our study goals (SG) are briefly summarised in Tab. I
using the Goal template from GQM[3]:
TABLE I: Study goals.
Characterize the TT of SE objects
With respect to (SG 1) to the current state of practice ,
(SG 2) the transfer mediums used, and
(SG 3) improvements / future transfer trends
From the perspective of industry and academia
In context of the projects ARAMIS and SPES XT.
B. Survey Instrument
As instrument, we used a questionnaire divided into five
interrelated sections represented in Fig. 1. The codes contained
in Fig. 1 are the question identifiers, which are listed in Table II
and in Table III. The left side of the figure shows sections
for distilling the demographics. This part was defined for the
segmentation of the results and to navigate though the ques-
tionnaire via conditional questions. The main segmentation of
the data is the differentiation of industry and academia as well
as the different organisational size (large vs. small companies).
The right part of the figure contains the sections linked to our
study goals: (SG1) the current state of TT, (SG2) assessment
medium, and (SG3) improvements which will be detailed in
the following paragraphs.
4We added ”tools and techniques” to better fit the SE context
Industry organisation
Industry organisation
Organisation profile
Participant profile
Current state of Transfer
Currently doing transfer
Medium01-02 Medium assessment (SG2)
Improvements & Future Ideas
Interest in
measurement goals
Concl01-03 Stop
Not doing transfer
Fig. 1: Questionnaire structure
We mainly tailored the validated demographic questions
of [9]. Similar as done by Rombach and Reinhold [22], we
specified only the organisation type to allow for fine-grained
distinctions, e.g. between fundamental and applied research
as well as between organisations, research units, and their
business units. In addition, we asked for the academic degree
as well as for previous work in industry or academia (academia
includes universities and research institutes). Respondents
could access the first two sections (right part of Fig. 1) only
if they were currently performing TT (C Prof05). Herein, we
summarise the aspects investigated for our three goals (SG).
a) SG1 - Current state: We are interested in assessing
the current state of five different facets (question codes in
1) The motivations for TT (Transfer01) using the classifi-
cation of Reisman [21]: social, economic, operational,
strategic, or personal factors.
2) The type of transfer objects (Transfer02) from the list
provided by the adapted definition in [25].
3) The transfer process description (Transfer04) as an open
4) The transfer time (Transfer05 to 08) including average
duration, frequency, and the related perception to compare
it with [20] (setting a TT duration baseline of 18 years).
5) The trigger of ideas for TT (Transfer09).
b) SG2 - Medium assessment: We created a broad clas-
sification of mediums and asked the participants to indicate on
a 5-point scale of frequency how often they were using them
TABLE II: Survey questions SG1 and SG2
Goal ID Question Scale
C Prof01 How many employees are working in your organisation? Interval
Valid answers: <10; 10-49; 50-249; 250-500; >500
C Prof02 What is your organisation ? Nominal
Valid answers: University (e.g., TUM); Basic research institute (e.g. Max-Planck- institute); Applied research institute (e.g., Fraunhofer) ; Development company (research
unit) (e.g., e.g. Siemens CT); Development company (business unit) (e.g., Siemens BUs); Consulting company (e.g., Accenture); Start-up company; Other (specify)
C Prof03 In which domain is your organisation working? Nominal
Valid answers: Automotiv; Avionics; Automation; Railway; Tool; Other(specify)
C Prof04 In which current research project are you working? Nominal
Valid answers (multiple choice): SPES; ARAMIS
C Prof05 Is your organisation performing technology transfer at the moment? Boolean
P Prof01 Which is/are your role(s) in the organisation? Nominal
Valid answers (multiple choice): Requirements Engineer; Developer; Product Manager; Quality Engineer; Architect; Process Engineer; Tester; Decision Maker; Researcher;
Other (specify)
P Prof02 What is your highest academic qualification? Nominal
Valid answers: PhD; Diploma; Master; Bachelor; Other (specify)
P Prof03 Have you ever worked in industry before? Boolean
P Prof04 Have you ever worked in academia (university, research institute) before? Boolean
P Prof05 On which side of the technology transfer are you involved? Nominal
Valid answers: Transferor; Transferee; Both
Transfer01 What are your motivations for Technology Transfer in Software Engineering? Nominal
Valid answers (multiple choice): economic factors (e.g., cost savings); social factors (e.g., job satisfaction); operational factors (e.g., improve material); strategic factors (e.g.,
and / or process innovation); global factors (e.g., political image); personal factors (e.g., knowledge sharing); Other (specify)
Transfer02 What are transfer objects in your current research projects (e.g. SPES-XT, ARAMIS) that you would like to
Valid answers (multiple choice): Knowledge (can specify); Machines (can specify); Equipment (can specify); Methods (can specify); Techniques (can specify); Processes (can
specify); Facilities (can specify); Other (can specify)
Transfer03 What are the transfer medium(s) you are using at the moment? -
Transfer04 How is the technology transfer process performed at the moment in your organization? -
Transfer05 How long do you think a technology transfer transaction takes, on average? (from final object to real usage
in industry)
Transfer06 Do you think that this time should be...? Ordinal
Valid answers (multiple choice): shorter (specify how much); it’s fine like this; longer (specify how much); I don’t know
Transfer07 How often are new transfer objects transferred from your organization? Ordinal
Valid answers (multiple choice): shorter (specify how much); it’s fine like this; longer (specify how much); I don’t know
Transfer08 How often are new transfer objects transferred to your organization? Ordinal
Valid answers (multiple choice): shorter (specify how much); it’s fine like this; longer (specify how much); I don’t know
Transfer09 Where do you get the trigger for technology transfer? Nominal
Valid answers (multiple choice): Own company; Competitors; Consulting companies; Other companies; Research; Other (specify)
Medium01 What are the transfer medium(s) you are using at the moment? Likert 5p
Valid answers: Personnel exchange; Publications; Internet resources; Conferences; Workshops and meetings; Guidelines; Consultancy; Software, systems, and tools; Licensing
and standards; Co-working; Research cooperation; Educational programs
5p. scale values: 1 Never; 2 Rarely; 3 Sometimes; 4 Often; 5 Always; 6 I don’t know
Medium02 Which transfer mediums are best for which specific transfer objects in your opinion? -
TABLE III: Survey questions - SG3
Goal ID Question Scale
ImpF01 What are your goals for SE technology transfer? -
ImpF02 How would you measure the technology transfer effectiveness on that goal(s)? -
ImpF03 Would empirical evidence of a transfer object help your decision regarding its usage? Boolean
ImpF04 Are you interested in starting a technology transfer project with measurement goals? Boolean
ImpF05 You just answered that you might be interested in starting a technology transfer project with measurement
goals after ongoing research projects (e.g. SPES-XT or ARAMiS). Could you please tell us with which
mediums and which object (pairs)?
Concl01 Are you interested in receiving the anonymized results? -
Concl02 Are you interested in conducting a short (20 mins) interview on your experience with a Technology Transfer
Concl03 Do you have additional comments? -
(Medium01). Building the list and classification of the medi-
ums was a step-wise approach, which included the following
action points:
Analysing the literature (using comprehensive tax-
onomies previously published, e.g. [5] and [21]), about
what they present on different mediums for TT (not
exclusive to the SE domain). This resulted in a list of
around 80 transfer mediums.
This list of mediums was then extended by brainstorming
workshops held at TUM and Fraunhofer IESE. Based on
this, we extended the list to 105 mediums.
Clustering the classification into three levels:
Level 1: An adaptation of the models of Berniker [4]
(used also by Pfleeger [19]): It was extended by two
new models, i.e., the cooperative and education models
on the highest level. In contrast, the rule model [28]
(which is transversal and simply means that TT is
enforced), the organisational model, and the informal
cooperation from the cooperative model were excluded
because they were out of our scope.
Level 2: Groups of medium types, each belonging to a
specific model.
Level 3: The single mediums collected in the literature
Reviewing each other’s classifications and building the
final classification.
The first two levels of the classification are shown in
Tab. IV. We provide the full list of mediums and literature
references online [27]. In a subsequent question (Medium02),
we also asked the participants which transfer mediums they
believed to be best for which specific transfer objects.
TABLE IV: Classification of mediums
Models (1st level) Medium categories (2nd level)
People-mover model [4] Personal exchange
Communication model [4] Publications
Internet resources
Conferences, workshops, etc.
Vendor model [4] Consultancy
On-the-shelf model [4] Software, systems, and tools
Cooperative model Co-working
Research cooperation
Educational model Educational programs
c) SG3 - Improvements: We collected indicators for
improving TT in the continuation of the projects. The main
focus was on the goals for TT. We asked this via an open
question for two reasons: (1) to check whether goals are
the same as motivations (ImpF01), and (2) to understand
how to support transfer activities in follow-up projects with
measurements (ImpF02 to 05).
The survey was open from 12th of March until 30th of
April 2014 and disseminated via the mailing lists of the two
participating projects. In the middle of the survey period, we
additionally sent a reminder to the same mailing lists.
In total, we collected 45 responses to our survey, but only
49% of them completed the survey. Since we spread the survey
via a project mailing lists and we don’t know the exact number
of addressees, we cannot provide a response rate. About 40%
of our overall respondents answered after the reminder mail
(n=18). Most of the non-complete responses (n=23) aborted
the survey at the beginning (Introduction: n=8; Company
profile: n=3) and in the current-state description (n=11). In the
following, we will only refer to the data set of the completed
survey (n=22). In addition to the textual description of all
the different results, we summarized all results in Fig. 2. This
offers a graphical overview of the transfer motivations, objects,
mediums, triggers, and the time to transfer.
For each investigated aspect, we present the overall results
as well as segmentation by organisation type (academia vs.
industry, C Prof02), and by organisation size (C Prof01):
large organisations, i.e., more than 500 employees vs. small
to medium, i.e., less than 500 employees. We did not split
the results into roles, qualifications, or other possible variation
factors due to the small number of answers for each category.
A. Demographics
In our respondents sample (C Prof02), 63% of participants
work in industry (n=14) and 37% in academia (n=8). All
academia respondents came from either universities or applied
research institutes such as Fraunhofer (both 18%, n=4). The
industry partners came from development companies (54%,
n=12), almost equally from research units (32%, n=7) and
business units (27%, n=6) in those companies.
Our results cover a broad rage application domains (C -
Prof03): 27% from automotive (n=6), 14% from avionics
(n=3), 23% from automation (n=5), 14% from railway (n=3)
and others. The respondents of our survey work in companies
of different sizes (C Prof01): 68% of them are working in
companies with more than 500 employees (n=15). We classify
all the others as small and medium enterprises (n=7).
The roles that our respondents hold (P Prof01) range from
requirements engineer, architect, developer, quality engineer
to testers. In addition to these roles, the sample also includes
product managers, process engineers, decision makers, and re-
searchers both from academia and industrial research units (but
mainly from the former). Based on the academic qualifications
(P Prof02), our sample is composed of 36% PhDs (n=8), 46%
respondents with a Diploma5(n=10), and 18% with a Master’s
degree (n=4).
Regarding the TT specification presented above, our sample
contains both TT roles (P Prof05) given in the TT definition:
32% (n=7) perform both roles, 59% (n=13) classified them-
selves exclusively as transferors, and 9% (n=2) exclusively as
transferees. Most of the participants performing both are from
company research units because they are transferees when
receiving technologies from outside, e.g., from academia,
and transferors when they transfer technologies inside their
company, e.g. to the companies’ business units.
5German university degree, equivalent to Master’s degree
Transferor Transferee
Transfer Medium
Time to transfer
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Academia" Industry"
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Q0R07# B470<D# I;>0:>02# SJ01# T<G4D2#
Q0R07#U$V# B470<D#U*V# I;>0:>02#U!V# SJ01#U&V# T<G4D2#U'V#
Fig. 2: Characterization of the technology transfer in the two projects studied
B. SG1: Current state of TT
We present descriptive statistics on each of the five aspects
investigated in the following paragraphs.
1) Motivation:
Overall: Among all our responses we found different moti-
vations for TT (Transfer01, Fig.2). Given that this question
was a multiple choice question, the percentage values in the
results add up to more than 100%. The 78% of all responses
listed economic factors as one of their motivations (n=14).
This is followed by strategic and personal factors with 67%
each (n=12). With 56%, operational factors are mentioned by
more than half of the participants (n=10). Social factors (39%,
n=7) and global factors (22%, n=4) were mentioned less often.
Organisation Type: Within the industry segment, all the factors
are the same as in the overall analysis, but personal factors
(n=6) are the least frequently stated one of the top four. All
(n=11) industrial participants mentioned economic factors. In
contrast for the academic perspective, personal factors were
mentioned most frequently (n=6). Economic factors as well as
operational factors are in the second to the last position (n=3).
Similar to the overall and industrial results, global factors were
mentioned least often (n=2).
Organisation Size: For large organisations, the most mentioned
frequently factors are the same four as in the overall set. The
difference is that operational,strategic, and personal factors
are on the same level (n=8). All large organisations (n=13)
mentioned economic factors. In contrast, in SMEs strategic
and personal factors are most often cited motivations (n=4).
The other factors are on a low level (n=1 or n=2).
2) Transfer objects:
Overall: Within the different transfer objects given in the TT
definition (Transfer02), there is a wide gap between three
very often used objects and the others (see also Fig. 2,
bottom left). The most frequently transferred objects belong
to the categories methods, mentioned in 89% (n=16) of the
responses, knowledge in 83% (n=15) and techniques in 67%
(n=12) of responses. The two remaining transfer objects
mentioned are processes by 28% of the respondents (n=5)
and facilities by 2% (n=1). Machines as well as equipment
were not mentioned. Some of the participants also clarified in
detail which objects are transferred (or would like to) using
the open question Transfer02:methods for safety and security
were the most frequently mentioned ones, followed by model
based development and tools or prototypes. One answer was
not taken into account because it was too general (i.e., ”want
to learn about practical problem”).
Organisation Type: The industry viewpoint shows exactly
the same distribution as the overall results. In the academic
answers, techniques (n=7) exceed knowledge as well as meth-
ods (both n=6). Processes are not that important (n=3) and
facilities were not mentioned by participants from academia.
3) Process: 80% of the respondents (n=14) answered the
open question about explicit process transfer (Transfer04),
three of them from SMEs. Most of the answers were not taken
into consideration for the following reasons: one declared the
question could not be understood; others answered by just
listing the mediums used; two participants answered that there
is no dedicated transfer because, either the case study is still in
a preliminary phase, or the case studies proposed by industry
are much more complex than the capabilities of the academic
tools (answer provided by an SME); other answers were quite
general, e.g., ”Dedicated transfer process” without further
details; another person answered citing consulting activities
but without any explicit link to the transfer process. As a
consequence, only subsequent four answers were informative
for the sake of our investigation:
”At the beginning of a research/pre-development project
an agreement on the transfer objects is signed by both
sides. Progress is continuously monitored and finally the
transferee confirms successful completion and transfer. If
and how the transfer object is used by the transferee is
not part of the transfer process.”
”Needs for action are identified in the organisation. Pos-
sible transfer objects are identified. Transfer objects are
modified, so they fit to our internal processes. Objects are
applied in pilot projects. Objects are integrated in internal
work instructions.
”I am not sure whether there is an explicit process.
Usually, a result needs to be packaged into a service.
This service is promoted by the researcher and business
areas managers. They talk to potential customers, or while
identifying customers’ needs they find that this new thing
might be a good solution. However, in most cases the
solution has to be adapted to the specific needs of the
customer.” (answer from a SME).
”This new thing might be a good solution. However, in
most cases the solution has to be adapted to the specific
needs of the customer.
A common aspect in all these answers is the importance
of customisation of the transfer objects (also via piloting) and
the absence of a well-defined step-wise process (except one
4) Time to transfer: The results about time to transfer were
already presented in our previous work [10], where we showed
that TT duration (Transfer05), although strictly dependent on
the type of object transferred (n=6), is on average three years.
In contrast to the previous publication, we look here only at
the completed questionnaires and with a greater level of details
in presenting the answers. Fig. 2 shows a smaller average,
around two years. Even if this seems to be a short duration,
the answers ranged from (several) months to a maximum of ten
years. Besides this wide range, the answer of most participants
was one year (n=5), which is even lower than the average.
Six respondents stated that the transfer time depends on
different transfer objects. In addition to this, five participants
also stated a time frame (e.g. ”1 to 5 years”).
Eleven participants considered years as an appropriate scale,
whereas three considered months. Only one respondent con-
sidered weeks. For those considering months, one stated ”three
months”, another one ”more than six months”, and the last one
”months to years”. The stated number of years ranges from one
year to five years. One year was mentioned most often (n=5),
followed by three years (n=4). Two and four years were each
stated twice and the longest time frame of five years was given
by one respondent. This results in an average of approx. 2.5
years. Out of the 18 respondents, a third (n=6) did not have
an answer.
In question Transfer06, for 39% of the countable responses
(n=7), the transfer time reported in Transfer05 is fine, while
22% would like to have a shorter transfer time (n=4) and only
1 respondent thinks that the transfer time should be longer
(this person answered Transfer05 with ”3 to 4 years”). The
respondents who are fine with the transfer time reported a
value ranging from 1 to 5 years (mode=1). The participants
who would like faster transfer reported a shorter time range
in Transfer05, i.e., from months to at most 3 years. The
remaining 33% of the respondents (n=6) did not know the
answer to this question.
In addition to the duration, we elicited how often new trans-
fer objects are transferred from one organisation to another
(Transfer07 and Transfer08). Half of the transferors reported
a frequency of at least once a month, the others at least once
a year and only one participant answered once a day. The
transferees focus more on the longer term, because 60% of all
respondents receive objects at least once a year and only 20%
at least every month or every week, respectively.
5) Triggers (only for transferees):
Overall: Triggers for technology transfer (Transfer09) come
from within the own organisation in 78% of the answers
(n=7) and from research institutes in 67% (n=6), followed by
other organisations (33%, n=3) and competitors (22%, n=2),
whereas consulting (11%, n=1) was mentioned only once.
Organisation Type: The segmentation into industry and aca-
demic partners showed that industry acquires knowledge
mainly from within their own organisation (n=5) and from
research institutions (n=3). The other three trigger provenances
were mentioned only once.
Organisation Size: The only appreciable difference to the
overall as well as to the different organisation types is that
smaller organisations get more triggers from competitors.
C. SG2: Transfer Mediums
Overall: At the bottom of Figure 2, mediums are ordered
based on their usage, which means the one with the highest
average (tagged by the black mark) regarding the usage is at
the top and the lowest at the bottom. The average of the top
three mediums is: workshops and meetings µ=4.06, personnel
exchanges µ=3.76, and publications µ=3.44, i.e., between
”often” and ”sometimes”. These top items are followed by
six mediums with an average of around 3.0 (between 3.22
and 2.94). For the remaining three transfer mediums, the
lowest one is licensing and standards with µ=1.88 and a large
gap to the others. The first preliminary analysis [10] showed
that the most frequently used mediums are human-intensive,
e.g. personnel exchange or workshop, whereas artefact-based
mediums, e.g. standards, are used less often.
In addition to the results of the usage on the transfer
mediums, we had two open questions for transfer mediums
(Transfer03 and Medium02). From Transfer03 we extracted 29
mediums overall, 18 of them classified as human intensive and
11 as artefact based. This additionally supports the findings of
the closed question (Medium01, [10]) presented above.
From Medium02, we collected the following suggestions:
Personnel Exchange, Publications, Workshops & Meet-
ings for Knowledge
Personnel Exchange, Workshop & Meetings (n=2),
Guidelines, Consultancy (n=2), Software, Systems and
Tools for Methods
Workshop & Meetings for Processes
Workshop & Meetings for Tools
We observe again the prevalence of the human-intensive
mediums over artefact-based ones (n=9 vs n=3), and we
observe that one participant considered ”tools” as an object
and not only as a medium. We did not include answers that
were too general; however, we report them because they also
support the prevalence of human-intensive mediums:
”Successful transfer absolutely requires personal contact
with the transferees.”
”Workshops and meetings are suited for all transfer
objects (no hard transfer criteria though); personnel ex-
change for knowledge, guidelines for methods”
”Depends on the audience and objective. Needs to be
decided from case to case due to the fact that more
than 10.000 employees in different countries need to be
”Consultancy for everything. Without talking to the peo-
ple, you do not get their specific problem. I am not a
traveling salesman, trying to sell my dots to who ever I
meet. We try to understand their problems, then develop
a solution, and perhaps one or more explicit objects are
Organisation Type: Looking only at the results of the
industrial companies, there are two differences compared to
the overall data set. One of them is that the guidelines are
ranked notably higher (µ=3.44) than in the overall results
(µ=2.87). The other difference is a lower value (µ=3.27 versus
3.44) for the publications.
In contrast, for academia and the ordering / importance
of their transfer mediums, guidelines are ranked as second
lowest (µ=2.0) and research cooperations (µ=3.43) as well as
conferences (µ=3.29) are notably higher than in the overall
and industry segments.
Organisation Size: The data shows that in large organ-
isations, personnel exchange, publications, workshops, and
meetings as well as co-working have high values (µ3.4). In
contrast, for the small and medium sized organisations, besides
the workshops and meetings (µ=4.23), which are ranked the
highest, nine of the remaining eleven mediums are ranked in
a small range (between 3.6 and 3.0).
D. SG3: Improvements
Out of the nine open answers we received regarding the
goals, we discarded three because they were too general (e.g.,
”Technology transfer”). The remaining six were all about
specific operational goals in SPES XT project except for one,
which was an economic goal.
The next step following the definition of the goals for
TT is the determination of measurements to check the TT
effectiveness with respect to the goals previously defined
(ImpF02). Here, the results of the participants deviate very
much: empirical studies, comparative analysis, pilot projects,
revenue, expert judgments (n=2), or a simple binary variable
on transfer. Question ImpF03 investigates whether methodi-
cally collect empirical evidence would help to support TT:
78% of the nine participants answered that empirical evidence,
such as experiments or case studies, would help in the decision
making process regarding transfer of a particular object.
Finally, only one respondent was interested in a TT project
with explicit measurement goals (ImpF04) (indicating internal
company-wide exploitation) and four people signalised their
interest in conducting follow-up interviews (Concl02).
Based on the results we reported in Sect. III, we now
induce a set of research hypotheses which constitute a first
step towards the construction of a framework to characterise
how technology transfer takes place in SE research projects
with both academic and industrial partners involved.
A. H1: The technologies developed in SE research projects
are not mature enough for direct application but need strong
customisation to fit the industrial contexts.
In our answer sample, respondents reported on the need
of customisation of the transfer object to fit the context
needs, which, paradoxically, decreases the external validity of
the technological solution. Hence, an important and practical
implication for us is to understand which techniques developed
in research projects can be adopted as they are and which
encounter barriers to the adoption so that customisation is
required (potentially as an own self-contained TT project).
We are already verifying in more detail this hypothesis by
currently conducting another survey on the barriers to the
transfer to practice. In that survey, we also conduct a partial
replication of an assessment of barriers and benefits of model-
driven development adoption in Italy [26] and we aim at
a comparison of the results in the light of this research
hypothesis to further scale up to practice.
B. H2: The standard model of representing TT as a transac-
tion of an object that occurs from a transferor to a transferee
does not fit reality.
An interesting and at the same time challenging aspect that
emerged from the answers in the survey concerns the roles
involved in TT. Many of our respondents identified themselves
on both sides, i.e. as transferors and as transferees. The model
we have used, although with some simplifications, is based on
(and coherent to) currently used models and taxonomies from
literature (e.g., [21] and [5], and for further details see [10]).
However, we believe that this way of modelling TT is not
precise enough to capture the complexity of the interactions
involved in the transfer. For example, Aoyama et al. [1] created
a more complex model that involves also intermediate roles
(”brokers”) and feedback loops; but even with this refinement,
roles are still too static and linked to the metaphor of transfer,
which might be obsolete. For instance, a better concept might
be found starting from the idea of ”Knowledge Circulation”
which is used by European institutions (see, for instance, [11,
pp. 121-199]). Further empirical evidence in favour of this
hypothesis could motivate the need of a more precise and
specific model to SE research, and even a new metaphor or
concept which goes beyond the idea of a ”transfer”. Such a
model can serve as a conceptual framework to provide better
support for same or similar studies.
C. H3: Technology transfer takes place without an explicit
Another important point revealed by the answers to the
survey is the lack of explicit processes for TT going beyond
the formalities and best practices linked to the setting of
establishing research collaborations between academia and
industry (see, for example [15]). Also, some respondents
reported informal ways to conduct a transfer; we know, for
example, from literature that informal transfer occurs more
likely in case of spatial proximity [12]. Based on this initial
data we collected, we refined and extended our mediums
classification (see Tab. IV). In detail, we added the category
Informal transfer to the model, and added also five third-
level mediums, which were reported in the open question, i.e.
web meetings, presentations, talks, movies, and coaching. In
addition, the answers to the frequency of the transfer reveal
that TT is not perceived as a continuos process especially
by the transferees. In SE research, an example for complex
process for TT is the one of Gorschek et al. [13]. In their 7-step
process, the validation of the solution plays an important role,
both in terms of in-vitro experiments in an academic set-up as
well as in-vivo through piloting in real industrial contexts.
However, a key question arises here: ”What could be the
benefits of having a dedicated and formalised TT process?”.
Therefore we believe that it is important to understand whether
other research projects have an explicit process for TT, and
what are the consequences of having (and applying) / not
having it.
D. H4: Industrial organisations gain new knowledge mainly
within their own confines.
Additionally, we have seen that triggers for new ideas for
industrial participants come mostly from within the organi-
sations’ own confines. This is a surprising fact, given that
there is evidence that external knowledge has a positive impact
on product development and innovation (see, e.g. [16], [23]).
By looking at the small and medium-sized organisations, the
triggers come more often from competitors ideas. An expla-
nation of the latter point could come from the Gatekeeping
theory [18] and its recent evolutions [2], which state that
knowledge in smaller organisations might have to traverse
fewer gates and channels. We believe that this hypothesis
should be further investigated in multiple settings to verify
when and why it happens, and which are the implications for
the success of a transfer.
E. H5: Most used transfer mediums are human-intensive.
As we already discussed in our previous publication of the
preliminary results [10], human-intensive mediums are used
much more often than artefact-based ones. However, when
looking at the variation factors, guidelines, which are artefact-
based and developed on purpose for the projects [14], are used
more often by the industry partners. Finally, it is relevant to
report that patents and standards, despite their widespread use
as a measure of the effectiveness of TT, are used less often
in these projects, which is in tune with what seems to be
happening in the IT sector [24]. In our current survey on the
barriers to the adoption of the SPES XT technologies, we are
also investigating this hypothesis by looking at the usage and
effectiveness of the different mediums to understand which
are the best possible strategies to disseminate the results for
different stakeholders and in different contexts.
F. H6: The motivations that drive the transfer in industry and
academia are heterogenous.
We could observe that the motivations for transfer heavily
differ between industry and academic organisations; as ex-
pected, however, with prevalence of economic and strategic
motivations in the first case, and more personal reasons
(e.g., intellectual growth) in the second case. In both cases,
motivations are different from the goals, which are mainly at
operational level. These cultural differences between academia
and industry are historically known as the problem of the ”The
Republic of Science vs The Realm of Technology” [8], which
can create barriers to collaboration and limit the transfer suc-
cess [7]. One example is the diversity of the rewards systems
and practices of disclosing results in the two sectors [24].
Supporting or rejecting this hypothesis also in the SE field
is important to leverage as much as possible the motivations
of the research partners.
G. Revised definition for Technology Transfer
Based on our observations and follow-up stated hypothesis,
we reworked the original definition of TT [25] resulting in a
new one, which should fit better what we expect to hold for
SE, also in relation to the aspects investigated. Tab. V shows
the (re-)definition. The most important differences with the
original definition are:
1) We introduced the concept of sustainable adaptation of an
object by a technology recipient for a specific purpose,
to stress the need of adoption (tailoring and usage) in a
specific context (see H1)
2) We removed the concept of a transaction between a
transferor and a transferee in favour of a more generic
sharing and developing of a technology object between
actors, to add flexibility and reflect better reality. Please
note that the technology recipient from previous point can
be external to the actors (see H2,H3,H4)
3) We made the objects6and the mediums7(see H5) explicit
This new definition has not to be intended as final but as a
starting point to be refined with the verification of the research
hypotheses we have set up, or the verification of additional
ones from independent assessments.
Internal Validity. The simplified model used for representing
TT permitted reducing the risk of different mental models
among participants, thus reducing the corresponding threats.
However, it does not fully capture all the complex interactions
between transferors and transferees and the dynamics of the
whole TT process (as the imbalanced distribution of TT
roles suggests), by, e.g., using an outdated linear model for
technology push. We tried to mitigate this threat in two ways:
with an explicit (optional) open question for describing the
process8and by collecting a comprehensive list of medi-
ums [27] of which some might break the linearity of such
model (e.g. ”research cooperations”’). Also, we started a more
systematic literature review of the existing TT models with the
aim to improve them with a more comprehensive conceptual
framework in future work. This is even the case why we
revised the definition of technology transfer in SE.
Another important threat is represented by the low response
rate. Given that only 22 participants filled out the question-
naire, we might have missed some information, and answers
might not be fully representative of the actual transfer in the
projects. However, we do not exactly know what our target
population is, i.e., the number of participants in ARAMIS
and SPEX-XT who are actively involved in transferring the
developed techniques. We could only control this threat in
the current study in the way of sending one reminder to the
mailing list, but we aim at improving the response rate with
the follow-up survey by specifically contacting the different
project partners.
External Validity. We are aware of the fact that TT is a
process that can largely vary from case to case in different
contexts. Therefore, the scope of validity of this survey is
limited to the two German national projects ARAMIS and
SPES XT, and more specifically to the area of model-driven
development for embedded systems covered by these two
projects. Nonetheless, the focus of this survey was to generate
testable hypotheses which can be already used to steer future
research in that direction. These hypotheses can then be
also used to be validated in different other domains to get
generalizable results.
In this study, we undertook a first step towards the con-
struction of a framework to characterise how technology
transfer takes place in SE research projects with both academic
6In SE, the objects are knowledge, methods, techniques, and processes
7The classification of mediums [27] can be used as initial list of mediums.
8However, we did not find any structured process.
TABLE V: Redefining Technology Transfer
Technology transfer is the process of sharing...
(Old definition) (New definition)
knowledge, machines, equipment, methods, tools, techniques, processes, or developing a technology object between two or more
and facilities with the aim of facilitating accessibility of scientific actors via one or more mediums so that the technology
and technological developments from primary discoverers or transferors recipient sustainably adopts the object in the recipient’s context
to potential users or transferees/recipients, who will exploit the in order to evidently achieve a specific purpose.
technology into new products,processes, applications, and business models.
and industrial partners involved. To do that, we studied the
technology transfer in two large German research projects
on model-driven SE technologies in the context of embedded
systems. Taking as a reference a basic conceptual model of
technology transfer, we observed how industry and academia
interacted in the transfer of the developed technologies. Due
to the exploratory nature of the work and to the low number
of respondents, our main goal was no to find statistically
significant results, but rather to extract from our observations
a set of hypotheses as a basis for follow up work, also in
the sense of a collaboratively effort of the community to
fill the gap of poor empirical evidence on the assessment of
technology transfer in SE research. Therefore in our study, we
also identified and discussed some issues when applying the
traditional definition of TT to SE and finally created a new
more SE-specific version.
Our next step is to verify the research hypotheses formulated
during the results analysis in follow-up studies. Necessary
condition for such a goal will be to break them down into
statistical hypotheses, testable through empirical studies. We
would like also to extend the initial set of hypotheses with the
contribution of other researchers.
The authors would like to thank all participants to the
survey. They are also thankful to Wolfgang Boehm for his
precious contributions, and to Marcello Urgo and Silke Stein-
bach for their valuable feedback.
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Research transfer projects should be beneficial and inspiring for both, the academic as well as the industrial partners. If the setting is inadequate they can, however, also be a source of frustration and a waste of time and money for all parties. In the last decade, the Chair of Software and Systems Engineering at Technische Universität M ¨ unchen (TUM) participated in a series of eight research transfer projects, conducted jointly with the re-insurance company Munich Re. The common theme of these projects has been quality of software development artefacts. This cooperation has been exceptionally productive for both sides. Results of this continuous success are, for example, a university spin-off, a considerable number of publications, as well as more systematic and improved methods in software engineering at Munich Re. A corner stone to the fruitful cooperation has been the model of how the university and practitioners have been working together. In this paper, we look at the cooperation from the retrospective and identify a number of basic principles that contributed to the success of the cooperation. Aditionally, we illustrate our research process and approach which helps to realize these principles.
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Background: Particularly during and after research projects, technology transfer into practice plays an important role for academia to get technologies into use and for industry to improve their development. Objective: Our goal was to gain more and current knowledge about how technology transfer from software engineering (SE) research into industrial practice is accomplished best and how to measure the effectiveness of this transfer. Method: We conducted a study in the context of two German research projects, covering many different organizations from industry and academia. Results: This paper presents the design of the study and the survey performed. After introducing the concept of technology transfer we used and adapted, we present preliminary results. Conclusions: We observed that traditional means such as meetings or workshops are still the most widely used mediums for technology transfer in SE. We also discovered that, even though the duration of transfer depends on the object being transferred, the average duration is three years, which is far less than previously published (~18 years).
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Abstract: It takes between 15 and 20 years to achieve widespread implementation of recent technologies from research to practice. Guidelines have shown to be an adequate method for efficiently transferring technology into an industrial context, especially in software engineering. However, recommendations for writing guide-lines are still fuzzy w.r.t. content and structure, i.e., existing approaches do not give comprehensive recommendations on how to write meaningful guidelines. In this paper, we propose recommendations for writing guidelines. These recommen-dations include a reference structure that supports the author in writing guidelines by providing guiding questions for each chapter of a guideline. The recommenda-tions are based on requirements that were elicited from leading companies in dif-ferent industry domains. It was initially evaluated in a prototypical guideline in-stantiation by one of our industry partners.
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Background: The mobility domains are moving towards the adoption of multicore technology. Appropriate methods, techniques, and tools need to be developed or adapted in order to fulfill the existing requirements. This is a case for design space exploration methods and tools. Objective: Our goal was to understand the importance of different design space exploration goals with respect to their relevance, frequency of use, and tool support required in the development of multicore systems from the point of view of the ARAMiS project members. Our aim was to use the results to guide further work in the project. Method: We conducted a survey regarding the current state of the art in design space exploration in industry and research and collected the expectations of project members regarding design space exploration goals. Results: The results show that design space exploration is an important topic in industry as well as in research. It is used very often with different important goals to optimize the system. Conclusions: Current tools provide only partial solutions for design space exploration. Our results can be used for improving them and guiding their development according to the priorities explained in this contribution.
Software architecture design decisions are key drivers for the success of software systems. Despite awareness for their criticality, software architects often rationalize and document their decisions poorly. On this behalf, ABB Corporate Research initiated a technology transfer project to integrate an architecture decision framework from the University of Groningen into ABB software development processes. The project involved close communication between university researchers, industry researchers, and ABB software architects and resulted in the implementation of a plug-in for the UML tool Enterprise Architect. This paper summarizes success factors for the technology transfer, such as strong buy-in from the stakeholders, short feedback cycles, and seamless integration into existing tool-chains.
Over the past twelve years the aim of Sweden’s Technological Systems (STS) project has been to identify the role of technology in economic growth. While the importance of technology is generally accepted, its role in the economic growth process continues to be only partially understood. The interdependencies between technological change and economic growth become particularly important when the rate and scope of technological change increase. Under these conditions there is a risk that the institutions, policies, and organizations, as well as the concepts and perceptions on which they are based, become obsolete.
There is abundant evidence that research collaboration has become the norm in every field of scientific and technical research. We provide a critical overview of the literature on research collaboration, focusing particularly on individual-level collaborations among university researchers, but we also give attention to university researchers’ collaborations with researchers in other sectors, including industry. We consider collaborations aimed chiefly at expanding the base of knowledge (knowledge-focused collaborations) as well as ones focused on production of economic value and wealth (property-focused collaborations), the latter including most academic entrepreneurship research collaborations. To help organize our review we develop a framework for analysis, one that considers attributes of collaborators, collaborative process and organization characteristics as the affect collaboration choices and outcomes. In addition, we develop and use a “Propositional Table for Research Collaboration Literature,” presented as an “Appendix” to this study. We conclude with some suggestions for possible improvement in research on collaboration including: (1) more attention to multiple levels of analysis and the interactions among them; (2) more careful measurement of impacts as opposed to outputs; (3) more studies on ‘malpractice’ in collaboration, including exploitation; (4) increased attention to collaborators’ motives and the social psychology of collaborative teams.