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The paper reports on the results of a Delphi study with 143 information systems (IS) academics that was designed to explore what IS academics perceive to be the grand challenges of the IS discipline. The results provide evidence that the scholarly IS discipline is still much concerned with itself, for instance, in terms of its identity, relevance, foundational theory, or methodological pluralism – suggesting that the old debate on IS identity is not yet overcome. It thus cannot be claimed that the study identifies the grand challenges of the discipline – still it becomes noticeable that the academic community sees potentials for the IS discipline to have societal impact. A total of 21 challenges are identified, of which six challenges are categorized as ''meta challenges for further developing the IS discipline'' and the remaining 15 challenges are categorized as ''IS research challenges'' pertaining to socio-technical systems, IS infrastructures, society and ecology, as well as social well-being and affectivity. We provide a ranking of all challenges according to their relevance, potential impact, and possible time frame of realization. The results have some important implications for IS as a discipline as well as its prospective future societal role. It is hoped that through our study we can contribute to the important debate on the challenges of the academic IS discipline.
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RESEARCH NOTES
In Search of Information Systems (Grand) Challenges
A Community of Inquirers Perspective
Jo
¨rg Becker Jan vom Brocke Marcel Heddier Stefan Seidel
Received: 27 January 2015 / Accepted: 20 April 2015 / Published online: 16 September 2015
ÓThe Author(s) 2015. This article is published with open access at Springerlink.com
Abstract The paper reports on the results of a Delphi
study with 143 information systems (IS) academics that
was designed to explore what IS academics perceive to be
the grand challenges of the IS discipline. The results pro-
vide evidence that the scholarly IS discipline is still much
concerned with itself, for instance, in terms of its identity,
relevance, foundational theory, or methodological plural-
ism – suggesting that the old debate on IS identity is not yet
overcome. It thus cannot be claimed that the study identi-
fies the grand challenges of the discipline – still it becomes
noticeable that the academic community sees potentials for
the IS discipline to have societal impact. A total of 21
challenges are identified, of which six challenges are cat-
egorized as ‘‘meta challenges for further developing the IS
discipline’’ and the remaining 15 challenges are catego-
rized as ‘‘IS research challenges’’ pertaining to socio-
technical systems, IS infrastructures, society and ecology,
as well as social well-being and affectivity. We provide a
ranking of all challenges according to their relevance,
potential impact, and possible time frame of realization.
The results have some important implications for IS as a
discipline as well as its prospective future societal role. It is
hoped that through our study we can contribute to the
important debate on the challenges of the academic IS
discipline.
Keywords Grand challenges IS research Delphi
study IS community Research impact
1 Introduction
What are the grand challenges of a scientific discipline?
Finding the Higgs boson or flying to the moon were grand
challenges and could only be achieved through collabora-
tive, cross-disciplinary efforts and the allocation of con-
siderable resources. In physics, biology, or medicine it is –
often even for the layperson – easy to identify some grand
challenges. They personally matter to us (e.g., in the case
of medicine), they have highly visible societal conse-
quences (e.g., in the case of using alternative energy
sources), or they are frequently in the news (e.g., in the
case of CERN’s Large Hadron Collider).
But what are the challenges of the information systems
(IS) discipline? Considering that IS have been the major
contributor to economic growth and productivity over the
past decades (Watson et al. 2010), this question definitely
warrants our attention. While considerable effort has been
put into identifying such challenges (Dickson et al. 1984;
Brancheau and Wetherbe 1987; Niederman et al. 1990;
Brancheau et al. 1996; Kappelman et al. 2013; Dekleva and
Zupancic 1996; Krcmar 1990; Moores 1996; Wang and
Turban 1994; Watson 1989; Yang 1996; Luftman et al.
Electronic supplementary material The online version of this
article (doi:10.1007/s12599-015-0394-0) contains supplementary
material, which is available to authorized users.
Prof. Dr. Dr. h.c. Dr. h.c. J. Becker (&)Dr. M. Heddier
ERCIS – European Research Center for Information Systems,
Westfa
¨lische Wilhelms-Universita
¨tMu
¨nster, Leonardo-Campus
3, 48149 Mu
¨nster, Germany
e-mail: joerg.becker@ercis.uni-muenster.de
Dr. M. Heddier
e-mail: marcel.heddier@ercis.uni-muenster.de
Prof. Dr. J. vom Brocke Prof. Dr. S. Seidel
Institute of Information Systems, University of Liechtenstein,
Fu
¨rst-Franz-Josef-Strasse, 9490 Vaduz, Liechtenstein
e-mail: jan.vom.brocke@uni.li
Prof. Dr. S. Seidel
e-mail: stefan.seidel@uni.li
123
Bus Inf Syst Eng 57(6):377–390 (2015)
DOI 10.1007/s12599-015-0394-0
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
2013), the answers are still not obvious, and different
scholars are likely to name different challenges, if asked.
And, indeed, the IS field is characterized by its diversity
(Schwartz 2014) as well as dynamic technological devel-
opment. Big data, the internet of things, or cloud com-
puting are just a few examples of developments that could
not even be envisioned a few years ago. Diversity has been
identified as both a strength and weakness of our discipline
(Robey 1996; Benbasat and Zmud 2003; Benbasat and
Weber 1996). While diversity is desirable in terms of
contributions from different research communities in the
sense of ‘‘disciplined methodological pluralism’’ (Landry
and Banville 1992), it is our contention that identifying
grand challenges might help develop an ideal of collabo-
ration (Robey 1996) and thus foster both effectiveness and
efficiency in IS. Researchers can more easily pinpoint
relevant topics and can share and communicate their
results. Research is a collaborative effort, and we need to
understand where resources should be best allocated.
Against this background, we believe that it is worth-
while to investigate what the community of IS scholars,
which forms a community of inquirers (Peirce 1877;
Seixas 1993; Constantinides et al. 2012), considers their
grand challenges. We feel that it is important for us to
understand what we aim to accomplish as a discipline as
well as what challenges we face as a discipline. As a
community of inquirers, the IS field is characterized by its
social context and is built upon certain intersubjective
agreements among its members (Peirce 1877; Seixas
1993). While there has been intensive debate on the
subject of inquiry, that is, the core of the IS discipline
(Benbasat and Zmud 2003), there is considerable agree-
ment that this community of inquirers is concerned with
the development, use, and effects of IS artifacts. Our
essential research question is:
What are the grand challenges of IS research from
the perspective of the community of inquirers?
In order to seek answers to this question, we use three
perspectives and corresponding questions:
1. What are the grand challenges of IS with regard to
solving problems of specific domains (i.e., using IS
artifacts to solve problems)?
2. What are the grand challenges of IS that are indepen-
dent of specific domains (i.e., phenomena related to the
IS artifact itself, such as integration, complexity,
usefulness)?
3. What are the grand challenges of the IS discipline with
regard to its further development?
The first and second question relate to what the disci-
pline should study with regard to its primary units of
analysis (IS artifacts, individuals, groups, organizations,
governments, society) in terms of the development, use,
and effects of IS. The third question asks how the discipline
should position itself to accomplish its goals. In order to
seek answers to these questions, we conducted a prelimi-
nary workshop with 17 participants, a qualitative two-
round Delphi study involving 14 IS researchers, and a
subsequent quantitative two-round Delphi study involving
113 IS researchers.
Our work contributes to the debate on the important
question of what the IS discipline should study. Specifi-
cally, we contribute to our discipline’s understanding of its
major goals as a community of inquirers. While the target
(i.e., the grand challenges) is moving along in a dynamic
discipline such as IS, this does not mean that we shouldn’t
think about what we should study now. We also expect that
the grand challenges of our discipline will take years and
considerable resources to be solved, and that new grand
challenges will emerge.
The remainder of this paper is structured as follows.
First, we present related work on grand challenges in
general and in IS in particular. This is followed by the
research design and the results section. We then discuss our
findings, highlight limitations, and conclude with a
summary.
2 On Grand Challenges of Scientific Disciplines
Research on grand challenges and key questions of scien-
tific disciplines, or research fields, has a long tradition.
There are different approaches to identifying grand chal-
lenges, including conferences, summits, workshops, or,
indeed, Delphi studies.
In the field of engineering, for instance, Bathia (2013)
summarized the results of the ‘‘Global Grand Challenges
Summit’’ held at the Institution of Engineering and Tech-
nology in London in 2013 on challenges for Engineering
and International Development. McKone et al. (2011)
elaborated on the grand challenges for life-cycle assess-
ment of biofuels that resulted from ‘‘research planning and
progress meetings of the Life-Cycle Program of the Energy
Biosciences Institute at the University of California,
Berkeley’’ (McKone et al. 2011, p. 1). Mo
¨nch et al. (2011)
called for research on the grand challenges for discrete
event logistics systems in a special issue of the journal
Computers in Industry. Wheeler et al. (2011) did the same
for the grand challenges in Neuroengineering in a special
section in the IEEE Transactions on Biomedical
Engineering.
In Medical Science, He et al. (2013a) summarized the
results of the discussion on grand challenges in interfacing
engineering with life sciences and medicine, held during
the first IEEE Life Sciences Grand Challenges Conference
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in 2012. Furthermore, He et al. (2013b) presented the
results of a discussion (three grand challenges on engi-
neering and mapping the brain) of the NSF Workshop on
Mapping and Engineering the Brain in 2013. Singer et al.
(2007) investigated the grand challenges for global health
in the context of the Grand Challenges in Global Health
Initiative.
In the IS discipline, the debate on grand challenges dates
back to the 1980s, when Dickson et al. (1984) initiated a
sequence of related studies with a Delphi study on the key
issues of information systems management. They vindi-
cated their work by stating that the ‘‘information systems
profession is continually faced with making difficult deci-
sions about the commitment of its limited management,
research, and educational resources’’ (Dickson et al. 1984,
p. 1), and that ‘‘a widely accepted and current assessment
of the important management issues in information systems
does not exist’’ (Dickson et al. 1984, p. 1). Their Delphi
study consisted of four rounds, with the participants being
IS professionals, mainly from leading positions (IS exec-
utives) in the United States. The study was repeated
three years later by Brancheau and Wetherbe (1987), this
time with a three-round Delphi study. Niederman et al.
(1990) repeated the study again to determine the grand
challenges of IS management of the 1990s, also with a
three-round Delphi study. In 1996, another three-round
Delphi study was conducted by Brancheau et al. (1996).
Finally, a recent study by Kappelman et al. (2013) deter-
mined the key issues of IT organizations and their leader-
ship by conducting a survey with IS professionals.
A number of studies have investigated grand challenges
from the perspectives of specific countries. A two-round
Delphi study by Hayne and Pollard (2000) with 157
Canadian participants identified critical IS issues in
Canada. Dekleva and Zupancic (1996) used the Delphi
technique to determine key issues in information systems
management in Slovenia. A German perspective was
described by Krcmar (1990), who conducted a survey,
asking German IT executives of large and medium-sized
enterprises for current issues of the field. Moores (1996)
investigated key issues in the management of information
systems with a survey in Hong Kong. A Chinese per-
spective on management information systems (MIS) key
issues was presented by Wang and Turban (1994). Watson
(1989) used a three-round Delphi study to establish the
most important MIS issues of Australia. Yang (1996) sur-
veyed Taiwanese companies for their key MIS issues and
compared the results to MIS issues from the USA. Finally,
a recent study of Luftman et al. (2013) investigated key
information technology and management issues from an
international perspective. They conducted the same survey
in four different geographic regions (U.S., Europe, Asia,
and Latin America) questioning IS/IT professionals in
these regions and comparing the results. Also recently,
Mertens and Barbian (2015) studied the views of the
business informatics discipline that much characterizes the
German-speaking IS community.
Grand challenges research in the field of IS/IT has fur-
ther been conducted for different sub-fields of the disci-
pline. Lai and Chung (2002), for instance, identified top ten
issues for international data communications management
in a two-round Delphi study. The three-round Delphi study
of Viehland and Hughes (2002) elaborated on future sce-
narios for the wireless application protocol. Winter (2012)
presented an argumentative paper where she proposes and
discusses grand challenges for eCommerce, and Hoare and
Milner (2005) investigated grand challenges for Computing
Research by conducting an exercise with an expert panel.
Similarly, the German Informatics Society used expert
opinions to formulate five grand challenges of informatics
(Gesellschaft fu
¨r Informatik 2014).
To summarize, there is a considerable body of knowl-
edge on grand challenges. Typically, these studies focus on
an IT/IS practice perspective by either questioning IT/IS
professionals or academics. It is our contention that iden-
tifying grand challenges from the perspective of IS aca-
demics – that is, from that of the community of inquirers –
will provide an insiders’ perspective which helps us
understand two things: what we aim to accomplish and
how we need to further develop our discipline in order to
do so. Besides, prior work has primarily focused on specific
countries/regions and differences between these regions
(e.g., Yang 1996). Considering the global scope of both IS
practice and academy, in our study, we aim to provide an
international perspective.
3 Research Design
3.1 The Delphi Method
We used the Delphi method to explore grand challenges for
IS research. The method allows for ‘‘structuring a group
communication process so that the process is effective in
allowing a group of individuals, as a whole, to deal with a
complex problem’’ (Linstone and Turoff 1975, p. 3), and is
an accepted method in IS research that is frequently used to
forecast and to identify and prioritize important issues
(Okoli and Pawlowski 2004). While no single standardized
way to conduct a Delphi study exists, there are general
principles that are usually followed: First, the participants
of the study should be a group of experts in the respective
field. Second, a Delphi study consists of different phases,
which can, but do not have to, include brainstorming,
consolidation, and ranking (Okoli and Pawlowski 2004).
Third, each phase may consist of more than one survey
123
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round, which is typically aimed at generating a consensus
among the participating experts. This process is supported
by providing participants, within each phase, with feedback
based on the results of previous rounds. Finally, the goal of
a Delphi study must be determined. Goals can be roughly
divided into prediction and conceptualization. In the fol-
lowing, we describe our research procedure (see also
Fig. 1).
3.2 Preliminary Phase
The project commenced with a workshop with 14 IS
experts (nine full professors, four associate professors, and
one senior lecturer) from nine different countries (Finland,
France, Germany, Italy, Liechtenstein, Lithuania, Norway,
Poland, and South Korea). This initial sample was a con-
venience sample as we took advantage of a meeting of a
group of international IS scholars, all of whom can be
considered to be experts in the field, and the sample was
thus considered to be appropriate. Online Appendix A.1
provides demographics of the participants of the prelimi-
nary phase. The participants were given an introduction
into the topic of grand challenges and were then asked to
write down two or three challenges of IS research. The
results were presented and discussed in the group. Six of
the participants became the steering committee that was
responsible for monitoring and supporting the study, and
making decisions where necessary. The steering committee
consolidated the workshop results and proposed a first list
of challenges for IS research. This list was input for Phase
1 of the Delphi study (qualitative phase).
3.3 Phase 1: Qualitative Delphi Study
In the first phase, we addressed a group of selected IS
experts, one of whom had participated in the initial work-
shop, and asked for their participation in an online Delphi
survey. The sample was thus a purposive sample as the
participants were chosen based on their expertise. The 13
participating experts (all of them IS professors) were from
twelve different countries (Australia, Austria, France,
Germany, Ireland, Italy, Lithuania, New Zealand, Russia,
Slovenia, Spain, and Switzerland). Online Appendix A.2
provides demographics of these participants. In the survey,
we presented the list of challenges retrieved from the pre-
liminary workshop, provided a short explanation for each
challenge, and asked the participants to comment on the
issue. We further asked them to propose new potential
challenges as well as changes/amendments to existing ones.
In order to structure the list of challenges and to provide
additional stimulus, it was decided to group the challenges
along the three sub-questions related to external challenges
of specific domains that should be solved by IS, challenges
that are independent of specific domains, and challenges of
the IS discipline regarding its further development.
The results of the survey were consolidated by the
steering committee and used as input for a second online
survey (i.e., the second round of this qualitative Delphi
Phase 1: Qualitave Delphi Study Phase 2: Quantave Delphi Study
Prelim inary Phase:
Workshop
14 IS schol ars at ERCIS
Annua l Worksh op
Task: Name 2-3 Grand
Challenges of IS research
Fo undaon of the GC
steering commiee
(6 ful l IS pro fessors)
Consolidaon and
harmonizaon of
workshop result s First list of
challenges
1st Qualitave Delphi Round
Qualitave Delphi study with an internaonal
group of 13 IS scholars
Comments on proposed GCs
Pr oposio ns of new GCs
2nd Qualitave Delphi Round
Qualitave Delphi study with an internaonal
group of 9 IS scholars
Comments on proposed GCs
Cons olidat ed Feedback
Consolidaon and
harmonizaon of Delphi
results from Phase 1 Final list of
challenges
1st Quantave Delphi Round
Quantave Delphi study with an
internaonal group of 113 IS scholars
Quantave assessment of GCs
Comments on proposed GCs
2nd Quantave Delphi Round
Quantave Delphi study with an
internaonal group of 55 IS scholars
Quantave assessment of GCs
Comments on proposed GCs
Cons olidat ed Feedback
Consolidated Feedback
Steer ing
Committee
Steer ing
Committee
Steering Committee
Evaluaon
of Results Completed
research
paper
Steer ing
Committee
Steering Committ ee
Fig. 1 Research procedure
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phase). Those participants who proposed new challenges
were asked to provide an explanatory text for these. All
new challenges that were provided with an explanation
were included in the second round. In accordance with the
general principles of Delphi studies, the second survey also
contained the aggregated results from the first round as
feedback for the participants. This feedback consisted of all
(partly paraphrased) comments from the first round. The
same participants were asked to revise their comments and
to comment on the new challenges, thereby considering the
feedback/comments from the other experts. Nine of the
previous 13 experts participated in the second round.
Moreover, in the second round, participants could classify
their own comments using the following items: ‘‘I totally
agree with this grand challenge’’, ‘‘I would change/edit this
grand challenge’’, ‘‘I would merge this grand challenge
with one or more other grand challenges’’, ‘‘I would split
this grand challenge into two or more grand challenges’’, ‘‘I
totally disagree with this grand challenge and would
remove it from this survey’’, and ‘‘None of the above’’.
Depending on the classification, the steering committee
took according action. For example, if the majority of
participants classified their comments for a specific chal-
lenge as ‘‘agree’’, this challenge was included in the second
Delphi phase; if most participants classified their com-
ments as ‘‘disagree’’, the challenge was excluded from the
second Delphi phase. For the classifications edit, merge,
and split, the steering committee made decisions based on
the comments to edit, merge or split the respective chal-
lenges. The final list of challenges served as input for Phase
2 (quantitative phase). Notably, some challenges that were
included in this list were discussed controversially –
lending some evidence towards the assumption that there is
no common understanding of what constitutes grand
challenges in IS research.
3.4 Phase 2: Quantitative Delphi Study
In the second phase, we addressed a larger group of IS
researchers through an online survey instrument. In the first
round of this phase, 112 IS researchers from 21 countries
(Australia, Austria, Canada, Czech Republic, Denmark,
Faroe Islands, Finland, France, Germany, Italy, Lithuania,
Netherlands, New Zealand, Norway, Poland, Russia,
Slovenia, Sweden, Switzerland, United Kingdom, and
United States) participated. The survey presented the
challenges from the first phase (including a short expla-
nation for each), and the participants were asked to rate
these challenges (on a five-point Likert scale) regarding
relevance, impact, and time frame for the solution of each
challenge.
The participants were further asked whether they think
that the presented challenge is a grand challenge of IS
research—or not. In the survey, we explicitly used the term
‘issue’ instead of ‘grand challenge’ to leave it open for the
participants to think of an issue as a grand challenge or not.
In the end of the survey, participants were asked to pick
those three challenges they considered the most important
ones, and rank them accordingly.
We invited the participants of the first survey round to
participate in a second survey round. Of the 112 partici-
pants from the first round, 55 respondents from 18 different
countries (Australia, Austria, Canada, Czech Republic,
Finland, France, Germany, Italy, Netherlands, New Zeal-
and, Norway, Poland, Russia, Slovenia, Sweden, Switzer-
land, United Kingdom, and United States) participated in
the second round. We provided the same survey, but pre-
sented the aggregated results from the first round next to
each survey item. The results were visualized through a
boxplot visualization showing the mean value and standard
deviation for each item. Figure 2visualizes the criterion
‘impact’ of a specific challenge.
The participants were asked to complete the survey
again, this time under consideration of the first round’s
results. Furthermore, the participants were provided with
the opportunity to state comments for each challenge (di-
vided into pro and contra) in order to better understand
their rationale.
4 Results
4.1 Phase 1: Qualitative Delphi Study
The preliminary workshop produced a list of 15 challenges
as well as short explanatory texts. These texts were used to
describe the challenges in the survey in order to avoid
possible misunderstandings.
After consolidating the results of the second survey
round, the total of 27 challenges was condensed to a final
set of 21 challenges based on the comments made by
respondents as well the analysis of the participants’ clas-
sification. Table 1shows the final list of challenges (in no
specific order), including the history of each challenge.
Descriptions of each challenge can be found in Online
Appendix A.
Fig. 2 Example for the
feedback depiction of the mean
and standard deviation
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4.2 Phase 2: Quantitative Delphi Study
The quantitative phase of the study terminated after two
rounds. The average standard deviation for the three cri-
teria largely remained the same between the first and the
second round (relevance: -0.04; impact: -0.09; time-
frame: ?0.15). It was thus concluded that a third Delphi
round would not lead to significantly higher levels of
consensus among the participants. At this stage, the par-
ticipants had relatively stable opinions.
The first criterion surveyed was the relevance of an
issue. Table 2ranks the 21 challenges regarding their mean
value for this criterion, beginning with the challenge with
the highest value (i.e., the highest relevance). Overall, for
this criterion, the challenges received comparably high
values, meaning that on average the participants perceived
all challenges as relevant to IS research (or were indecisive
in some cases). However, the average standard deviation
(0.96) shows that even after the second Delphi round there
were different perceptions of the challenges’ relevance
within the group of participants.
The second criterion surveyed was the impact an issue
has on the IS field. We define ‘‘impact’’ as the extent of
future effects and consequences that may result from
solving a respective challenge. Table 3ranks the issues
according to their impact. Again, on average, the
participants perceived the impact of all challenges as
strong. The rather high average standard deviation (1.0) is
an indicator for significant dissent among the participants
regarding this criterion.
The third criterion surveyed was the time frame, that is,
the expected period of time an issue needs to be dealt
with/solved. On average, the participants either agreed with
the time frame of ten years or expected solving the issues
to take even longer. The average standard deviation (1.12)
once more indicates a dissent among the participants
(Table 4).
Table 5provides an overview of the percentage of
respondents that rated a challenge to be a grand challenge.
This item was included because of some comments from
the first phase, where participants had stated that several
challenges actually did not qualify for being considered
grand challenges, but were rather (nevertheless important)
issues. While the responses might depend on how the term
grand challenge is defined, we deemed it relevant to ask for
the participants’ opinions. It became obvious that partici-
pants tended to consider the challenges C11 (developing
universal methods for the translation between different
coding systems), C14 (leveraging the ‘‘fun’’ in information
systems applications), C13 (developing model-driven
methods and tools for the full-scale automated generation
of implementation-ready IS), C20 (raising collective
Table 1 Challenges for IS research identified from the qualitative delphi study
Challenges for IS research History
C01 – identifying IS as an academic discipline W
C02 – adapting IS teaching to current IS research developments W
C03 – proving relevance of IS research W
C04 – rethink the theoretical foundations of the IS discipline New
C05 – streamlining and providing equal quality standards for different strands of IS research New, E
C06 – mastering the methodological breadth/richness New, E
C07 – increasing theoretical/methodological sophistication New
C08 – providing ubiquitous access to IS services W
C09 – integrating information systems in one single virtual space W, E
C10 – making different IT generations work together W
C11 – developing universal methods for the translation between different coding systems W, E
C12 – aligning organizational objectives with IT by developing and establishing efficient communication means W
C13 – developing model–driven methods and tools for the full–scale automated generation of implementation–ready IS New, E
C14 – leveraging the ‘‘fun’’ in information systems applications New
C15 – integrating human and machine problem solving New
C16 – leveraging knowledge from data, with the related management of high data volumes New, E
C17 – developing effective IS for emergency management W
C18 – utilizing energy informatics W
C19 – supporting effective collaboration and learning through evolving media repertoires W
C20 – raising collective consciousness W
C21 – embedding systems in real–life environments W, E
W=proposed in preliminary workshop; New =proposed in first qualitative round; E =edited according to comments from 1st and 2nd round
123
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consciousness), and GC18 (utilizing energy informatics)
not to be ‘grand’ challenges. Considering that the majority
of participants in Phase 1 agreed on the list of 21 chal-
lenges, this shows how differently people conceive the
term ‘grand challenge’ (although we introduced our defi-
nition at the beginning of each survey).
Table 6provides a ranking based on the challenges that
were identified as the three most important ones by each
respondent. Challenges that were ranked first got three
points, those ranked second got two points, and those
ranked third got one point. Challenges that were not ranked
by any participant got zero points. Perusing this method,
we received a ranking which was slightly different to, for
instance, the ranking according to their relevance.
5 Discussion and Implications
The Delphi study produced 21 challenges that were eval-
uated according to relevance, impact, timeframe, signifi-
cance, and importance. The analysis of our data displays a
diversity of challenges, which can be roughly distinguished
into those pertaining to the IS discipline and its
development (meta challenges for developing the IS dis-
cipline), and those pertaining to the actual problems the
discipline could solve (IS research challenges). Within the
category of IS research challenges, we identify four com-
mon themes that are appropriate to further structure the
research challenges. In what follows, we first discuss the
meta challenges and then turn to the IS research challenges
identified through our analysis.
5.1 Meta Challenges for Developing the IS Discipline
Six out of the top ten challenges that were identified relate
to issues concerning the development of the IS discipline
itself (cf. Table 6). As the most important one the
respondents identified ‘‘proving relevance of IS research’’
(rank 1 in overall importance, rank 3 in that it is a grand
challenge, and rank 4 in impact). Further challenges
include ‘‘identifying IS as an academic discipline’’ (rank
4), ‘‘rethink the theoretical foundations of the IS disci-
pline’’ (rank 5), ‘‘mastering the methodological breadth/
richness’’ (rank 6), ‘‘adapting IS teaching to current IS
research developments’’ (rank 9), ‘‘increasing theoretical/
methodological sophistication’’ (rank 10), and
Table 2 Relevance (‘‘This issue is relevant in the field of information systems research’’)
Rank Challenges for IS research Mean
value
Standard
deviation
1 C16 – leveraging knowledge from data, with the related management of high data volumes 4.49 0.758
2 C15 – integrating human and machine problem solving 4.44 0.698
3 C04 – rethink the theoretical foundations of the IS discipline 4.31 0.897
4 C02 – adapting IS teaching to current IS research developments 4.25 0.821
5 C06 – mastering the methodological breadth/richness. 4.16 0.946
6 C12 – aligning organizational objectives with IT by developing and establishing efficient communication means 4.08 0.860
7 C21 – embedding systems in real–life environments 4.04 0.692
8 C03 – proving relevance of IS research 3.98 1.049
9 C08 – providing ubiquitous access to IS services 3.94 1.008
10 C19 – supporting effective collaboration and learning through evolving media repertoires 3.94 0.826
11 C01 – identifying IS as an academic discipline 3.92 1.064
12 C17 – developing effective IS for emergency management 3.84 0.834
13 C05 – streamlining and providing equal quality standards for different strands of IS research 3.80 1.149
14 C09 – integrating information systems in one single virtual space 3.74 0.964
15 C18 – utilizing energy informatics 3.71 0.997
16 C07 – increasing theoretical/methodological sophistication 3.65 1.146
17 C10 – making different IT generations work together 3.65 1.016
18 C14 – leveraging the ‘‘fun’’ in information systems applications 3.63 1.131
19 C13 – developing model–driven methods and tools for the full–scale automated generation of implementation–
ready IS
3.53 1.206
20 C20 – raising collective consciousness 3.51 1.084
21 C11 – developing universal methods for the translation between different coding systems 3.44 0.998
Scale: 1 =strongly disagree; 2 =disagree; 3 =undecided; 4 =agree; 5 =strongly agree
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‘streamlining and providing equal quality standards for
different strands of IS research’’ (rank 15). These chal-
lenges relate to themes recurrently discussed in the IS field,
such as relevance, theoretical foundations, methods, and IS
identity. Moreover, the study highlights challenges related
to IS teaching. Overall, the results suggest a tendency to
formulate generic challenges. It has been asserted that the
IS discipline is still in its infancy with regard to its level of
specialization, compared to other disciplines (Schwartz
2014). Table 7provides an overview of those challenges
pertaining to further developing the academic IS field.
It is interesting to see that ‘‘proving relevance of IS
research’’ was identified as a highly important challenge
(rank 1 in overall importance, rank 3 in being a grand
challenge, and rank 4 in impact). However, as we inves-
tigate the community of inquirers, it is not surprising that
the respondents are self-referential. Many scholars have
pointed out that the IS discipline should focus on topics
that are relevant to practitioners and should provide
knowledge that can be implemented and is accessible
(Benbasat and Zmud 2003; Rosemann and Vessey 2008).
Our findings suggest that the old debate about rigor and
relevance in IS research is still ongoing.
Two challenges relate to the theoretical foundation of
IS, namely ‘‘rethink the theoretical foundations of the IS
discipline’’, and ‘‘increasing theoretical/methodological
sophistication’’. The study thus confirms prior work, which
has discussed the lack of foundational IS theory and which
calls to develop theory in IS (Watson 2001). Currently, IS
tends to borrow theory from a number of reference disci-
plines. We interpret this as a call to further develop the
theoretical core of our discipline (Urquhart and Ferna
´ndez
2013; Seidel and Urquhart 2013).
The respondents identify ‘‘mastering the methodological
breadths and richness of the IS discipline’’ as a grand
challenge. IS is methodologically plural, and researchers
draw on different paradigms such as interpretivism, posi-
tivism, and critical realism, and apply a multitude of dif-
ferent research methods (Benbasat and Weber 1996).
While prior debates have suggested that IS should favor
certain methods and certain research approaches (Lyytinen
et al. 2007;O
¨sterle et al. 2010), our study suggests that the
community of inquirers appreciates a diversity of methods
and paradigms, and sees a challenge in better understand-
ing how they relate to and complement each other. This is
consistent with the idea of ‘‘disciplined methodological
Table 3 Impact (‘‘The solution of this issue will have a strong impact on the information systems discipline’’)
Rank Challenges of IS research Mean
value
Standard
deviation
1 C16 – leveraging knowledge from data, with the related management of high data volumes 4.31 0.707
2 C15 – integrating human and machine problem solving 4.13 0.817
3 C04 – rethink the theoretical foundations of the IS discipline 4.12 0.832
4 C03 – proving relevance of IS research 3.98 1.029
5 C06 – mastering the methodological breadth/richness 3.90 0.944
6 C02 – adapting IS teaching to current IS research developments 3.84 0.880
7 C08 – providing ubiquitous access to IS services 3.70 1.030
8 C01 – identifying IS as an academic discipline 3.69 1.130
9 C05 – streamlining and providing equal quality standards for different strands of IS research 3.69 1.122
10 C12 – aligning organizational objectives with IT by developing and establishing efficient communication means 3.67 1.043
11 C21 – embedding systems in real–life environments 3.67 0.909
12 C07 – increasing theoretical/methodological sophistication 3.63 1.076
13 C14 – leveraging the ‘‘fun’’ in information systems applications 3.49 1.189
14 C19 – supporting effective collaboration and learning through evolving media repertoires 3.48 1.019
15 C13 – developing model–driven methods and tools for the full–scale automated generation of implementation–ready
IS
3.43 1.204
16 C10 – making different IT generations work together 3.39 1.097
17 C09 – integrating information systems in one single virtual space 3.38 1.130
18 C18 – utilizing energy informatics 3.35 1.027
19 C20 – raising collective consciousness 3.31 1.049
20 C17 – developing effective IS for emergency management 3.20 0.775
21 C11 – developing universal methods for the translation between different coding systems 3.19 1.011
Scale: 1 =strongly disagree; 2 =disagree; 3 =undecided; 4 =agree; 5 =strongly agree
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pluralism’’ (Landry and Banville 1992). We would agree
with this view, and reference the dialectic of design-ori-
ented research and behavioral research (Gregor and Hevner
2013).
‘Identifying IS as an academic discipline’’ was ranked
fourth in importance, and thus confirms the debate that has
been coined as the IS identity crisis (Benbasat and Zmud
2003). While some may consider the debate an old chest-
nut (Seidel and Watson 2014), it still appears to be alive. It
has been suggested that the IS discipline may model other
disciplines, such as medicine, and follow a strategy of
unification and specialization (Schwartz 2014). Identifying
grand challenges may contribute to such a development,
help fostering the identity of the discipline, and allocate
resources to where they are most needed.
‘Adapting IS teaching to current IS research develop-
ments’’ was identified as the ninth most important chal-
lenge. The respondents thus see a gap between research and
teaching, and this gap may be explained by the comparably
high dynamics of our field. While the alignment between
teaching and research is at the core of university education,
we often struggle to update textbooks and curricula under
consideration of the latest developments in our discipline.
Another problem may be seen in the lack of foundational
theory – IS is still a young field. As a discipline, we should
evaluate new institutional arrangements for IS teaching.
Apart from textbooks, recent contributions can be provided
to students in the form of journal, conference, or newspaper
articles, seminars provide appropriate settings to discuss
current topics, and IS can be used in order to improve
collaboration in teaching. We must walk the talk, and our
students need to be exposed to practical problems – after all,
IS is an applied discipline that seeks to improve practice.
5.2 IS Research Challenges
The identified IS research challenges fall into the four
categories of socio-technical challenges, IS infrastructure
challenges, societal and ecological challenges, and social
and affective challenges. Table 8provides an overview of
IS research challenges, that is, challenges related to prob-
lems that might be solved through IS research.
Table 4 Time frame (‘‘This issue can be dealt with/solved in 10 years from now on’’)
Rank Challenges of IS research Mean
value
Standard
deviation
1 C20 – raising collective consciousness 4.43 1.253
2 C11 – developing universal methods for the translation between different coding systems 4.42 1.109
3 C15 – integrating human and machine problem solving 3.98 0.852
4 C01 – identifying IS as an academic discipline 3.92 1.218
5 C13 – developing model–driven methods and tools for the full–scale automated generation of implementation–ready
IS
3.92 1.163
6 C18 – utilizing energy informatics 3.83 1.133
7 C09 – integrating information systems in one single virtual space 3.79 1.026
8 C05 – streamlining and providing equal quality standards for different strands of IS research 3.76 1.595
9 C07 – increasing theoretical/methodological sophistication 3.75 1.055
10 C12 – aligning organizational objectives with IT by developing and establishing efficient communication means 3.73 1.050
11 C16 – leveraging knowledge from data, with the related management of high data volumes 3.73 0.850
12 C10 – making different IT generations work together 3.73 1.201
13 C04 – rethink the theoretical foundations of the IS discipline 3.69 1.058
14 C14 – leveraging the ‘‘fun’’ in information systems applications 3.57 1.432
15 C06 – mastering the methodological breadth/richness 3.55 1.101
16 C21 – embedding systems in real–life environments 3.45 1.045
17 C08 – providing ubiquitous access to IS services 3.40 1.166
18 C03 – proving relevance of IS research 3.35 1.110
19 C17 – developing effective IS for emergency management 3.29 0.855
20 C19 – supporting effective collaboration and learning through evolving media repertoires 3.13 1.010
21 C02 – adapting IS teaching to current IS research developments 3.04 1.148
Scale 1: =It will take significantly shorter; 2 =It will take shorter; 3 =The time frame fits; 4 =It will take longer; 5 =It will take
significantly longer
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5.2.1 Socio-Technical Challenges
Five out of 21 challenges relate to challenges of integrating
social and technical aspects of systems design, use, and
impact: ‘‘integrating human and machine problem solving’
(rank 2), ‘‘leveraging knowledge from data, with the related
management of high data volumes’’ (rank 3), ‘‘supporting
effective collaboration and learning through evolving media
repertoires’’ (rank 8), ‘‘developing model-driven methods
and tools for the full-scale automated generation of imple-
mentation-ready IS’’ (rank 11), and ‘‘aligning organiza-
tional objectives with IT by developing and establishing
efficient communication means’’ (rank 12). These results
reflect the foundation of IS in socio-technical systems
(Bostrom et al. 2009; Bostrom and Heine 1977). Relevant
contributions to the IS body of knowledge require the
simultaneous consideration of the technical and the social
subsystem (Gregor 2006). The identified challenges con-
cern questions of how IS can contribute and support human
activities such as problem solving, collaboration, commu-
nication, and learning as well as how technological and
social subsystems can be successfully integrated.
It is interesting to see that fundamental topics such as
the alignment of organizational objectives and IT (Reich
and Benbasat 2000; Becker et al. 2015), which have been
on the agenda for more than two decades (Henderson and
Venkatraman 1993), are indeed still seen as a challenge.
This supports the argument that IS is still lacking founda-
tional theory and applicable knowledge in important fields.
5.2.2 IS Infrastructure Challenges
Four of the identified challenges relate to IS infrastructures:
‘providing ubiquitous access to IS services’’ (rank 7),
‘integrating information systems in one single virtual
space’’ (rank 13), ‘‘embedding systems in real-life envi-
ronments’’ (rank 16), and ‘‘developing universal methods
for the translation between different coding systems’’ (rank
17). IS infrastructures afford action possibilities for groups
of users (Markus and Silver 2008; Volkoff and Strong
2013). Consequently, IS must investigate (a) how such
infrastructures are developed and (b) what action possi-
bilities they proffer to what group of users. This has
important implications for practice: First, organizations,
when implementing IT infrastructures, must carefully
consider which groups will use these infrastructures for
which purposes. Second, existent infrastructures can be re-
evaluated in the light of new, emergent action goals.
Table 5 Yes/no (‘‘Overall, do you think that the issue above is a grand challenge for IS research?’’)
Rank Challenges of IS research Yes
(%)
No
(%)
No answer
(%)
1 C04 – rethink the theoretical foundations of the IS discipline 74.5 14.5 10.9
2 C16 – leveraging knowledge from data, with the related management of high data volumes 74.5 14.5 10.9
3 C03 – proving relevance of IS research 72.7 18.2 9.1
4 C15 – integrating human and machine problem solving 72.7 14.5 12.7
5 C06 – mastering the methodological breadth/richness 65.5 23.6 10.9
6 C21 – embedding systems in real–life environments 60.0 29.1 10.9
7 C01 – identifying IS as an academic discipline 54.5 36.4 9.1
8 C12 – aligning organizational objectives with IT by developing and establishing efficient
communication means
54.5 36.4 9.1
9 C19 – supporting effective collaboration and learning through evolving media repertoires 50.9 43.6 5.5
10 C07 – increasing theoretical/methodological sophistication. 47.3 40.0 12.7
11 C09 – integrating information systems in one single virtual space 45.5 45.5 9.1
12 C17 – developing effective IS for emergency management 43.6 47.3 9.1
13 C08 – providing ubiquitous access to IS services 40.0 50.9 9.1
14 C10 – making different IT generations work together 40.0 49.1 10.9
15 C02 – adapting IS teaching to current IS research developments 34.5 54.5 10.9
16 C05 – streamlining and providing equal quality standards for different strands of IS research 34.5 36.4 29.1
17 C18 – utilizing energy informatics 32.7 56.4 10.9
18 C20 – raising collective consciousness 30.9 47.3 21.8
19 C13 – developing model–driven methods and tools for the full–scale automated generation of
implementation–ready IS
23.6 61.8 14.5
20 C14 – leveraging the ‘‘fun’’ in information systems applications 21.8 58.2 20.0
21 C11 – developing universal methods for the translation between different coding systems 16.4 65.5 18.2
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5.2.3 Societal and Ecological Challenges
Three of the identified challenges relate to important social
challenges: ‘‘developing effective IS for emergency man-
agement’’ (rank 18), ‘‘raising collective consciousness’
(rank 20), and ‘‘utilizing energy informatics’’ (rank 21).
This is consistent with a general tendency in IS research
towards increased sensitivity for societal problems (e.g.,
Elliot 2011; Melville 2010; vom Brocke et al. 2013),
reflected in recent special issues on Green IS (e.g., MIS
Quarterly, Journal of Strategic Information Systems) or
conference themes such as ‘‘Building a Better World
through Information Systems’’ at ICIS 2014 or ‘‘Reshaping
Society Through Information Systems Design’’ at ICIS
2013. We contend that IS has much to offer to solve global
challenges as the IS field is concerned with nothing less
than the ‘‘central task of managing the information of
mankind’’ (Schwartz 2014, p. 3).
5.2.4 Social and Affective Challenges
Two further challenges refer to the social and affective
aspects of IS: ‘‘leveraging the ‘fun’ in information systems
applications’’ (rank 14) and ‘‘making different IT genera-
tions work together’’ (rank 19). Prior IS research has not
considered these issues in much depth. Incorporating
affective aspects related to the design, use, and impact of
information systems is an emergent field, and efficiency
gains through concepts such as gamification (e.g., Ando-
nova 2013) or IT consumerization (e.g., Ko
¨ffer et al. 2014)
have been considered only recently. For instance, the field
of NeuroIS has been proposed to systematically investigate
affective effects through the measurement and analysis of
neuro-physiological data (Dimoka et al. 2012; Riedl et al.
2010). Studying the social and affective aspects thus calls
for interdisciplinary research that affords the IS discipline
to draw on theories from reference disciplines or, in the
best case, collaborate with scholars from these disciplines.
The example of NeuroIS is a commendable example,
Table 6 Top 3 (‘‘Please choose and rank those three grand challenges from the list that are in your opinion the most important ones’’)
Rank Challenges of IS research Total points
1 C03 – proving relevance of IS research 47
2 C15 – integrating human and machine problem solving 42
3 C16 – leveraging knowledge from data, with the related management of high data volumes 32
4 C01 – identifying IS as an academic discipline 28
5 C04 – rethink the theoretical foundations of the IS discipline 16
6 C06 – mastering the methodological breadth/richness 14
7 C08 – providing ubiquitous access to IS services 12
8 C19 – supporting effective collaboration and learning through evolving media repertoires 11
9 C02 – adapting IS teaching to current IS research developments 10
10 C07 – increasing theoretical/methodological sophistication 9
11 C13 – developing model–driven methods and tools for the full–scale automated generation of implementation–ready IS 8
12 C12 – aligning organizational objectives with IT by developing and establishing efficient communication means 7
13 C09 – integrating information systems in one single virtual space 7
14 C14 – leveraging the ‘‘fun’’ in information systems applications 7
15 C05 – streamlining and providing equal quality standards for different strands of IS research 4
16 C21 – embedding systems in real–life environments 2
17 C11 – developing universal methods for the translation between different coding systems 2
18 C17 – developing effective IS for emergency management 2
19 C10 – making different IT generations work together 2
20 C20 – raising collective consciousness 2
21 C18 – utilizing energy informatics 1
Rating: rank 1 =3 points; Rank 2 =2 points; Rank 3 =1 point; Not ranked =0 points
Table 7 Meta challenges pertaining to further developing the aca-
demic IS field
C03 – Proving relevance of IS research
C01 – identifying IS as an academic discipline
C04 – rethink the theoretical foundations of the IS discipline
C06 – mastering the methodological breadth/richness
C02 – adapting IS teaching to current IS research developments
C07 – increasing theoretical/methodological sophistication
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where well-established methods from the field of neuro-
science, which is traditionally rooted in the natural science
of biology, are now used to study IS-related phenomena,
typically based on experimental research.
6 Limitations
This study has several limitations. First, it presents chal-
lenges from the perspective of the community of inquirers
and may thus be regarded self-referential. Still, a commu-
nity of inquirers has its own context and builds upon
intersubjective agreements – and one way to understand
these intersubjective agreements is to ask those who are
involved. Second, it cannot be excluded that the original
list provided as input for the first phase of the Delphi study
biased subsequent steps. Still, we ensured that participants
were given the chance to add further challenges and
comment on those proposed in earlier rounds. Third, we
acknowledge that different researchers may have grouped
the identified challenges into other categories.
Most notably, it is possible that the replication of the
study with a different sample would reveal different results.
The sample involved in the Delphi study is not represen-
tative for the IS discipline, any specific sub-fields, or the
countries involved. In particular, the Anglo-American IS
community, which has much impacted IS research since its
inception, is underrepresented. Mertens and Barbian (2015)
investigated grand challenges with a focus on the German-
speaking community and identified a set of (partly) dif-
ferent challenges. At this, it is notable that the German-
speaking IS community highlights the role of engineering
and design. The different views and opinions show that a
common set of IS research challenges, which every group
of international researchers can agree upon, does not yet
exist.
7 Summary: Grand Challenges or Just Challenges?
In this paper we have presented the results of a Delphi
study that aimed to identify grand challenges of IS research
– but are the identified challenges indeed grand chal-
lenges? We argue that the identified challenges provide
important insights into the beliefs held in – at least parts of
– the community of inquirers that constitutes the IS disci-
pline. The results illustrate how the IS discipline is both
self-reflective and concerned with solving practical issues,
even at a global level. On the one hand, the study shows
that the IS discipline is still much concerned with itself,
and that the old debate about IS identity is still alive –
finding an identity, proving relevance, creating founda-
tional theory, or mastering the methodological pluralism
are still seen as major challenges. On the other hand, the
Delphi study has highlighted challenges of solving con-
crete, important problems related to communication and
collaboration, social progress, or sustainability. Ultimately,
IS research is a practical discipline that needs to serve the
goals of society and thus has to understand what society
demands (Seidel and Watson 2014). As a discipline, we
must understand what tangible accomplishments we can
achieve for the betterment of society. We thus promote the
stance that doing research in relevant fields – as those
suggested by the participants of our Delphi study –
Table 8 IS research challenges
Theme Research challenges
Socio-technical challenges C15 – integrating human and machine problem solving
C16 – leveraging knowledge from data, with the related management of high data volumes
C19 – supporting effective collaboration and learning through evolving media repertoires
C13 – developing model-driven methods and tools for the full-scale automated generation of implementation-
ready IS
C12 – aligning organizational objectives with IT by developing and establishing efficient communication
means
IS infrastructure challenges C08 – providing ubiquitous access to IS services
C09 – integrating information systems in one single virtual space
C21 – embedding systems in real-life environments
C11 – developing universal methods for the translation between different coding systems
Societal and ecological
challenges
C17 – developing effective IS for emergency management
C20 – raising collective consciousness
C21 – utilizing energy informatics
Social and affective challenges C14 – leveraging the ‘‘fun’’ in information systems applications
C10 – making different IT generations work together
123
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Content courtesy of Springer Nature, terms of use apply. Rights reserved.
will further help our field find its identity and prove its
relevance. Thus, it remains open whether the challenges
identified in this study are ‘‘grand’’. In many discussions
about the topic, some of the participants asked whether the
challenges are visionary enough to deserve this label.
Through our work, we have attempted to contribute to the
debate on grand challenges of our discipline in order to
improve its impact and societal relevance. It is in the very
nature of debates that alternative opinions, viewpoints, and
studies using different samples and methods must follow.
May these opinions and views result in an ongoing debate
about the challenges IS research faces.
Acknowledgments We thank Marco de Marco, Bjørn Erik Munk-
vold, Reima Suomi, and Stanislaw Wrycza, who were part of the
steering committee and contributed to consolidating the findings from
the different rounds of our Delphi study. We are also indebted to
Katrin Bergener, Sara Hofmann, and Armin Stein for their great
support in collecting and analyzing data. We would further like to
thank the editorial and review team for their developmental feedback,
which helped us shape and improve our work.
Open Access This article is distributed under the terms of the
Creative Commons Attribution License which permits any use, dis-
tribution, and reproduction in any medium, provided the original
author(s) and the source are credited.
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... Consequently, vom Brocke et al. (2015) identify a set of societal grand challenges, emphasizing the importance and responsibility of IS for providing practical solutions. Based on the qualitative input of academics, also Becker et al. (2015) identify 21 IS research challenges which can be grouped into meta challenges and overarching societal themes. Perceiving IS research legitimacy from this societal perspective, the overarching legitimacy of IS research depends on its ability to conduct research approaching grand challenges, particularly since we deal with ahistoricallytendentially praxis-oriented research field (Hirschheim and Klein, 2012). ...
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Chapter
The development and widespread dissemination of digital technologies in many sectors of the economy has led to radical changes in the management of companies, which in turn creates new problems in organizing their effective functioning. This necessitates the search for new approaches in management. Existing structures of Business Process Management are faced by socio-technical changes such as digitalization, that are fundamentally changing the ways business units cooperate and companies interact. The status of digitalization in companies has changed from a desirable to a fundamental requirement. In modern conditions, the use of digital technologies is becoming one of the key factors in ensuring the competitiveness and investment attractiveness of companies. This paper presents the results of a study of the level of digitalization of management processes in Russian companies. Based on the results of the study, a gradation of companies using neuro- and digital technologies in making management decisions is proposed.KeywordsDigitalizationBusiness processesManagement processesDigital technologies
Chapter
Several authors addressed issues in transforming or adapting current models of doing business and (re)organizing business information systems. Bichler et al. (2016) provided a summary of theoretical approaches in business and information system engineering and presented a theory of conceptual models. Chalupnik et al. (2013) presented a framework for comparing the relationships among reliability, robustness, adaptability, resilience in the context of system design. Bharosa et al. (2010) considered issues in sharing and coordinating information during disasters, within the concept of disaster management. Lee et al. (2015) emphasized the need to “restore the idea that the study of design in IS needs to attend to the design of the entire IS artifact, not just the IT artifact.” Some aspects of “modernization” through ICT is given in Faik and Walsham (2013). A framework for assembling IT-infrastructures and business models is given in Kuk and Janssen (2013), while Ashurst et al. (2012) presented a new paradigm for IT-enabled innovation. Baden-Fuller and Haefliger (2013) compared business models and technological innovations, while Hu (2014) considered the relations between business models and technological innovation performance through organizational learning. Several concepts of business models developed over the last three decades have been created under the impact of IT trends. Typical examples of such trends are the concepts such as cloud computing and business continuity. Becker et al. (2015) noted that information systems have been the major contributor to economic growth and productivity over the past decades.
Article
Full-text available
Attempts have been made to identify the grand challenges (GC) in quite different disciplines including Computer Science. These major problems should be solved within one or two generations and the solution would have great societal and economic impact. GCs are to be distinguished from the improvement of methods where the basic problem has already been solved ("emerging fields"). Among other purposes, a common understanding of GCs within a community helps to focus efforts and resources and to create a climate of competition. With our study we try to gain an impression whether a certain consensus is within reach in Business and Information Systems Engineering (BISE; Wirtschaftsinformatik, WI) in the German speaking area. We used a multi-staged opinion survey among scientists and practitioners of WI and could establish an order of precedence concerning the most important GCs. At the top ranks the item "Control of systemic risks in global networks", followed by "Humanlike Information Systems in business context", "Determining the influences on the degree of automation und integration", "Influence of WI on the solution of semantic data processing problems", and "Overcoming of communication barriers in inter-company integration". We discuss drawbacks of the GC concept as well as attempts to improve the method. One main problem is to distinguish the terms "grand challenges" (GC), "emerging fields" (EF), and "new research goals" (NR).
Article
This article discusses the role of commonly used neurophysiological tools such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems research. There is heated interest now in the social sciences in capturing presumably objective data directly from the human body, and this interest in neurophysiological tools has also been gaining momentum in IS research (termed NeuroIS). This article first reviews commonly used neurophysiological tools with regard to their major strengths and weaknesses. It then discusses several promising application areas and research questions where IS researchers can benefit from the use of neurophysiological data. The proposed research topics are presented within three thematic areas: (1) development and use of systems, (2) IS strategy and business outcomes, and (3) group work and decision support. The article concludes with recommendations on how to use neurophysiological tools in IS research along with a set ofpractical suggestions for developing a research agenda for NeuroIS and establishing NeuroIS as a viable subfield in the IS literature.
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Convincing arguments for using critical realism as an underpinning for theories of IT-associated organizational change have appeared in the Information Systems literature. A central task in developing such theories is to uncover the generative mechanisms by which IT is implicated in organizational change processes, but to do so, we must explain how critical realism's concept of generative mechanisms applies in an IS context. Similarly, convincing arguments have been made for using Gibson's (1986) affordance theory from ecological psychology for developing theories of IT-associated organizational change, but this effort has been hampered due to insufficient attention to the ontological status of affordances. In this paper, we argue that affordances are the generative mechanisms we need to specify and explain how affordances are a specific type of generative mechanism. We use the core principles of critical realism to argue how affordances arise in the real domain from the relation between the complex assemblages of organizations and of IT artifacts, how affordances are actualized over time by organizational actors, and how these actualizations lead to the various effects we observe in the empirical domain. After presenting these arguments, we reanalyze two published cases in the literature, those of ACRO and Autoworks, to illustrate how affordance-based theories informed by critical realism enhance our ability to explain IT-associated organizational change. These examples show how researchers using this approach should proceed, and how managers can use these ideas to diagnose and address IT implementation problems.
Article
The consumerization of information technology (IT) refers to consumer IT resources, such as laptops, smartphones, social media, or cloud storage, that are used for business purposes. The topic has engendered an ongoing debate among practitioners. However, a scientific approach has yet to be developed to understand the effects of IT consumerization on individual performance in the workplace. In this paper, we conduct an inductive empirical study on pilot projects in an industrial and a public sector organization. From these data, we derive key concepts and develop a theoretical framework that conceptualizes the relationship between IT consumerization and job performance. In particular, ownership and freedom of choice are interesting concepts to provide insights beyond what has already been investigated in previous research on IT-related job performance. Our findings lay the foundation for developing a substantive theory that is independent of our area of enquiry. Moreover, they show numerous connections to the body of information systems literature that sharpen our construct definitions and raise the theoretical level of the results. We outline potential avenues for future research on IT consumerization based on our study’s contributions.