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Digital Facilitation Assistance for Collaborative, Creative Design Processes

Digital Facilitation Assistance for Collaborative, Creative Design Processes
Eva A. C. Bittner
University of Hamburg
Milad Mirbabaie
University of Bremen
Stefan Morana
Saarland University
People focus more and more on creating innovations
collaboratively. Digital assistants (DAs) can accelerate
such collaborative, creative design processes by suppo-
rting people in their work. Especially in the context of
design, such as design thinking, moderators that facili-
tate collaborative, creative workshops can benefit from
the support for their teams and themselves in the form
of a DA. Based on interviews with experienced work-
shop facilitators from research and practice, we discuss
implications for the design and usage of DAs in colla-
borative, creative design processes. We identify 16 dis-
tinct capabilities of DAs for task, process and interac-
tion facilitation to guide design research and practi-
tioners’ endeavors toward helpful automated DT facili-
tation support. Moreover, we outline a research agenda
to foster future research on this young research area.
1. Introduction
Over the last years, collaboration has taken new
facets and people are focusing more and more on crea-
ting innovations collaboratively. To orchestrate this
collaboration, innovation approaches, such as Design
Thinking (DT), have been established to involve stake-
holders with different backgrounds [18, 42]. Nowadays
DT describes a problem-solving approach that can be
divided into different stages. The amount of stages can
vary with different authors from five to seven [3, 69].
DT is a framework, which integrates various tools and
techniques for problem solving [42]. Because of its
flexibility, multitude of methods, and a necessary open
mindset, DT processes usually rely on facilitation by a
skilled DT coach and are challenging for facilitators and
participants alike. Thus, DT attracted interest from aca-
demics and practitioners [46]. Different studies on DT
approaches tested the potential usage of information
technology (IT) to reduce the effort of the people invol-
ved, improve the overall experience, and resulting out-
comes. For example, Rauth et al. [50] found that the
creation of dedicated DT spaces was important for
revealing values of experimentation. The creation of
these spaces in a virtual environment was further re-
searched and allowed the participants to engage in the
process without being at the same location [35].
Another approach to enhance the DT process is to
include digital assistance systems. In their article, Zhu
et al. [15] revealed that the research phase could be
accelerated by including artificial intelligence (AI), e.g.
in the form of a digital assistant (DA), which led to
shortening of the time dedicated for this activity [21].
While AI in the form of DAs can take over different
roles in collaboration (e.g. peers), in the study at hand
we focus on DAs that act as facilitators [10]. Specific
DAs have been tested before as a facilitator in a virtual
group brainstorming session to support and organize it
[8, 62] or as an automated facilitator for the Empathy
Map Method in DT [9]. The used DA performed
successfully in moderating the session. However, with
advances in natural language processing and machine
learning, more potentials of machines taking over
facilitation tasks arise, allowing human participants to
focus more on their value-creating creative work. The
informed design of facilitation support for DT also
demands design knowledge considering the complexity
of the entire DT process and grounded in real-world
needs of facilitators and participants of collaborative
creative design processes [57]. In our research project,
we contribute to this research stream and address the
following research question:
How can collaborative, creative design proces-
ses such as Design Thinking be supported with
DA systems serving as automated facilitators?
The paper proceeds with conceptual foundations and
related work. Next, we outline the research approach
and present results from our interview study. We discuss
implications and propose avenues for further research
on the support of collaborative, creative design
processes with DAs.
2. Foundations and Related Work
2.1. Collaborative, creative design processes
and Design Thinking
Research revealed, that the phenomenon of commu-
nication is the driving force for collaborative creativity
[59]. For example, the process of sharing or criticizing
ideas, will likely encourage creativity among individu-
als in teams and hence lead to the creation of new ideas,
which is the determination of collaborative creativity.
Therefore, creative collaborators can be seen as: people
who are interacting with others to discover genuinely
new ways of thinking and doing something new together.
Collaborators play a principal role by conceiving and
carrying out the work [][60].
Collaborative creative design processes appeared
due to the increasing complexity and growing number
of design projects [71, 72]. They support employees to
work together more efficiently for a satisfactory result
[12]. One of several approaches for supporting such
processes, which is used to help solving complex
problems is called Design Thinking (DT) [22]. It aims
at inventing new patterns, instead of analyzing them,
and realizing new possibilities [28]. DT is described as
a cross-disciplinary and user centered method, which
proposes to work in teams with an interdisciplinary
background [43]. If executed well, the DT can lead to
increased team collaboration [26]. DT also aims to let
designers participate more in the whole process, enable
them to see the big picture and look upon the economic
bottom line [11]. The process of DT is often articulated
to the Stanford University’s 5-step approach
[63], an adaption of a design process activity [5]. It
consists of five recursive steps: empathize, define,
ideate, prototype, and test [28, 63]. In previous work it
has been observed that it can be challenging for a project
manager to understand the users’ real problem correctly
[27]. By conducting interviews with users, for example,
the first step empathize” can avoid project failure, as
its intention is to minimize the risk of misunder-
standings [55]. The aim of the second stage “define”, is
to transfer the knowledge gained previously into one or
multiple problem statements. The ideate” stage is
characterized by the creation of a large number of ideas
without any judgement made by others [34]. However,
during this step the previously identified problem(s)
should not be lost from sight. Based on this, prototypes
are created in stage four. In the last test” stage the
representations of the previously developed prototypes
are tested by users. In contrast to other approaches, the
design is tested early [11] in order to deduce what users
see as central functions of the product. In addition,
testing aims to reveal, which elements are classified as
rather unimportant or even unnecessary [56]. If the test
shows different results than expected, the stages can be
repeated with new assumptions. In order to obtain the
best result possible, the process is intentionally iterative.
Through the multiple passes of the five stages, several
solutions are tested until the optimal one is found [20,
28, 31]. DT projects are high-value, but complex
collaborative endeavors, which to date mostly rely on
expert human coaches. However, little is known about
how to use DAs within collaborative, creative processes.
2.2. DAs in Creative Design Processes
Basic DAs are language-enabled software, which
performs basic, information-based tasks for its users
[19]. These tasks can serve several purposes, such as
entertainment, home automation, and task management
like timers and reminders. The majority of DAs rely on
a conversational user interface instead of or in addition
to the classic graphical user interface [44]. Therefore,
the term DA is often used synonymously with the terms
chatbot, conversational agent, or dialogue system,
which all refer to the usage of natural language for the
interaction between users and the system. Thereby, the
interaction is based on using written and/or spoken
language, resulting in text-based (i.e. chatbots) and
speech-based systems (e.g. Siri or Alexa) [2, 25, 32, 41].
However, it has to be noted that DAs can include non-
conversational capabilities as well, such as expert
systems, intelligent information dashboards, or intelli-
gent search. An important enabler of DAs is the usage
of AI. By applying AI technologies, like machine lear-
ning, DAs “augment human task performance with
higher extents of interactivity and intelligence than
previous generations of DAs or traditional software
applications” [44]. Although the utilization of DAs at
the workspace is still limited in contrast to the use in
private life, their importance in work related settings is
evolving [64]. Over the last years the usage of DAs to
improve collaboration at the workspace has been gradu-
ally getting more attention [30, 37, 58]. DAs are utilized
in different branches and for various tasks, e.g. for
internal personnel communication [54], education
purposes [33] and customer services on social media
[15, 70]. Moreover, companies use DAs in order to get
reminders for upcoming meetings or deadlines [61].
Nevertheless, a recent study revealed that even at the
large business sector only 24% of companies currently
use DAs, for tasks such as voice dictation or calendar
management [65]. However, Gartner [47] predicts that
by 2021, one quarter of all digital workers will use a DA
When it comes to creative collaboration, the question
arises how DAs can be utilized for supporting and
facilitating the collaboration process. Recent empirical
and design work started to address specific aspects of
this question [10]. Bittner and Shoury [9] could present
results, in which a chatbot performed successfully in
facilitating a method within DT. This is also confirmed
by previous studies, which present similar conclusions
by showing DAs potential [2, 16, 68]. Wang et al. [55]
have also shown that brainstorming is more effective
within a DA-human interaction than in a human-human
interaction. Another advantage of DAs is their ability to
control conversations about topics and nudge their user
in other directions. This puts the system in the position
to inform the user about other possible topics. The next
step could be a DA that is able to answer for its user
based on their usual behavior [48]. On the other hand,
there are studies revealing that the users’ expectations
were not met and mediocre natural language capabilities
disappointed and frustrated users [36]. Furthermore,
answers from a DA are often rather simple compared to
answers from a real person [53].
In the study at hand, we complement this research
stream with a perspective on the entire DT process as
we seek to understand the potentials and boundaries of
DAs as facilitators to support the DT moderator or team.
As previously described, DT is a complex process that
strongly relies on a skilled facilitator, who knows the
methods well, keeps track of the team’s dynamic and
progress, and guides the team through the task.
Although DAs are unlikely to replace expert human
facilitators in the near future, they may be able to
augment their work, reduce effort, and take over
specified parts of the task. This could include for
example monitoring balanced activity levels of all team
members or the contribution of data-based background
information. Therefore, human facilitators could focus
on the crucial parts of their work while the team gets
more guidance from the DA [67]. Additionally, DAs are
neutral instances without an own agenda [67].
Therefore, humans may have fewer obstacles to discuss
critical issues as DAs do not convey verbal or non-
verbal judgment [49]. Moreover, they are able to
generate more knowledge during the design process.,
especially, if they have access to conversations or
internal company information. The DBpedia chatbot for
example is used to enhance community interactions by
analyzing data of conversations within a community.
Thus, the chatbot is able to search conversations to
check, if a question has already been answered [6].
Furthermore, the system is able to check external
sources to get more relevant information, or information
related to the actual search topic [48]. It has to be noted
that there is a variety of methods and approaches to
design a DA and those techniques are still a matter for
debate [1]. Studies explored the extent to which human-
like cues such as the name of the DA or the language
style can influence the perception of social presence [4,
29]. Because of the hedonic nature, the perceived social
presence has been shown to have a positive effect on the
perceived enjoyment and ease of use [52, 66]. Rietz,
Benke and Maedche [51] investigated the functional and
anthropomorphic design features of DAs in collabora-
tion. The result showed that anthropomorphism has a
highly significant effect on perceived usefulness. With
our work, we complement this empirical and
experimental literature and explore general capabilities
and characteristics of DAs to provide facilitation
assistance throughout entire collaborative creative
design processes.
2.3. Facilitation
Outcomes of various team collaboration processes
have been shown to depend on facilitation, i.e.
interventions performed by a facilitator in a collabo-
rative process that guide groups to achieve their com-
mon goals [7, 14, 40]. Effective facilitation is affected
by the facilitator’s skills [13, 17, 23]. Those skills requi-
red for complex collaboration processes cover the broad
range from keeping the focus of the team on the task
toward an optimum outcome, building up a positive
dialog environment by observing and intervening in an
encouraging way, encouraging participation and
controlling the collaborative setting, recognizing indi-
viduals as different and approaching them individually
[23]. According to the Facilitation Framework [13],
facilitative acts can be categorized as either targeted
toward the task, the process to accomplish this task or
activities that affect the relationship during the process.
In a similar way, Dickson et al. [24] classified facilita-
tive acts into task interventions and interactional inter-
ventions. Task interventions refer to facilitative acts to
direct the group's focus to reach the group goal. Inter-
actional interventions are facilitative acts to improve
and stimulate group dynamics and communication by
considering its members’ socio-emotional state.
Facilitators of collaboration processes such as DT,
face the challenge to master both types of facilitative
acts, which poses high demands on their training and
experience as well as high cognitive load during the
collaboration process. A lack of on-demand availability
of expert facilitators might thus impede leveraging the
potentials of DT in organizations. The “facilitator in a
box” idea is one approach to address this bottleneck by
transforming facilitative acts to system restrictions with
the automated execution of prompts implemented in
scripts [17]. However, such systems lack the ability to
utilize language skills to construct conversations similar
to humans to create a familiar environment for teams
when addressing their socio-emotional needs during the
collaboration. With the rise of DAs and their increasing
functional capabilities, automated facilitation has the
potential to become more intuitively integrated into
collaborative work practices and should also foster
robust and effective DT processes even for less expe-
rienced DT coaches and teams. However, due to
dynamic technological progress, little is known on how
this facilitation support needs to be designed from a
practitioner’s perspective. Our study contributes by
identifying task/process and relationship/interaction
facilitation support needs of DT coaches for their teams
and themselves. Furthermore, we provide a foundation
for further research on the usage of DAs in
collaborative, creative design processes.
3. Research Method
In order to shed light on facilitation support needs
and potentials for DT, we conducted a series of seven
expert interviews with DT professionals. We selected a
convenience sample of industry experts as well as
scholars from the authors’ personal networks.
Prerequisite for selection was substantial practical DT
experience (>3 years) in not only researching or
teaching, but moderating DT workshops, as we expect
them to know both their own assistance needs as well as
those of the broad range of teams they have worked
with. We focus on DT as one instance of collaborative,
creative processes because it is a well-known and
common approach utilized in industry as well as
academia. In the semi-structured expert interviews, we
asked questions on the interviewees’ experience with
DT, current needs and problems they face during DT
workshops, and questions about potential DA facili-
tation support. None of the interviewees has been
working with DAs in DT yet, which indicates the low
prevalence of DAs in this domain and calls for an open,
exploratory approach. During the interviews, we
followed the imaginative variation interview approach
[38] and asked the interviewees to imagine the usage of
DAs during their DT workshops. We specifically asked
for needed capabilities of and requirements for DAs as
well as expected positive or negative outcomes of DA
usage during DT workshops.
Organization and DT Experience
University teacher and facilitator of student DT
workshops for 5 years
University researcher on DT methods and DT
teacher for 4 years
Innovation project manager and DT facilitator in
large automotive company for 6 years
Strategy consultant and facilitator for corporate
DT workshops for 3 years
University researcher and Consultant on DT
methods for 7 years
Founder of DT Agency and Trainer for 6 years
IT consultant with 5 years of DT experience
Table 1. Interviewees DT background
The interviewees have an average DT experience of
5.57 years (SD 1.99) and the interviews lasted 53.86
minutes on average (SD 13.67). All interviews were re-
corded with interviewees consent and paraphrased. Two
researchers independently coded the interviews with
MAXQDA using an open coding approach [39]. We
organized the coding along the questions whom the DA
is supporting (the team directly or the human facilitator
in the back-office”) and what types of facilitative acts
the DA is supporting (task and process or interaction
assistance, see section 2.3) and derived desired capabi-
lities inductively. In addition, we identified initial assis-
tance needs that referred to specific DT methods and
started to identify future research topics from open
issues the interviewees raised. Subsequently, the codes
were discussed and refined among the research team and
consensus about the coding was reached.
4. Results
In the following, we discuss the findings from our
interviews (I1 I7) along the four fields of Table 2 with
respect to several capabilities the DA could have to
support the DT team and the human facilitator. We use
the term capability in the sense of Markus et al. [45] to
refer to the ability of the DA to provide a certain functio-
nality, e.g. to provide feedback to the DT team or audio
record the content of a team meeting. Table 2 summa-
rizes our results. In our analysis, we focus on the DA as
facilitator. The DA could take over further roles, such as
peer within the group. As technology is advancing and
getting even more intelligent, future DAs can take over
Task and
Minute taker
Method explanation
Workshop feedback analyst
Participant selection and
Method Selection assistance
Team sentiment feedback
Centrality and speech
share feedback
Animator / game master
Workshop feedback
Facilitation feedback /
sparring partner
Sensing Assistance
Table 2. Capabilities for digital assistant identified in the interviews
more roles and responsibilities in collaborative creative
design processes, which is out of scope of this study.
4.1. Team Task and Process Facilitation
Seven capabilities of DAs were addressed by the
interviewees that target the frontline support of the DT
team to achieve their goal and execute the DT process.
The first three capabilities within this field focus on
supporting certain recurring tasks of DT teams, namely
analyzing data, acquiring new knowledge, and creating
divergent ideas. First, I1, I3, I4, I5 and I7 stated the need
for a Data Analysis capability that helps the team to
process and make sense of the large amount of data they
gather during the DT process, e.g. from interviews or
desk research. Potential benefits of such a system are
seen in more time efficient and conclusive analysis of
large datasets from different perspectives (I3, I7), the
higher objectivity compared to human analysis (I1, I3,
I4, I5), and the revelation of patterns within the data (I3).
In a similar way, the DA could not only analyze data
provided by the DT team, but make further external data
sources accessible to the team with an Intelligent
Research capability, which applies AI to present
conditioned knowledge upon request (I2, I7) or
proactively. The DA could, for example, analyze social
media information (I2) conduct market and user
research (I3, I5) or general desk research (I4), verify
arguments in team discussions with factual data (I3), or
access knowledge from prior workshops (I2). Interview-
ees noted that such knowledge would need to be
presented in form that is quickly available, easy to
understand and use during the workshop (I7). Moreover,
the utilized sources and reasoning process by the DA
must be made transparent (I4). The third capability
refers to the teams’ frequent challenge to get and stay in
a creative work mode and think out of the box, which is
essential for the DT approach. A Divergence Fostering
capability may give creative impulses and encourage
participants to express divergent ideas. The DA could
induce utterances that participants can build on, e.g.
information from social media (I2), example ideas (I2),
guiding questions or hints to topics that have not been
addressed yet (I5, I6) or experiences from past projects
(I5). It could also give procedural guidance on how to
work divergently, e.g. by reminding of team rules for
creative work (I1) or pointing out biases toward or
against certain solution spaces (I3).
The next four capabilities focus on facilitating the
DT process as such. On a global level, interviewees
requested a Process Keeper capability compassing all
facilitative acts that help the team stick to the DT
process and execute it in the intended way, including
explanations on why a certain step is taken. Such a
capability should relieve the facilitator from some
monitoring duties and act proactively, e.g. by recogni-
zing and pointing out breaches of rules and deviations
from the planned process to the team (I1, I2, I7) as well
as by continuously visualizing the DT process and pro-
gress of the team (I5, I7). In addition to proactive
management of the whole process, Method Explana-
tion was mentioned as a beneficial capability for a
reactive DA, as numerous different DT methods exist,
and participants are often inexperienced with the overall
approach as well as specific methods. The DA should be
able to answer participants’ questions in relation to
specific methods or the current state of the process (I2,
I7). This could allow participants to act more indepen-
dently in situations, in which they need to work alone
e.g. during interviews (I5) or in sub teams, where the
human facilitator’s attention is a bottleneck (I7). A Time
Keeper capability was mentioned by I2, I3, and I7 as a
simple use case to automate a routine facilitation task
and remind the team unobtrusively. I7 noted that
sticking to the time plan for the phases is important, as
DT is about generating and rejecting ideas quickly
instead of focusing on a single idea for too long. Finally,
the team process can be supported by a Minute Taker
capability. All interviewees stated that the DT sessions
they facilitate are predominantly characterized by face-
to-face oral discussion as well as paper based hands-on
work; digital communication or collaboration tools are
hardly used. Thus, teams face the challenge to recon-
struct their decision-making processes in retrospective
and important information might be lost (I5). A DA
could transcribe the session and use natural language
processing to make generated knowledge accessible.
4.2. Team Interaction Facilitation
The interviews revealed four types of capabilities
that refer to a DA helping the team directly to monitor
and improve their interpersonal interaction. The most
prevalent capability, Team Sentiment Feedback was
picked out as a central theme by five out of seven
interviewees and relies on tracking the team conversa-
tion and sentiment. I1, I2, I3, and I6 see value in a
system reminding the team to keep the DT mindset and
be open and constructive, if it detects critical or negative
utterances or behavior in phases where they are not
appreciated. I6 notes that “the assistance system could
point out difficult or destructive situations in the
discourse, for example: you used the word ‘but’ three
times” (I6, translated). Interviewees can think of
different ways how this information is provided, e.g.
continuously on an extra screen or as an ad hoc alert.
However, they note that the feedback should be unob-
trusive to avoid disturbing the creative flow. Further-
more, it should be communicated on a team level to
allow for team self-regulation. If individual feedback is
required, it should be mediated by the human facilitator
(I3, I6) to avoid confrontation of individuals, although
I1 assumes that critique from a DA might also be better
accepted due to its neutrality and objectivity. A second
capability that requires natural language understanding
of the DA addresses Centrality and Speech Share
Feedback. Such a capability allows the DA to inform
the team, if one person dominates the conversation or if
speech shares are unbalanced between the team mem-
bers (which might conflict the aspired openness of the
DT process and negatively impact participants’ satisfac-
tion). The DA could either present speech shares (I3, I6)
and leave self-regulation to the team or motivate passive
team members directly for turn taking (I2). Such feed-
back is of importance particularly, if superiors are pre-
sent or prior team structures manifest (I3, I6, I7). A third
capability for team interaction facilitation is enabling
the DA to take over the role of an Animator or Game
Master, that motivates the team by inducing mood-
lightening recreational interventions, if participants get
exhausted in the course of a long workshop day, such as
games, music and jokes (I1, I2, I7). Finally, several
facilitators end their DT workshops with collecting
feedback from the participants. I6 mentions that this
activity results in a need for an automated and
interactive Workshop Feedback capability to allow the
discussion in the wrap-up phase of the workshop.
4.3. Facilitator Task and Process Assistance
Capabilities of the DA to support the facilitator “in
the back-office" covered activities from the preparation
of DT workshops until their wrap-up and
documentation. Initially, a DA could support facilitators
in the task of Participant Selection and Invitation. It
may reduce the facilitator’s effort for workshop
preparation and ensure an un-biased composition of
teams, e.g. with sufficient diversity (I3) or who do not
know each other too well (I7). Facilitators may also
benefit from Method Selection Assistance capability.
I2, I3, I4, and I7 wish for an intelligent method toolbox
or database that helps them to select and configure (e.g.
with templates) an effective method based on good
practices for the specific task and team setting. This
capability should cover the whole DT process and be
available upfront during workshop preparation as well
as during the workshop, when facilitators flexibly need
to adjust their plan in accordance to progress and team
dynamics. At the end of the workshop and at certain
milestones, facilitators would like to outsource parts of
their Process Documentation and Digitalization tasks
to a DA, which are seen as high-effort, non-value-
adding routine work. The DA could digitize paper-based
artefacts (post-it notes, flipcharts, paper prototypes),
reduce media discontinuities and make analogous
documents available for further digital processing.
Automated documentation can also make results and
knowledge available across workshops and build up a
knowledge base (I2).
4.4. Facilitator Interaction Assistance
The interviews revealed two capabilities to help the
facilitator execute their interaction-oriented tasks. The
first refers to Sensing Assistance for the human facili-
tator, which continuously monitors certain indicators of
team mood (e.g. facial expressions and gestures, lan-
guage and tonality) and alerts the facilitator in case of
negative team dynamics. The need for such support was
mentioned in four out of seven interviews. Interviewees
stressed in particular the challenge of keeping track of
the task, process, and team interaction at the same time
and noted that sensing assistance could relieve them
from this multitasking challenge to a certain extent. The
second capability, Facilitation Feedback by the DA
regarding their behavior and facilitation performance to
learn and improve, was requested by two interviewees.
They appreciate workshops with co-facilitators and see
a potential benefit in an objective DA to act as a sparring
partner where no other human colleague is available.
4.5. Method Specific Assistance
Surprisingly, most of the facilitation needs raised by the
interviewees did not target a specific method or tech-
nique, but experts rather asked for continuous support
throughout the complete DT process. However, for
some methods, a DA could provide specialized functio-
nality. The interviews revealed that DAs can be
particularly useful for conducting focus groups (Focus
Group Facilitation). Focus groups might be conducted
online with partly automated facilitation of a DA, which
could enable the involvement of an increased number of
participants under limited human facilitator availability.
Furthermore, a speech-to-text capability of a DA could
be used as an extension of human participants and
support other participants in writing ideas down. A
further capability of the DA could be supporting inter-
views by automatically transcribing and analyzing the
interviews (Interview Transcript Analyst). The DA
should be able to detect patterns and reveal hidden
insights. Here, the DA might cluster topics and structure
information within texts (Topic Clustering and
Information Structuring). Lastly the interviews revea-
led that a DA might be particularly helpful for brainstor-
ming sessions. During brainstorming sessions, humans
tend to throw around every thought, which comes to
their mind, which may lead to a chaotic situation. Thus,
after such a session, the DA could prepare and catego-
rize the ideas and deliver a compact summary, enabling
objective decision making. Furthermore, it could foster
different directions, by providing impulses and bring in
new ideas (Brainstorming Seeds). Participants might
build upon these impulses and develop the idea further.
5. Research Agenda
Based on our analysis of the interviews, we
identified six avenues for future research on the design
of DA for collaborative, creative design processes such
as DT. In the following, we outline these topics with
more detail. The first stream of research addresses the
capabilities of the DA. Interviewees had some concerns
about the capabilities and the resulting performance of
the system, especially when the system is not
sophisticated, and the answers are therefore not helpful,
but frustrating (I7, translated). Thus, an important
avenue for future research is to further investigate users’
needs and requirements for such DAs with respect to
their functionality in light of the fast-changing
technology landscape. Based on these findings, the
design of DAs to fulfil these requirements needs to be
investigated and prescriptive design knowledge
contributed. The second topic addresses the opportunity
to combine human and non-human facilitators to
enhance the capabilities of the DA by human in the loop
learning. Many interviewees (I2, I3, I6, and I7) sugges-
ted the functionality to rate the actions by the DA and
provide feedback to enable the system to learn. Future
research could explore how the feedback could be
collected efficiently and with as little effort as possible.
Moreover, research should explore what kind of feed-
back is required (simple ratings or more complex quali-
tative feedback). In general, a division of labor among
human and DA facilitator that leverages their unique
strengths and allows for seamless handovers, remains an
important research field to investigate. The third
avenue for future research addresses the three
interrelated topics security, privacy, and ethics.
Especially the recording and processing of person-
related information and the effects on the acceptance of
the DA was mentioned as an important challenge by two
interviewees (I2 and I6). Moreover, the utilization of
DAs was questioned in general, for example by I3 who
stated that to blindly rely on the system is an ethical
question” (I3, translated). This research stream might be
of special importance for creative work such as DT,
where the interviewees appeared to identify strongly
with their profession and where DAs might enter the
very heart of their job. The fourth avenue for future
research addresses the DAs’ impact on team collabo-
ration, which was an important aspect for all inter-
viewees. Thereby, future research could, for example,
investigate how the DA affects team dynamics (I1, I5),
if team members feel inhibited or encouraged when a
DA with a conversational interface is present (I2, I3), or
if a DA positively or negatively impacts the team’s
creativity (I4, I3, I5, I6). The fifth avenue for future
research addresses the role, agency, and authority of
the DA and was discussed very intensively by all inter-
viewees. Future research should, for example, explore,
if the DA should be an active or passive team member
(I1, I5, I6) or should explicitly not serve as a team
member (I4). Moreover, future research could
investigate, how human team members perceive the
authority of the DA, how the presence of a DA impacts
the human facilitator’s authority and analyze the
resulting impact on the team’s behavior (I1, I2, I7).
Similarly, research could explore how the neutrality and
objectivity of a DA affects the team, which consists of
humans having subjective opinions and behavior (I3).
The sixth avenue for future research addresses the
appearance of the DA. Research should explore, to
which degree the DA should be designed to be human-
like (I2, I7). Moreover, research should investigate the
questions, if a (virtual and / or physical) embodiment of
the DA is required or has a negative impact (I2, I5).
Many interviewees (I2, I3, I4, I5) stressed the impor-
tance of having a voice-based interface for interaction
during the workshop and future research could investi-
gate how to structure the dialog between team and DA
using voice-based interfaces. However, interviews also
suggested that different modes of interaction might be
necessary in different phases and tasks, e.g. the faci-
litator needs to get discreet alerts during the workshop.
6. Conclusion, Limitations and Outlook
Design spaces for useful and performant DAs for
complex and collaborative human tasks are opening up
due to advances in AI technologies. In the study at hand,
we gathered and structured an initial understanding of
the potential design space for DA facilitation support for
collaborative, creative design processes such as DT. The
interviews revealed that DT coaches see great potential
for augmenting and assisting their own and their teams’
efforts with facilitation support by a DA, both for
process/task-oriented interventions as well as for
interaction facilitation. However, it also becomes clear
that DA capabilities and how they are expressed in
collaboration need to be carefully crafted to enhance
rather than inhibit collaboration and creativity. From the
expressed needs by seven experienced DT coaches, we
identified 16 distinct capabilities of DAs that can guide
design research and practitioners’ endeavors toward
helpful automated DT facilitation support. We also
contribute initial hints on method-specific support as
well as a first research agenda outlining six avenues for
further research. Follow-up research can build on this
work by investigating, how the presence of DA
facilitators, different characteristics and interventions of
such DAs may impact relevant performance metrics of
creative teams, such as productivity or creativity of
solutions, which exceeds the scope of this paper.
Furthermore, our study results provide indication that
individual or team level process variables, e.g.
motivation, trust, cognitive load, or psychological safety
might depend on DA involvement. Future research
should seek to understand these relationships to provide
descriptive and prescriptive knowledge for human-DA-
The presented findings need to be considered with
adequate caution due to the early stage of the research
field and the inherent limitations of the exploratory
study. First, DT facilitation needs, and digitalization
potentials might vary with the available infrastructure,
the type of artefacts to be designed, and the nature of the
teams involved. While all interviewees are experienced
experts in DT and contributed rich insights, the sample
size should be extended towards further DT coaches
from different industries and within different settings.
Second, the picture needs to be complemented with the
first-hand perspective from DT participants, who are
only represented indirectly via their DT coaches’ voices
in the current data. Third, due to the innovative nature
of the topic, all expressed needs, expectations, and
attitudes toward DA facilitation support were based on
imagination, as no DAs are at use yet in the DT coaches
work. Their ideas might thus be biased by personal
experiences with DAs from other contexts or the lack
thereof. Thus, an important next step to validate the
findings from this study is to instantiate the most
promising potential capabilities and investigate their
functioning and impact in laboratory and real-world DT
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... Strohmann et al. (2018) demonstrate the need for AI assistance in DT and outline the requirements for such assistance in terms of conditions, characteristics, and tasks. Similarly, Bittner et al. (2021) outline the need for AI assistance in different DT areas, namely: Team Task and Process Facilitation, Team Interaction Facilitation, Facilitator Task and Process Assistance, Facilitator Interaction Assistance, and Method Specific Assistance. Human facilitators use various types of human input to moderate DT, such as emotional and cognitive states, arguments from discussions, ideas etc. ...
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In this empirical study, we present requirements and design principles for a virtual collaborator-an AI that acts like a virtual teammate in a physical creative workshop situation to promote creative work. Therefore, we consider relevant literature as well as insights from creative workshops in the automotive industry by conducting interviews with participants of these workshops. We derive problem areas and corresponding requirements raised by the participants. Main problem areas are individual challenges and group interaction related challenges as well as creative work with new methods and general problems working in workshop sessions. To conquer those problems, the interviewees stated requirements to the virtual collaborator mainly to get a more objective perspective in a workshop, make workshops more participative and create an open mindset and atmosphere for creative work. Besides, the interviewees wish to solve problems like building clusters of content and taking the minutes.
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Frugal describes a new strategic approach which is often praised to be the key to emerging markets. More than four billion people will live in these extremely price-conscious markets in the near future. Therefore, processes for frugal products become more and more important in engineering departments of high-tech manufacturers who want to expand to emerging markets as they are struggling with local saturated markets. However, in order to meet the needs of price-sensitive customers a frugal product should be focused on core functionalities, a significant price reduction and also on an optimized performance level taking into account the expected life time and local conditions. In order to do this successfully, a new mindset for these emerging markets and, along with this, the necessity to loosen up their traditional development processes towards a more agile approach with for example the Design Thinking (DT) process is often claimed in literature. As these literature contributions are mainly based on descriptive fundaments, this paper presents an adapted process which links the basic ideas of Design Thinking and Frugal Innovation with the focus on production systems for emerging markets. The application of the process is shown by a use case.
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Purpose This article reports the results from a panel discussion held at the 2019 European Conference on Information Systems (ECIS) on the use of technology-based autonomous agents in collaborative work. Design/methodology/approach The panelists (Drs Izak Benbasat, Paul Benjamin Lowry, Stefan Morana, and Stefan Seidel) presented ideas related to affective and cognitive implications of using autonomous technology-based agents in terms of (1) emotional connection with these agents, (2) decision-making, and (3) knowledge and learning in settings with autonomous agents. These ideas provided the basis for a moderated panel discussion (the moderators were Drs Isabella Seeber and Lena Waizenegger), during which the initial position statements were elaborated on and additional issues were raised. Findings Through the discussion, a set of additional issues were identified. These issues related to (1) the design of autonomous technology-based agents in terms of human–machine workplace configurations, as well as transparency and explainability, and (2) the unintended consequences of using autonomous technology-based agents in terms of de-evolution of social interaction, prioritization of machine teammates, psychological health, and biased algorithms. Originality/value Key issues related to the affective and cognitive implications of using autonomous technology-based agents, design issues, and unintended consequences highlight key contemporary research challenges that allow researchers in this area to leverage compelling questions that can guide further research in this field.
Conference Paper
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Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented Groupfeed-Bot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.
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Although design thinking as a teaching method has mainly been applied by architecture or economy-related study programmes, in the last decade less business-oriented courses have also discovered the potential of this creative, problem-solving approach. In 2018 Constantine the Philosopher University in Nitra, Slovakia, piloted design thinking on three courses in the Regional Tourism study programme to test whether it can foster creativity, teamwork and communication, as emphasised by many experts. The paper provides an insight into the lessons, as well as a summary of the participants’ opinions, according to which students were far more active, cooperative and creative than using traditional teaching methods.
Conference Paper
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Design Thinking proposes an innovation model, focused on creativity and with a potential for positive results to the universities that seek to increase their capacity to develop students, as it contemplates methods of investigation and development of high resolution in the users. The objective of this paper is to develop a study related to the level of students' knowledge related to Design Thinking and knowledge about the subject. For this, the project includes bibliographical research on innovation, design and Design Thinking and a research with some students of Information Technology (IT). The results obtained in this study demonstrate that the implementation of a systematic system of Design Thinking in the universities, requires several changes in the academic culture. However, the model has the capacity to generate positive results in relation to the development of innovative solutions, since it supports in advanced methods of understanding and generating ideas focused on the user and their needs.
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Digital and agile companies widely use chatbots in the form of integrations into enterprise messengers such as Slack and Microsoft Teams. However, there is a lack of empirical evidence about their action possibilities (i.e., affordances), for example, to link social interactions with third-party systems and processes. Therefore, we adopt a three-stage process. Grounded in a preliminary study and a qualitative study with 29 interviews from 17 organizations, we inductively derive rich contextual insights of 14 affordances and constraints, which serve as input for a Q-Methodology study that highlights five perceptional differences. We find that actualizing these affordances leads to higher-level affordances of chatbots that augment social information systems with affordances of traditional enterprise systems. Crossing the chasm between these, so far, detached systems contributes a novel perspective on how to balance novel digital with traditional systems, flexibility and malleability with stability and control, exploration with exploitation, and agility with discipline.
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Conversational agents (CAs) are software-based systems designed to interact with humans using natural language and have attracted considerable research interest in recent years. Following the Computers Are Social Actors paradigm, many studies have shown that humans react socially to CAs when they display social cues such as small talk, gender, age, gestures, or facial expressions. However, research on social cues for CAs is scattered across different fields, often using their specific terminology, which makes it challenging to identify, classify, and accumulate existing knowledge. To address this problem, we conducted a systematic literature review to identify an initial set of social cues of CAs from existing research. Building on classifications from interpersonal communication theory, we developed a taxonomy that classifies the identified social cues into four major categories (i.e., verbal, visual, auditory, invisible) and ten subcategories. Subsequently, we evaluated the mapping between the identified social cues and the categories using a card sorting approach in order to verify that the taxonomy is natural, simple, and parsimonious. Finally, we demonstrate the usefulness of the taxonomy by classifying a broader and more generic set of social cues of CAs from existing research and practice. Our main contribution is a comprehensive taxonomy of social cues for CAs. For researchers, the taxonomy helps to systematically classify research about social cues into one of the taxonomy's categories and corresponding subcategories. Therefore, it builds a bridge between different research fields and provides a starting point for interdisciplinary research and knowledge accumulation. For practitioners, the taxonomy provides a systematic overview of relevant categories of social cues in order to identify, implement, and test their effects in the design of a CA.
The process of design explicates the procedural knowledge of design activities, shifting theoretical conceptions across practical dimensions. Design thinking, as a creative and innovative methodology, has been established as a designerly process for non-designers to address complex problems. This article reviews the implications of introducing the design thinking methodology as a pedagogical approach in design education at LASALLE College of the Arts in Singapore, generating new knowledge to inform the research spaces of design practice and theory. Using the design thinking methodology as a sound framework to facilitate risk-taking decisions in design research and practice, students from the design specialisms of Design Communication, Product Design and Interior Design were inducted into an interdisciplinary project. The perspectives and insights arising from the collaborative, design thinking methodology are extracted, analysed and adapted to form a framework to illustrate the non-linear, circular structures of knowledge generation from theory (designerly knowing) to practice (design thinking) and research (design knowing).
Conference Paper
This industry case study explores where and how Design Thinking supports software development teams in their endeavour to create innovative software solutions. Design Thinking has found its way into software companies ranging from startups to SMEs and multinationals. It is mostly seen as a human centered innovation approach or a way to elicit requirements in a more agile fashion. However, research in Design Thinking suggests that being exposed to DT changes the mindset of employees. Thus this article aims to explore the wider use of DT within software companies through a case study in a multinational organization. Our results indicate, that once trained in DT, employees find various ways to implement it not only as a pre-phase to software development but throughout their projects even applying it to aspects of their surroundings such as the development process, team spaces and team work. Specifically we present a model of how DT manifests itself in a software development company.