Available online at www.sciencedirect.com
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Procedia Technology 16 (2014) 138 – 149
CENTERIS 2014 - Conference on ENTERprise Information Systems / ProjMAN 2014 - International Conference on Project MANagement / HCIST 2014 - International Conference on Health and
Social Care Information Systems and Technologies
“I am an Engaged Scholar”: a typology of
IS researchers’ engagement in research with industry
Petra Schuberta *, Thomas Kiliana, Niels Bjørn-Andersenb
a
University of Koblenz-Landau, Universitaetsstrasse 1, Koblenz 56070, Germany
b
Copenhagen Business School, Howitzvej 60, Frederiksberg 2000, Denmark
Abstract
This paper addresses the topic of University-Industry Collaboration (UIC) in the academic discipline of Information Systems.
The term UIC is used to describe the active engagement of an industry partner in a joint research project with academics. The
objectives and motivations of UIC have been discussed widely and controversially in the literature. In our study, we were particularly interested in the factors that influence the setup of research projects. We started with an online survey in which we explore
the influence of country of origin. The findings encouraged us to theorize about existing types of research engagements (which
we define as UIC archetypes) related to the personality and the surrounding value system of the researcher. From the literature
and our findings we developed an a priori framework of UIC archetypes which was then tested and refined using data from
interviews with “successful” researchers. The findings show the characteristics (personality traits) and the influencing factors that
shape the UIC archetypes. It will be interesting to see if the findings are meaningful to our readers and whether they can identify
themselves from these UIC archetypes.
© 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
© 2014 The Authors. Published by Elsevier Ltd.
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Organizing Committees of CENTERIS/ProjMAN/HCIST 2014
Peer-review under responsibility of the Organizing Committee of CENTERIS 2014.
Keywords: Industry; Collaboration; Engaged Scholarship, Research and Development; Design Science
* Corresponding author. Tel.: +49 261 287-2525; fax: +49 261 287-100-2520.
E-mail address: [email protected]
2212-0173 © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/3.0/).
Peer-review under responsibility of the Organizing Committee of CENTERIS 2014.
doi:10.1016/j.protcy.2014.10.077
Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
139
1. Introduction
There are many facets to the topic of university-industry (UI) collaboration. Publications in this field discuss
manifold questions such as “How can we ensure the transfer of innovation from academia to industry (for the benefit
of the national economy)?” [5],[2],[24], “What is the right incentive policy that Universities should set for researchers?” [17], “How can we manage the cultural differences between academia and industry?” [16],[33]), “How can we
overcome the existing barriers between University and Industry?” [10],[6], “The role of consulting in IS research?”
[30], “How much confidentiality can the industry partner ask for (expressed e.g. in publication bans)?”, “How can
we make sure professors do not become consultants and students are not used as cheap labor?” [17].
Against this backdrop, the purpose of this paper is to contribute to the understanding of UI collaboration within
the discipline of Information Systems (IS). We investigate differences among countries along with an analysis of
factors that influence the decision of an IS researcher about whether or not to engage in joint projects with industry.
We draw our research data from questionnaires and interviews on the basis of real-life experiences of IS researchers.
The examination of influential factors includes e.g. attitude, perceived benefits, problems, funding, methods, output,
relationship and setup of the project.
The motivation for our long-term study on IS researchers’ engagement with industry was intensified by specific
discussion streams in recent issues of IS journals and IS conferences. Among these are the following:
The provocative opinion piece “Why the old world cannot publish?” [18],
the debate about the publication chances of design science articles in journal publications [23], [3],
the value from research funding that stakeholders and society as a whole are deriving [8],[9],[11],
the stream about the concept of “Engaged Scholarship” which is meant to address the alleged gap between theory
and practice (“knowledge production problem” [35], p. 802),
x the Scholarship of Engagement, a movement reacting to the disconnect between academics and the public
[5],[25],[2],
x the pros and cons of collaborative research endeavors such as interactive social science [21],[30],[7],[33],[12] or
Co-operative Inquiry [13].
x
x
x
x
We argue that in principle, UI collaboration can address all of the above thematic challenges, but we accept that it
differs substantially depending on academic traditions and socio-economic settings due to different sets of internal
and external challenges. We believe that we can learn from IS researchers who have found a way of overcoming the
perceived barriers and who see the cooperation with industry as an important ingredient to their research success.
In this paper we are addressing the following research questions:
1. Are there differences in the propensity to engage in UI collaboration in different countries of the world?
2. What are the factors that characterize successful UI collaboration?
3. What are typical profiles (UIC archetypes) of IS researchers who are in favor of UI collaboration?
Note that by “successful UI collaboration” we understand successful from the view of the person conducting the
collaboration project. This “success” is dependent on the person’s aims and values and thus usually influenced by
the cultural setting and the reward scheme of the surrounding research environment.
The remaining paper is structured as follows: in the next section, we review the literature which led to our research questions and our a priori model of factors and archetypes. We explain the research steps of our longitudinal
research program and present the results of an international survey on the experience with UI collaboration in different countries. The main section presents the analysis of our in-depth interviews and the testing and refinement of our
framework. We conclude with an outlook on future research and discuss limitations of our current findings.
2. Literature Review: Theme Analysis
Before engaging in a study of UI collaboration it is necessary to define the term more clearly. In our understanding “university-industry collaboration” describes a research activity performed by a group of people containing aca-
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demics and practitioners. The research is carried out together (collaboratively) or, as Heron and Reason [13] aptly
put it, as research “with” rather than “on” people. In doing so, academics and practitioners are co-constructing
knowledge [12]. The practitioners in a company or government agency are engaged in the research process – they
are not mere study objects. Accordingly, a study of the impact of a particular technology in an organization common
to behavioral studies would not qualify as research collaboration in our definition. We are investigating research
projects that are carried out as a joint work between researchers in universities and practitioners in companies or
government agencies.
The engagement can occur at all stages, from the definition of the research question and the development of the
research design, to the actual research work and the interpretation of the findings. Pettigrew points out that it is essential that the practitioners are involved early in the research process in saying “The action steps to resolve the old
dichotomy of theory and practice were often portrayed with the minimalist request for management researchers to
engage with practitioners through more accessible dissemination. But dissemination is too late if the wrong questions have been asked.” ([25], p. 67). The following sections present and discuss some of the themes in the literature
that are related to UI collaboration.
2.1. Scholarship of Engagement
The idea of collaboration between university and industry is not a new one. Boyer [5] coined the term “Scholarship of Engagement” (also see [2]). While the current measures of research are the number of academic journal
publications, and to some extent the number of citations, there is a growing demand in industry and society for research metrics showing the benefits to key stakeholders, students, industry and society at large.
2.2. Engaged Scholarship
Van de Ven took up Boyer’s idea and published a book entitled “Engaged Scholarship” [34]. In his book he describes a research methodology for participatory research with stakeholders. The content is concerned with bridging
the knowledge gap and engaging practitioners in parts of the research process. However, his work does not talk
about academic-industry collaboration as such, and it does not provide practical guidance on the necessary organizational framework for such projects.
2.3. Real engagement through collaboration
Whilst Van de Ven [34] acknowledges the engagement of practitioners to ensure a certain degree of relevance
and practicability in the research he does not explicitly argue for collaboration with an industry partner over the
period of a research project with a defined outcome. There are, however, successful forms of collaboration, which
have been described in the literature, e.g. Collaborative Basic Research [29] or Consortium Research [22]. Such
forms of direct collaboration can vary depending on scope (number of parties involved, project amount), length
(time in months/years), initiator (research initiated by university or industry), research object (artifact, process,
data/information, behavior, attitudes) and research outcome (software, technology component, method, report).
We included these influential issues in the design of our interview guideline. We argue that collaboration can address many of the before-mentioned problems by engaging the practitioner in the research process as also argued by
proponents of field work such as Schein [27] or Whyte [37].
2.4. Drivers for engagement: successful publications
It has been argued that engagement with industry can lead to successful academic publications if well presented
and carried out in a rigorous way [3],[31]. Research with industry, when using a suitable research method, can be a
very valuable basis for evidence-based research following well accepted paradigms for how to deal with data, measurements, observations, testing, and validation. This perception is in accordance with statements from the provocative opinion piece “Why the old world cannot publish?” [18], in which the authors acknowledge a strong industry
engagement of European Ph.D. students but criticize their lack of rigor in their academic writing. They say: “Many
Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
141
times, Ph.D. theses are produced to address practical problems within industry; for example, innovative workflow
designs or modeling methods.” ([18], p. 323) They further argue that these projects often end when the industry
partner is satisfied with the research outcome, which is often a product, some software or a method without scientifically reporting the results in journal articles.
The analysis of our interviews showed that collaborative research can indeed lead to high-quality articles if the
academic requirements are part of the research design. Rigor and relevance can be improved by raising substantive
evidence data and monitoring the research process on a meta-level, which might seem superfluous for the industry
partner but is essential for high-quality research publications. It is a prevailing criticism that design research projects
often fall short of the last phase, the validation of the artifact in actual practice (Hevner el al. 2004), but we argue
that there is no inherent reason why such research activities should not be included to meet academic requirements.
2.5. Barriers to engagement
Schubert and Fisher (2009) identify a number of factors that impede collaboration between practitioners and academics from both of their respective points of view. Among the barriers for industry they mention (1) unclear relevance of research findings to industry [15], (2) lacking knowledge and interest in designing the research instruments
[1], (3) lack of access to research results (academic journals not attractive for practitioners), (4) different timescales,
(5) different expectations from the research outcomes as well as (6) disagreement on intellectual property rights.
For the academics they mention a belief that (1) industry is not interested or willing to work with universities
[11], the (2) rather long timeframe for academic research products [25] and the (3) tedious maintenance of relationships with industry partners over a long period of time [1]. Schubert and Bjørn-Andersen [28] tested the barriers for
academics in a series of interviews and found some of them confirmed. Aspect three, the tedious building up of trust
was confirmed by multiple respondents, one of them e.g. saying: “Research collaboration takes a lot of time and the
mutual understanding and trust increases over time. It is an investment that you have to make.” An experienced EU
project participant remarked: “There are path dependencies due to different expectations in different countries. You
have to identify overlapping interests with your research partners and build up social capital over time.” This assessment is shared by Van de Ven and Johnson” ([35], p. 812) who say: “Time is critical for building relationships
of trust, candour, and learning among researchers and practitioners”.
3. Research Approach and Steps
We followed a five-step approach to develop our typology (c.f. Fig. 1). In the first phase we conducted a structured literature review (cf. [36]) applying a thematic analysis [4] searching for keywords describing the concept of
“university-industry collaboration”. We included the following electronic databases in our search that were available
to us: ACM Digital Library, SpringerLink, Wiley Online Library and the EBSCOHost cross-database interface (including e.g. Business Source Complete, Communication and Mass Media Complete, and SocINDEX). We applied a
snowball technique in those articles that matched our search. An excerpt of our literature review is contained above
(c.f. section “Literature Review: Theme Analysis”).
We then used the results from the literature review to develop a short online questionnaire which was directed at
IS researchers and designed to give us an impression of their attitudes and experiences regarding UI collaboration.
The questionnaire is available from the authors on request.
In the second step, the survey was first piloted and then hosted online from March 15 to April 5, 2012. A series of
invitation e-mails were sent to well-established IS lists in order to ensure a good coverage of the community (e.g.
mailing lists of the following associations: AIS (US-based, worldwide), IRIS (Scandinavia), WI (Germany)). The
invitation yielded 215 (mostly) completed questionnaires.
The analysis of the questionnaires that we performed in the third step showed patterns in the attitude of the respondents and allowed us to theorize about the existence of different IS researcher profiles (which we call “UIC
archetypes”). The results of the quantitative analysis are presented in the next section (c.f. section “Results of the
Quantitative Survey”). Based on the findings from the short questionnaire we developed an a priori typology of
factors and “typical” researcher profiles which will be described in one of the following sections of this paper
(“Towards a Typology of UIC Archetypes”).
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In the fourth step we deployed an additional qualitative approach in order to further explore and possibly validate
our UIC archetypes. We selected prominent IS researchers with a track record that reflected an active engagement in
UI collaboration. In order to test our typology, we developed a semi-structured interview guideline (a questionnaire
with closed and open questions). The constructs of the interview guideline allowed us to test our assumptions. We
conducted interviews with our selected test candidates at two major IS conferences (AMCIS and HICSS) and in
video conferences. We purposefully picked researchers whom we assessed could be profiled according to our archetypes. In total, we conducted eight personal interviews in the years 2012 and 2013, each interview was around 60
minutes in length. We recorded and fully transcribed all the interviews.
Step 1: DATA COLLECTION:
Structured Literature Review
Literature
analysis
Development
questionnaire
Step 2: DATA COLLECTION:
Survey (Online Questionnaire)
Open
invitation to
IS community
Step 4: DATA COLLECTION:
Interviews (Interview Guideline)
Selected
interviews (6)
Independent
analysis
researcher 1
Independent
analysis
researcher 2
Step 3: ANALYSIS:
A priori framework archetypes
Collection of
questionnaires
(215)
Quantitative
analysis
Development
a priori
framework
Step 5: ANALYSIS:
Preliminary framework archetypes
Discussion &
agreement
on findings
Testing and
refining of
framework
Preliminary
framework
archetypes
Fig. 1. 5-step research process
In the fifth and last step, the transcripts were examined to validate (or falsify) our a priori framework (c.f. Table
6) and to further explore the terminology in our target area. The terminology and semantics had been purposefully
discussed with the respondents during the interviews. The interviews were coded using open and schematic coding
[20],[26]. The result of the coding process and the discussion of the findings are contained in the section “Findings
from the interviews: the current UIC archetypes” of this paper.
4. Results of the Quantitative Survey
As outlined in the previous section we conducted a quantitative survey based on a short questionnaire with the
aim of drawing a general picture on the state of UI collaboration in the field of IS. The questionnaire consisted of
two parts. In part one, demographic information (country of work, current position and research area) was collected
and researchers were asked if they had collaborated with industry in their research in the last five years and if so, in
how many projects. Table 1 shows that 39.1% of the respondents did not participate in industry-university collaboration in the last five years. The proportion of researchers not collaborating is especially high in the USA and North
America (49.4%) and in “other countries” (56.7%) compared to European researchers (overall 27.3%, sum of Scandinavia, DACH, and other Europe).
The overall results show that the disposition for industry-collaboration is significantly higher in European countries compared to the USA/Canada and other countries. These findings confirm the expectations that we had developed from the literature (c.f. especially [23],[3]). The group of “Other” contains the following countries: Australia,
China, Egypt, India, Israel, Lebanon, Mexico, New Zealand, South Korea, Singapore, South Africa, Sri Lanka, Turkey, and United Arab Emirates. We assume that our sample contains a bias in favor of active collaboration because
researchers with an interest in collaboration (i.e. the ones active in UI collaboration projects) are more likely to par-
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Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
ticipate and complete the questionnaire. However, we were surprised about the large proportion of respondents that
had not been actively engaged in UI collaboration (UIC) in the last five years.
Table 1. Collaboration by Countries Subsample (n=215)
Collaboration
No
Country Coded
Sum
Yes
USA and North America
38 (49.4%)
39 (50.6%)
77 (36.4%)
Scandinavian Countries
18 (28.1%)
46 (71.9%)
64 (29.5%)
DACH
5 (25%)
18 (75%)
23 (10.6%)
Other European Countries
6 (28.6%)
15 (71.4%)
21 (9.7%)
Other Countries
17 (56.7%)
13 (43.3%)
30 (13.8%)
Total
84 (39.1%)
131 (60.9%)
In the second part the UIC-active researchers were asked to think about their most successful collaboration project in the last five years and, with this in mind, to indicate how many university and industry partners were involved
and how the project was funded. Hence, the second part was only answered by a subsample of 131 researchers who
had participated in collaborative projects in the last five years. The subsample is described in Table 2.
Table 2. Description of the Subsample (n=131)
Country (of Work)
Position
Research Area
No. of Collaborative Projects
USA and North America
Scandinavian Countries
DACH
Other European countries
Other countries
Full Professor
Other Professorship
Lecturers
Researcher
Other
Information Systems
Information Management
Others
1
2
3
4
5
6–9
more than ten
n=131
39 (29.8%)
46 (35.1%)
18 (13.7%)
15 (11.5%)
13 (9.9%)
63 (48.1%)
41 (31.3%)
10 (7.6%)
15 (11.5%)
2 (1.5%)
112 (85.5%)
9 (6.9%)
10 (7.6%)
22 (16.8%)
30 (22.9%)
28 (21.4%)
15 (11.5%)
11 (8.4%)
8 (6.1%)
17 (12.9%)
Researchers from Europe account for approximately 60% of the UIC-active sample. Nearly 80% of the respondents hold a professorship and their research area is predominantly Information Systems. Thus, we believe the sample
is if not representative at least substantive enough to serve as a starting point. The number of collaborative projects
differs widely; however, the majority of the UIC-active researchers has conducted one to three projects in the last
five years (61%).
Next, we asked for the number of universities and companies involved in the most successful collaboration project of the last five years (Table 3). The successful projects had an average of 1.9 (SD=1.35) universities and 4.93
(SD=6.53) industry partners. This suggests that most of the projects that are ranked as “successful” are run by one
university but that some of them include more than one industry partner. On closer examination of the data the “industry consortium”, a setup of 1:m, seems to be a popular model. In the following, we are using the classification of
UIC research by [28].
1:1 research was carried out in 37 successful projects (bilateral projects). 34 successful projects were conducted
with one university and many companies (industry consortium, 1:m). In 15 projects more than one university col-
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Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
laborated with only one company (university consortium, n:1) but the majority of projects adopted a n:m-approach
(research consortium, 42 projects). These findings are complemented by the type of funding shown in the Table 4.
Table 3. Number of universities and companies involved in the most successful collaborative project
No. of Universities
Number of companies
1
2
3
>3
1
37
9
4
2
2
8
4
2
2
3
3
1
3
1
>3
23
15
5
9
n=126 projects (sample is smaller than in Table 2 because of some missing values)
The most common setup of an industry collaboration project in our sample are the following four different forms
1) “n:m, co-funded by companies” with 27 projects, 2) “1:m, co-funded by companies” (17 projects), 3) “1:1, 100%
funded by companies” (14 projects) and 4) “1:1, co-funded by companies” with 13 projects. Only seven projects are
not at all funded by the companies but by government grants or the researcher’s own University. Explanation for
these figures was provided during the in-depth interviews: The respondents were in agreement that “government
grants” was their preferred source of funding but they also claimed that it was also the hardest to get. In some cases
co-funding of companies/government agencies is even a prerequisite to receive complementary national research
grant funding.
Funding Type
Table 4. Cross-Tabulation: Funding Type and Project Partners
1:1
1:m
n:1
100% Companies
14
4
100% Grant
1
5
Sum
3
4
25
1
0
7
100% Own
4
7
1
1
13
Co-funded by
Companies%
13
17
9
27
66
Not Funded by Companies
Sum
n:m
2
3
1
1
7
34
36
15
33
118
n=118 projects (sample is smaller than in Table 2 because of some missing values)
Using the classification provided by [28] (p. 10) this would indicate that many of our respondents have been involved in EU Framework-like programs, where multiple Universities are funded by public grants while industry
partners contribute money and resources to the project costs.
The second and third most popular combination in the matrix (30 projects altogether) show projects that are run
by one University with one or more companies. In almost all these cases companies have to pay a contribution to the
project cost (exception of only 7 cases). It is interesting that there is no most favored configuration for the composition of number of university and industry partners. The figures are almost evenly spread for 1:1, 1:n and n:m. The
combination of only one company working with many universities is less frequent (15). It is noteworthy that these
results are in complete accordance with a previous study by [28] which was based on a different sample of IS researchers. Table 5 provides interesting insights into the funding type in the different countries.
The highest rate of industry funding can be found in UI research in German speaking countries, the lowest in
Scandinavian countries where all funding types are more or less equally important.
To summarise the findings of the survey: 38.7% of the respondents of this study did not conduct projects with industry partners in the last five years which we thought was a high number assuming the bias of UIC-active researchers in this survey. Of the remaining 61.3% the majority (61%) has conducted only one to three projects in the last
Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
145
five years. These figures seem plausible as there is a capacity limit to how many projects with industry one researcher can engage in. Considering the most successful projects in terms of participants there are two different
setups that seem to be favoured: 1) A fairly low complexity approach (1:1 research in 37 successful projects, and
1:m in 34 successful projects) opposed to a 2) high complexity approach with 42 successful projects in a n:m setting.
Table 5. Number of universities and companies involved in the most successful collaborative project
Country Coded
1
Mean of funding
from industry (%)1
Mean of funding from
funding grant (%)2
Mean of funding from Universities
and Research Institutions (%)3
USA and North America
51,42
12,11
35,16
Scandinavian Countries
30,64
35,01
33,46
DACH
71,56
16,94
12,94
Other European Countries
45,38
35,77
21,25
Other Countries
56,67
22,00
22,86
p=0.003; 2 p=0.009; 3 p= 0.176
These findings led us to the conclusion that different UIC-active researchers have different profiles. The n:m approach represents large, complex, possibly interdisciplinary projects with a need to openly discuss research ideas
and results. The 1:1 approach might represent small projects in which confidentiality (protection of the interest of
one company) is required. Both of these profiles are represented in our list of UIC archetypes (see below).
The most common funding type is co-funding by industry followed by full funding by industry. Finally, in Germanspeaking countries the proportion of funding by industry is highest.
5. Towards a Typology of UIC Archetypes
The following sections present a classification of researcher profiles according to the type of research they engage
in and their attitudes towards collaboration. According to the dictionary, the word “archetype” refers to a “perfect
example” of a thing [19]. We use this term to characterize typical profiles of IS researchers who are actively pursuing UI collaboration.
5.1. The A Priory Model of the Seven “UIC Archetypes”
From our previous research [28] and the results from the quantitative survey we developed a list of influencing
factors and an a priori framework of researcher profiles which we call “UIC archetypes”. The factors are the following:
1.
2.
3.
4.
5.
6.
7.
organization size (large/small)
type of organization (company/government agency)
source of funding (private/public grant)
number of industry partners (1/many)
number of university partners (1/many)
orientation: loyalty of engagement (short-term/long-term)
preferred output (method/artifact/information)
- Method: the need to structure something, e.g. procedural models, processes, guidelines
- Artifact: the need for a socio-technical system (e.g. software)
- Information: the need to know something (knowledge generation).
Table 6 shows our expectations of the prevalence of these factors for different researcher profiles (archetypes).
Dark grey areas indicate fields in the matrix that are typical for the archetypes. Lighter grey indicate that this attribute is sometimes encountered. White areas show that these attributes are unlikely to occur for the respective archetype. As an example: The “Top-100 Company Researcher” is characterized to work with large private companies,
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Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
receive financial support from industry only, collaborate mostly in 1:1 setups, focus on rather short-term engagement with a partner (to the extreme of “one time only”) with a typical output of “information” (e.g. project reports)
but with methods and technology artifacts also being an option.
Table 6. A priori model of UIC archetypes, influencing factors and attributes
Factors
Archetype
Top-100 Company
Researcher
Entrepreneurial
Researcher
Industryfunded Consortium
Researcher
Governmentfunded Consortium
Researcher
Design
Researcher
Relationship
Researcher
Size
Large
Small
Type
Private
Funding
Gov. Private Grant
# Organ.
1
Many
# Unis
1
Many
Orientation
Short Long
Output
Meth
Artifact
Info
Based on the above factor list, we developed the interview guideline which included additional criteria that extended the original factor list. The interviews were meant to be not only confirmatory but also exploratory. Open and
closed questions were posed to the interviewees. In the interviews the respondents were invited to talk about their
experiences, their attitudes and were asked for explanation on choices they had made for the setup of their past UI
collaboration projects.
5.2. Findings from the interviews: the current UIC archetypes
As mentioned earlier the interviews were conducted at the end of 2012 / beginning of 2013 with proponents of UI
collaboration at two major IS conferences and in video conferences. The eight researchers are well known in the
academic community of Information Systems. Together they represent six different countries (Australia, Germany,
Hong Kong, Switzerland, The Netherlands, and USA). Their experiences in the IS research discipline span from 18
years for the youngest to 36 years for the most experienced candidate. The size of their research groups ranges from
5 to 123 members of staff. Their interviews were fully transcribed and coded.
Many of the assumptions of our a priori model were confirmed during the coding process. However, some adjustments had to be made and it turned out that some of the factors were not suited to rigorously distinguish between
the profiles. The number of UIC archetypes remained the same but some were renamed after the analysis of the
interviews. Each archetype was represented by at least one interviewee. We had two interviews for the each of the
two types “Consortium Researcher” and the “Relationship Researcher”. The factor “orientation” (relationship) was
deleted from the model because there was no significant difference in the responses. All relationships with partners
had to be built up over a long period of time. The same applied to the company size. Nearly all collaborating organizations were larger than 1000 employees. The factors that finally allowed most differentiation were benefits, funding, applied research methods, organization type, output, and kind of setup.
The archetypes are not fully mutually exclusive as some of them share joint characteristics. Each of the types is
primarily characterized by one or two of the differentiating factors. It was interesting to see that different factors
define different archetypes. To give an example, the setup (1:m) can be the main characteristic for one archetype
(e.g. The Consortium Researcher) while an exclusive method (design science research) can be the defining characteristic for another (The Design Researcher). This is why some of the archetypes can look similar regarding most
factors while they may only differ in their most defining one. The “setup”, the “source of funding” and the “preferred method/output” are the most differentiating factors. The following paragraphs provide the background of the
UIC archetypes. Table 7 shows the revised list of archetypes and their attributes after the analysis of the interviews.
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5.2.1. The Blue Ocean Researcher
This type is driven by the curiosity to “go where no one has gone before”. Researchers in this group work mostly
with top-100 companies that can financially afford basic research and can engage in a mid- to long-term project that
does not yield immediate results. Typically, researchers in this group have a strong and extraverted personality that
enables them to engage top management in strategic research questions. Also, these researchers are surrounded by a
stable (and sufficiently large) group of researchers (post-docs and Ph.D. students) who are experienced enough to
develop convincing results in an industry environment. The funding can be either provided by government grants
(guaranteeing a greater degree of independence for the researchers) or provided by the industry, or both. The benefits such researchers see are mostly in the identification of an interesting research question and access to empirical
data/opportunities for empirical testing. These researchers draw substantial satisfaction from the impact that their
work has on industry. Their preferred methods are a combination of design research (developing prototypes and
testing them), action research (intervening and accompanying a company in a change process) and case studies on
their industry partner. Most of their projects are in 1:1 setups with private companies.
Table 7. Current framework of UIC archetypes, influencing factors and attributes
Factors
Archetype
Blue Ocean
Researcher
Benefits
Attributes
Research
problem
Impact
Consulting
Researcher
Personal
income
Consortium
Researcher
Relevance
Funding
Governmentfunded
Researcher
Design
Researcher
Research
problem
Empirical data
Research
problem
Funding
Credibility
Empirical data
Occasional UI
Researcher
OrgaType
Private
Gov.
Funding
Private Grant
ResMeth.
Attributes
Design
research
Action research
Case study
Case study
Deductive
analysis
Design research
Deductive
analysis
Action research
Case study
Output
Meth
Artifact
Info
# Unis
# Organ
Many 1
Many
1
Design
research
Any
5.2.2. The Consulting Researcher
This type receives funding (often in the form of personal consulting fees or a direct contribution to the research
group) almost exclusively from industry. The collaborative work is performed in a 1:1 setup with the top or at least
senior management and is geared at strategic topics. Typical results of the joint work are individual project support
as well as methods or frameworks for technology management. The joint work is optimized to support (consult)
companies. Outputs are generated for management and come in the form of management sessions, presentations and
reports. Due to the funding model and the need for complexity in the setup of their projects, this group only works
with large to very large companies. They are attracted by industries such as banking, insurances and services. Researchers with this profile often establish their own consulting companies or perform the consulting activity outside
of their official jobs at the university. The findings are transferred back to the university indirectly in the form of
teaching (seminars, classes) or Ph.D. supervision. This way, the university profits from the activity of the researcher.
This is why many universities (especially Universities of Applied Sciences) more or less explicitly tolerate or even
encourage this UIC archetype for their academic staff members.
5.2.3. The Consortium Researcher
This type is specialized on bringing different industry partners together in a 1:m setup. Funding and knowledgebase of the collaborating industry partners are pooled in order to conduct a large project with multiple researchers
(from only one research group) and multiple company representatives. The partners have a complementary interest
in a specific research topic. Typical results are empirical studies, explanatory frameworks or the development of
procedure models and methods. Almost all research methods and industries are feasible for this type. The industry
partners are normally from different industries and are thus not direct competitors. Collaboration can be intense
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Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
(members of the industry partner work actively on the result) or loose (researchers are compiling most of the results). Funding is typically raised from industry although complementary government funding is also conceivable.
5.2.4. The Government-funded Researcher
This type is typically involved in large-scale government-funded projects involving multiple parties (n:m). Typical forms are projects in the EU Framework program or other forms of national government funding programs.
These researchers are strong networkers and manage to bring different parties with sometimes even opposing interests together at a joint discussion table. In this scenario, there is typically a mix of private industry and public agencies working with more than one university (each specializing on certain work packages). Often, the research objectives are in the public interest. Typical methods used in these projects are action research and case studies, or in
some cases even design research.
5.2.5. The Design Researcher
This type does mostly research that is close to the discipline of applied computer sciences. Researchers in this
group are interested in the development of actual artifacts. They have a strong technology background and are visionaries for innovative technological developments. Their preferred research method is design science research that
can occasionally be combined with case studies and action research. The typical setup is 1:1 as the results of the
project are often declared confidential by the industry partner to be able to market them as products. The prior
agreement on the ownership of patents and commercialization rights is important for these UI projects.
5.2.6. The Occasional UI Researcher
This type could be called “old school” UI collaboration researcher. Representatives of this group are mostly interested in the academic output and do not pursue a monetary interest for themselves or for the establishment of a
large research group. They are in favor of collaboration with industry as they believe that research results in the IS
discipline should be empirically derived from industry and the findings need to be transferred back to industry for
the development of company competitiveness in their respective country. Researchers in this group are typically
interested in building up long lasting ties and a good level of trust with their industry partners. They pursue an engagement with industry only when the occasion arises – often even on the initiative of the industry partner (which is
very unlikely for the other types). Their funding can come in all three forms (private, public, grant). The setup is
mostly 1:1 as these researchers would shy away from the effort of forming a large research consortium.
5.2.7. The Pure Theory Researcher
The pure theory researcher rejects the notion of academia working with industry and does not engage in collaboration projects. The objective of this group is typically “development of theory”. They believe that desk research is
sufficient for their objectives. This group has not been further studied in our research and is listed to complete the
list of UIC archetypes.
6. Conclusions and Future Research
We are aware that the list of UIC archetypes we present is in an interim state. Although we developed the model
from previous research and tested it with selected proponents whom we had personally observed over years, a group
of eight people is still a small sample and additional UIC archetypes might exist that we have not yet identified. The
next stage of our research program will comprise another (this time larger scale) survey in which we will further test
our current model.
The results of our survey were in part surprising for us. We had expected that there are many researchers who are
not actively engaging in UI collaboration and that there are differences among the different research cultures (countries). However, we were surprised to see how large the non-engaging group is given that there must be a bias in
favor of UI collaboration among our respondents already.
The authors believe that UIC is a valuable way to conduct research that is rigorous, relevant and has impact on
industry and/or society at the same time. Our programme of research into the phenomenon of UIC will continue over
the years to come.
Petra Schubert et al. / Procedia Technology 16 (2014) 138 – 149
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