# Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances,
Approaches, and Guidelines

Kihoon Son<EMAIL_ADDRESS>School of Computing, KAISTDaejeonRepublic
of Korea ,  DaEun Choi<EMAIL_ADDRESS>School of Computing,
KAISTDaejeonRepublic of Korea ,  Tae Soo Kim<EMAIL_ADDRESS>School of
Computing, KAISTDaejeonRepublic of Korea and  Juho Kim<EMAIL_ADDRESS>School of Computing, KAISTDaejeonRepublic of Korea

(2024)

###### Abstract.

Despite the growing demand for professional graphic design knowledge, the
tacit nature of design inhibits knowledge sharing. However, there is a limited
understanding on the characteristics and instances of tacit knowledge in
graphic design. In this work, we build a comprehensive set of tacit knowledge
characteristics through a literature review. Through interviews with 10
professional graphic designers, we collected 123 tacit knowledge instances and
labeled their characteristics. By qualitatively coding the instances, we
identified the prominent elements, actions, and purposes of tacit knowledge.
To identify which instances have been addressed the least, we conducted a
systematic literature review of prior system support to graphic design. By
understanding the reasons for the lack of support on these instances based on
their characteristics, we propose design guidelines for capturing and applying
tacit knowledge in design tools. This work takes a step towards understanding
tacit knowledge, and how this knowledge can be communicated.

Tacit Knowledge; Design Guideline; Graphic Design; Knowledge Capturing;
Knowledge Sharing

††journalyear: 2024††copyright: rightsretained††conference: Proceedings of the
CHI Conference on Human Factors in Computing Systems; May 11–16, 2024;
Honolulu, HI, USA††booktitle: Proceedings of the CHI Conference on Human
Factors in Computing Systems (CHI ’24), May 11–16, 2024, Honolulu, HI,
USA††doi: 10.1145/3613904.3642886††isbn: 979-8-4007-0330-0/24/05††ccs: Human-
centered computing Empirical studies in HCI

##  1\. Introduction

There is a substantial demand for professional graphic design knowledge, which
is essential for graphic designers to create effective graphic design. Graphic
designers actively share their design knowledge with others through how-to
videos (freeCodeCamp.org, 2021), design livestreams (Fraser et al., 2019;
Tharatipyakul et al., 2020; Lu et al., 2021), or design discussion forums
(Marlow and Dabbish, 2014). Also, as most graphic design work is performed
through digital design tools nowadays (e.g., Adobe
Photoshop111https://www.adobe.com/products/photoshop.html, Adobe
Illustrator222https://www.adobe.com/products/illustrator.html,
Figma333https://www.figma.com/, etc.), it is possible to track the designer’s
actions performed within a design tool and the corresponding output. This
implies that professional designers can share their work in detail with
novices to enable them to learn from their detailed processes.

However, since design knowledge is inherently tacit, it is challenging to
capture and share with others a designer’s knowledge fully exhibited within a
design tool (Bernal et al., 2015). Knowledge exists on a continuous spectrum,
ranging from tacit to explicit, with tacit knowledge and explicit knowledge
possessing precisely opposite characteristics (Leonard and Sensiper, 2011).
Unlike explicit knowledge, tacit knowledge is knowledge that is difficult to
communicate verbally or through text and, at the same time, it can only be
shared by the person who possesses that knowledge. Examples of explicit
knowledge include design theories and widely adopted design rules. Examples of
tacit knowledge, on the other hand, encompass skills like “sensing that the
composition of design elements is unnatural given the design context” or
“conducting sufficient reference research before starting a design.” Since
design knowledge is synthesized through personal traits, practical expertise,
and experiences with various project tasks (Bernal et al., 2015), design
knowledge inherently contains diverse tacit knowledge.

To enhance the sharing of design knowledge from individuals who possess tacit
knowledge to those without it, it is crucial to comprehensively understand and
investigate the presence of tacit knowledge in graphic design. In other words,
it is crucial to understand the types of tacit knowledge, the characteristics
associated with each type, and how existing design tools support this tacit
knowledge. Such an understanding will not only shed light on how tacit
knowledge is defined in graphic design but will also aid in developing
strategies to capture this knowledge from its owners and disseminate it to
those without this expertise.

With this goal, this work aims to 1) preferentially construct a comprehensive
set of characteristics (Table 1-Characteristic) that tacit knowledge can
possess based on prior work; 2) gather 123 actual examples of tacit knowledge
(referred to as instance in this work; Table 1-Instance) and their
characteristics through interviews and annotation studies with graphic design
experts; and 3) extract element, action, and purpose from one tacit knowledge
instance (Table 1-Element, Action, and Purpose) and categorize them to
thoroughly understand how tacit knowledge is used in the graphic design field.
This analysis shows how the tacit knowledge instance of graphic design is
being used through what elements, through what actions of designers, and for
what purposes. Subsequently, we systematically reviewed existing graphic
design support tools or systems (referred to as approach; Table 1-Approach) to
analyze instances of tacit knowledge that are not adequately supported by
these tools and the reasons behind such limitations. Our findings revealed
that tacit knowledge in graphic design is most commonly applied to elements
within designs through actions related to cognition and manipulation, with the
purposes of producing visually appealing designs that consider the audience.
Overall, tacit knowledge is highly dependent on personal intuition and
expertise, but our findings also showed that it is characterized as shareable
with others through non-verbal channels in the design process. Still, existing
approaches to support graphic design cannot adequately support tacit knowledge
conducted by the designer’s actions related to sensing and defining, or the
knowledge is used for targeting audiences and visuals.

Following our investigation, we propose that tacit knowledge of graphic design
can be effectively passed on to others. To facilitate this, we propose two
types of design guidelines: 1) guidelines to capture tacit knowledge from
designers who possess it and 2) guidelines to support designers who lack this
knowledge in applying tacit knowledge in graphic design tools. The capture
guidelines describe what type of designer’s actions and information the tool
should track within and outside a design tool. These guidelines also suggest
how the knowledge can be captured by supporting tools that enable designers to
annotate their rationale behind specific design actions and patterns. The
application guidelines consist of suggestions related to the design of
interactions in tools that support designers in learning-by-doing by
leveraging captured tacit knowledge. Further, we discuss the designers’
personal viewpoints in understanding and interpreting the overall tacit
knowledge and design creativity. We also discuss the generalizability of our
investigation methods to other domains such as coding, writing, or research.

Our contribution is three-fold:

  * •

We analyzed tacit knowledge instances in graphic design according to their
element, action, and purpose, deeply understanding how this knowledge exists
with its characteristics.

  * •

We identified types of tacit knowledge inadequately addressed by existing
graphic design support approaches and explained the reasons based on their
characteristics.

  * •

We demonstrated that tacit knowledge of graphic design could be shared beyond
verbal channels and proposed design guidelines for design tools capable of
capturing and applying this knowledge.

##  2\. Related Work

###  2.1. Knowledge Sharing Methods in Design Field

Design knowledge sharing is a critical process for developing the design
profession. In the design field, there are various ways to share design
knowledge. Traditionally, designers have shared their design knowledge through
design studios (Oxman, 2004; Wilcox et al., 2019), critiques (Fischer et al.,
1993; Uluoglu, 2000), and feedback (Xu et al., 2014; Kotturi and Kingston,
2019). In design communities, designers actively share their knowledge or
project experiences through discourse (Marlow and Dabbish, 2014) that also
helps experts build knowledge (Goodwin, 2015), and the set of design
principles shared by experts in this way also helps novice’s knowledge-sharing
process (Yuan et al., 2016). Recently, various online channels have been
formed, allowing designers to share their design knowledge with larger
audiences through design live streaming (Fraser et al., 2019; Tharatipyakul et
al., 2020; Yang et al., 2020; Lu et al., 2021) or self-disclosure (Kou and
Gray, 2018).

However, it is hard to share design knowledge because it includes much tacit
knowledge in nature (Wong and Radcliffe, 2000). Visual materials are often
utilized to convey clearer design knowledge, but even designers often do not
know exactly which part of their knowledge is tacit, why, and how to share the
knowledge with others (Cross, 1982). In this respect, before considering how
to share or communicate professional design knowledge in the graphic domain,
this study aims to identify the instances and characteristics of tacit
graphical design knowledge first.

###  2.2. Tacit Nature of Design Knowledge

Polanyi (Polanyi, 2012) proposed the concept of tacit knowledge as one of the
natures of human knowledge, which has the opposite characteristics of explicit
knowledge. Explicit knowledge can be straightforwardly represented and
communicated between people (Collins, 2010). On the contrary, tacit knowledge
has the following features: gained through experiential learning and
reflection (Collins, 2010; Schon, 1983; Sharmin and Bailey, 2013), difficult
to sufficiently express through verbal means (Polanyi and Sen, 2009),
accessible only through the knowing subject (Lam, 2000), deeply embedded in
action patterns and external representation (Schön, 2017; Suwa and Tversky,
2002), and expressed differently depending on the context (Nonaka, 1994).
Tacit knowledge is crucial, as it significantly enhances the excellence of
knowledge and its application to tasks. (Seidler‐de Alwis and Hartmann, 2008;
Goffee and Jones, 2017; Kirsh and Maglio, 1994). Since this knowledge is also
an essential part of human knowledge, the HCI community has investigated tacit
knowledge to unpack and utilize it (Prpa et al., 2020; Kirsh, 2013a).

Design knowledge has many aspects that are tacit in nature (Bernal et al.,
2015). For example, the design principle is one of explicit knowledge, but
knowing the right time to apply the theory is an example of tacit knowledge.
In a design process, designers usually make design decisions at the cognitive
level (Kirsh, 2013b; Faste, 2017). Due to decisions being derived from
intuition (Faste, 2017) or experience (Nakakoji and Fischer, 1995), there are
frequent situations in which designers cannot clearly explain what they did
(Cross, 1982). This aspect of design knowledge makes sharing or communicating
knowledge more challenging in the learning context.

However, although various tacit knowledge was investigated by prior work,
there is a limited understanding of what instances of tacit knowledge exist or
what characteristics cause the communication process to be difficult. Also,
there is no comprehensive investigation of how the prior work on graphic
design support approaches covered what instances of tacit knowledge.
Therefore, this work focuses on specifying what characteristics and instances
of tacit knowledge exist in graphic design.

Table 1. The definition and examples of each term used in this paper.

Terms |  Definition |  Example
---|---|---
Characteristic |  Features that tacit knowledge may possess |  The knowledge that is difficult to articulate verbally
Instance |  Specific examples of tacit knowledge found in graphic design |  Harmoniously arranging images to match the background
Analytics Schemes | Element |  Where tacit knowledge is revealed or manifested in graphic design |  Harmoniously arranging images to match the background
Action |  Actions performed by the subject to reveal their tacit knowledge |  Harmoniously arranging images to match the background
Purpose |  Purposes for the subject of using their tacit knowledge |  Harmoniously arranging images to match the background
Approach |  Methods from prior work supporting the use of tacit knowledge |  Vinci: an intelligent graphic design system for generating advertising posters (Guo et al., 2021)

##  3\. Method Overview and Research Questions

###  3.1. Goals and Research Questions

Our study aims to investigate tacit knowledge in graphic design deeply by
identifying the characteristics and instances of tacit knowledge, as well as
by reviewing prior approaches for supporting the usage of design knowledge.
The ultimate goal is to propose design guidelines for systems that support the
capture and application of tacit knowledge. Based on these research goals, we
defined three research questions:

  * •

RQ1. What types of tacit knowledge instances exist in graphic design, and what
are the differences between these types regarding their characteristics?

  * •

RQ2: How do prior approaches in graphic design support the usage of tacit
knowledge instances, and what parts lack support?

  * •

RQ3. How should tools that support the sharing, communicating, and using tacit
knowledge be designed?

###  3.2. Definition of Terms

This research employs various terms to analyze tacit knowledge. For clarity,
we first provide definitions and examples of these terms in this section.
First, the term Characteristic refers to features that tacit knowledge may
possess (e.g., knowledge that is “difficult to articulate verbally”). Next,
the term Instance used in this work refers to specific examples of tacit
knowledge found in graphic design, like “harmoniously arranging images to
match the background”. Elements, actions, and purposes serve as schemes to
analyse instances in detail; in the previous example, the element is “image”,
action is “arrange”, and the purpose is “harmony”. Utilizing these schemes, we
analyzed instances collected from graphic design experts. Finally, the term
“Approach” signifies methods from prior work that support the use of tacit
knowledge. As these terms are frequently used, we have compiled definitions
and examples for each term in Table 1.

###  3.3. Research Procedure Overview

Our research consisted of five main steps, which are illustrated in Figure 1.
First, we examined the characteristics that are widely used in prior
literature to describe tacit knowledge and created an integrative set of these
characteristics (Figure 1-a). Subsequently, we conducted an interview study
with graphic design experts, where we asked them to recollect instances of
tacit knowledge and to annotate the characteristics of these instances (Figure
1-b). To analyze the collected data in-depth, we also extracted the elements,
actions, and purposes from each instance and conducted qualitative coding
(Figure 1-c). As a result, we analyzed the characteristics, elements, actions,
and purposes associated with the instances. Next, we conducted a systematic
literature review to understand why prior approaches may or may not support
certain instances due to their specific characteristics (Figure 1-d). Through
this series of research processes, we ultimately propose design guidelines for
supporting the capture and application of tacit knowledge in the graphic
design process (Figure 1-e).

Figure 1. Research overview. The diagram illustrates the overall research
process of this work. Each research step is depicted, with specific sections
marked corresponding to the description of each stage.

The diagram shows the overall research procedures with arrows. First, (a) is a
literature review process for collecting tacit knowledge characteristics. (b)
shows that the authors did an interview study with graphic design experts to
collect the tacit knowledge instances. In the study, the designers also
conducted an annotation task between the instances they mentioned and the
characteristics set. (c) is the open coding process to analyze the instance of
the collected tacit knowledge. Then, the authors conducted a systematic
literature review to investigate what type of instance is still uncovered at
(d). Based on these series of research steps, this paper suggests the design
guidelines for capturing and applying tacit knowledge at (e).

##  4\. Compiling Characteristics of Tacit Knowledge Through a Literature
Review

###  4.1. Tacit Knowledge Characteristics

According to Leonard and Sensiper (Leonard and Sensiper, 2011), knowledge
exists on a spectrum from tacit and unconscious knowledge to explicit and
codified knowledge. The majority of knowledge falls somewhere in this
knowledge spectrum. This can be interpreted as having varying degrees of
tacitness (Odell et al., 1983; Howells, 1996) depending on how difficult it is
to share and communicate. Hence, identifying the characteristics of each tacit
knowledge has to be the first step to understanding it deeply.

For example, “sensing that the composition of design elements is unnatural”
could have more intuition-related characteristics than “conducting sufficient
reference research before starting a design”. In addition, if the former
instance is more challenging to share with others than the latter, we can say
that the former instance has a higher tacitness than the latter. Therefore, it
is crucial to understand these characteristics and how they manifest in
different pieces of tacit knowledge to understand the knowledge. While prior
work has described some of these characteristics, to the best of our
knowledge, no study has provided a comprehensive list with the described
characteristics from prior work. In this background, we first assembled and
organized the characteristics defined in prior work through a literature
survey.

Table 2. A comprehensive list of the characteristics of tacit knowledge and
their categories.

A table shows the result of the literature review on tacit knowledge. The
category characteristics and paper of the tacit knowledge are illustrated.
There are four categories, and 14 characteristic codes are shown in the table.
In category, possession, expression, acquisition, and manifestation exist.

Category |  Characteristic Code | Prior work
---|---|---
Possession |  Individuals are able to recognize the existence of their tacit knowledge | (Gertler, 2003; Joia and Lemos, 2010; Lam, 2000)
Individuals possess knowledge that is personal, such as personal skills or routines | (Ambrosini and Bowman, 2001; McAdam et al., 2007; Erden et al., 2008; Boiral, 2002; Armstrong and Mahmud, 2008; Leonard and Sensiper, 2011; Seidler‐de Alwis and Hartmann, 2008; Panahi et al., 2013; Desouza, 2003; Eraut, 2000; Lam, 2000)
Individuals possess knowledge that is practical, such as rules or conventions in practice (e.g., specific to a certain industry or domain) | (Howells, 1996; Ambrosini and Bowman, 2001; McAdam et al., 2007; Koskinen and Vanharanta, 2002)
Expression |  Knowledge is expressed in human actions | (Koskinen et al., 2003; Seidler‐de Alwis and Hartmann, 2008; Collins, 2010; Odell et al., 1983)
Knowledge is hard to articulate but can be inferred via non-verbal channels | (McAdam et al., 2007; Boiral, 2002; Armstrong and Mahmud, 2008; Collins, 2010; Koskinen and Vanharanta, 2002; Goffin and Koners, 2011; K. Kakabadse et al., 2001)
Knowledge is not codified and difficult to write down or formalize | (Howells, 1996; Ambrosini and Bowman, 2001; McAdam et al., 2007; Erden et al., 2008; Boiral, 2002; Gertler, 2003; Seidler‐de Alwis and Hartmann, 2008; Foos et al., 2006; Desouza, 2003; Joia and Lemos, 2010; Holste and Fields, 2010; Nash and Collins, 2006)
Knowledge is difficult to explain in terms of the decision rules that were used to reason their application | (Ambrosini and Bowman, 2001; McAdam et al., 2007; Collins, 2010; Eraut, 2000)
Knowledge is difficult to share or communicate | (Boiral, 2002; Gertler, 2003; Panahi et al., 2013; Koskinen and Vanharanta, 2002; Johannessen et al., 2001; Haldin‐Herrgard, 2000; Desouza, 2003; Joia and Lemos, 2010; Goffin and Koners, 2011; Spender, 1996; K. Kakabadse et al., 2001)
Acquisition |  Knowledge is implicitly learned by doing and using | (Howells, 1996; McAdam et al., 2007; Koskinen et al., 2003; Boiral, 2002; Gertler, 2003; Johannessen et al., 2001; Haldin‐Herrgard, 2000; Foos et al., 2006; Joia and Lemos, 2010; Mascitelli, 2000; K. Kakabadse et al., 2001; Nash and Collins, 2006)
The process of acquiring knowledge is affected by personal traits, including mental models, values, beliefs, perceptions, and insights | (McAdam et al., 2007; Koskinen et al., 2003; Leonard and Sensiper, 2011; Seidler‐de Alwis and Hartmann, 2008; Koskinen and Vanharanta, 2002; Foos et al., 2006; Desouza, 2003; Holste and Fields, 2010; K. Kakabadse et al., 2001)
Manifestation |  Knowledge is specific to the context in which it is used | (Ambrosini and Bowman, 2001; McAdam et al., 2007; Koskinen et al., 2003; Boiral, 2002; Johannessen et al., 2001; Holste and Fields, 2010; K. Kakabadse et al., 2001)
Knowledge is specific to professionals and their experiences | (Koskinen et al., 2003; Boiral, 2002; Leonard and Sensiper, 2011; Holste and Fields, 2010; Eraut, 2000; Mascitelli, 2000)
Knowledge is related to an individual’s personal intuition, gut feelings, or experience | (Erden et al., 2008; Armstrong and Mahmud, 2008; Panahi et al., 2013; Koskinen and Vanharanta, 2002; Haldin‐Herrgard, 2000; Desouza, 2003; Holste and Fields, 2010; Spender, 1996; K. Kakabadse et al., 2001)
Knowledge is conventional knowledge that is affected by societal values, tradition, or culture | (Collins, 2010)

###  4.2. Search Keywords and Exclusion Criteria

To define a comprehensive set of tacit knowledge characteristics within the
vast literature, we narrowed our survey by setting limited search keywords.
Specifically, we used the search keywords “Tacit knowledge” and “Definition of
tacit knowledge” to identify literature in Google
Scholar444https://scholar.google.com/ and the ACM Digital
Library555https://dl.acm.org/. Then, to construct a set based on the most
widely used characteristics of tacit knowledge, we only included literature
with more than 300 citations with no restrictions on the publication year.
Second, we filtered out papers that had no specific sections or parts
describing the characteristics of tacit knowledge. Based on this procedure, we
first collected 398 papers through the search keywords, and filtered out 321
and 49 papers with the first and second constraints, respectively. As a
result, we landed on a set of 28 research papers. We found 13 papers with the
first keyword and 15 papers with the second keyword.

###  4.3. Coding Process of Tacit Knowledge Characteristics

From the sections or fragments in the 28 papers that described the
characteristics of tacit knowledge, we collected 102 data points related to
these characteristics. Specifically, we collected data from a paper in the
following situations: 1) when a particular section name includes
“characteristics”, “features”, or “definitions” of tacit knowledge, and 2)
when those are explained like “Tacit knowledge characteristics are A or B”.
Two authors conducted an iterative coding process on the data points to merge
overlapping characteristics into one code and build a category for similar
codes. The authors classified each data point by merging and dividing it into
groups. Next, three authors, including another author, met and reviewed the
groups and finalized the categorization of code groups. All steps were
repeated until the participating authors reached a consensus.

Through this process, we created a list of 14 characteristics with 4
categories in tacit knowledge (Table 2). The categories include “Possession”,
“Expression”, “Acquisition”, and “Manifestation”. “Possession” is related to
how to possess tacit knowledge; “Expression” is about how to express tacit
knowledge; “Acquisition” is related to acquiring tacit knowledge one does not
have; and “Manifestation” is about in what context and situations tacit
knowledge is used.

##  5\. Interview Study with Professional Graphic Designers

To identify concrete instances of tacit knowledge in the graphic design domain
and understand what characteristics these instances possess, we conducted an
interview study with 10 graphic design experts (Section 5.1). Through the
study, we collected 123 instances of tacit knowledge. Then, we analyzed how
tacit knowledge is manifested in graphic design by performing a qualitative
coding of the collected instances according to the element where the tacit
knowledge is revealed, the action taken by the knowledge subject to use the
tacit knowledge, and the purpose of using that knowledge. This study and
analysis aim to address the first RQ of our work through a set of more
detailed RQs:

  * •

RQ1. What types of tacit knowledge instances exist in graphic design, and how
do they differ regarding tacit knowledge characteristics?

    * –

RQ1-1. Through what elements, actions, and purposes is tacit knowledge in
graphic design used?

    * –

RQ1-2. In terms of the element, what characteristics do instances have where
tacit knowledge is revealed?

    * –

RQ1-3. In terms of the action performed to use the tacit knowledge, what
characteristics do instances have?

    * –

RQ1-4. What characteristics do instances have regarding the purpose of using
tacit knowledge?

###  5.1. Interview Study

####  5.1.1. Interviewees and Recruitment

Since one of the characteristics of tacit knowledge is “professional”
(Koskinen et al., 2003; Boiral, 2002) and “experienced” (Howells, 1996; McAdam
et al., 2007; Koskinen et al., 2003) knowledge, we recruited graphic design
experts with at least five years of experience. A total of 10 participants
(Table 3) were recruited through the online communities and social media
(Kakao666https://www.kakaocorp.com open group chat room or
Facebook777https://www.facebook.com/ group for graphic designers).
Specifically, we recruited graphic design experts in BX 888Brand experience
design., UI/UX, product, and editorial 999Closely related to physical printing
design like posters, packaging, or magazines. design. The participants
consisted of 3 male and 7 female designers, and their average length of
experience was 8.1 years (Min=5, Max=13, and SD=2.96).

Table 3. Information regarding the domain, experience, and type of design work
performed by the 10 designers interviewed.

Table 2 shows the participant’s list and their information. Participant’s ID,
gender, age, domain, experience, and their current work and examples of work
are illustrated in the table.

ID | Gender | Age | Domain | Experience | Current work (Examples of work)
---|---|---|---|---|---
P1 | Male | 31 | Product | 7 years | B2B service design project manager (from planning to actual service design)
P2 | Female | 30 | Product | 5 years | IT product services project manager (planning, designing, prototype testing, and validation of service)
P3 | Male | 39 | BX | 13 years | Branding design work related to broadcast media-focused (TV, news, and internet)
P4 | Female | 34 | BX | 8 years | Branding design focusing on e-commerce (from packaging to design promotion)
P5 | Female | 35 | UI/UX | 10 years | E-commerce service UI/UX design (data analysis-based design or seller side UI/UX design)
P6 | Male | 34 | UI/UX | 7 years | In-house UI/UX design (landing page, detailed description page, or company web page design)
P7 | Male | 31 | UI/UX | 5 years | UI/UX design in coding education start-up (UI/UX design of the actual page design through communication with developers)
P8 | Female | 33 | Editorial | 7 years | Editorial design of various channels (advertisement, signboard, poster, pamphlet, card news, logo, etc.)
P9 | Female | 32 | Editorial | 6 years | Kids related editorial design (product packaging, poster, advertisement, including character design)
P10 | Female | 36 | Editorial | 13 years | Editorial design of various channels (museum or cosmetics catalog, mobility poster, proposal design for large companies)

####  5.1.2. Protocol

Two authors led the study (90 minutes). First, the study started by explaining
the definition of tacit knowledge (5 minutes). Next, based on the pre-defined
questions in a semi-structured interview format, we asked participants to
think of and explain instances of tacit knowledge in the domain they work in
(35 minutes). In their work process, we asked whether tacit knowledge exists
in their domain, how professional and novice designers differ in using tacit
knowledge, and what the specific experience related to tacit knowledge would
be. What is needed to capture and apply tacit knowledge in the design process
was also asked. During the interview, another author listed the explained
instances of tacit knowledge.

After the interview, participants were asked to look through the instances
they mentioned and merge similar ones or divide some mixed instances. For
example, P10 explained “A sense of judging strange balance, symmetry, ratio,
or color of elements” as tacit knowledge in graphic design. However, P10 again
divided it into “A sense of judging strange balance, symmetry, ratio of
elements” and “Defining a good color scheme for a design” because P10 thought
the color was a distinct element compared to others. After that, we asked them
to check the proper characteristics for each instance by looking at the
characteristics set (Table 2) we built (50 minutes). Participants were
compensated with 105,000 KRW (Approx. 80 USD). The studies were conducted
remotely through Zoom101010https://zoom.us/, and the sessions were recorded.

###  5.2. Analysis Method and Goal on Tacit Knowledge Instances

To investigate the collected instances thoroughly, we conducted open coding on
each instance and analyzed the characteristics annotated by experts. This
section describes the analysis scheme and process of open coding, as well as
the annotation result analysis method.

####  5.2.1. Analysis Scheme: Element, Action, and Purpose of Tacit Knowledge

We defined three perspectives of tacit knowledge: the element where tacit
knowledge is revealed or manifested in graphic design, the action performed by
the subject to reveal their tacit knowledge, and the purpose of using the
knowledge. In prior work on the tacit knowledge of design, Bernal et al.
(Bernal et al., 2015) investigated whether tacit knowledge could be supported
by mapping the actions of design experts (i.e., Generation, Evaluation,
Selection, and Integration) to a computational approach that can support these
actions. However, in order to define tacit knowledge as knowledge that can be
captured and reused, we need to 1) concretize abstract actions, such as
Integration (Bernal et al., 2015), into low-level actions, and 2) specify the
target or element in which this action is performed. Furthermore, the context
in which the knowledge is used, including the background and purpose of the
knowledge, needs to be specified when the designer applies this knowledge.
Therefore, we defined not only the specific actions of the designer, but also
the element and purpose in which these actions are performed as a scheme to
code and analyze instances.

####  5.2.2. Open Coding Process

Two authors conducted an open coding (Corbin and Strauss, 1990; Khandkar,
2009) on the collected tacit knowledge instances from the participants (Total:
123; Mean: 12.3; SD: 1.49). Before starting the coding process, the authors
excluded instances where two or more schemes did not emerge. For example, the
instance “Design an appropriate action to achieve a certain purpose” (P2) was
excluded. A total of 10 instances were excluded, and coding was carried out
for 123 instances as follows. For example, in the “Adjusting the right font
size, boldness, line spacing, and margin according to the hierarchy of the
content” (P10), the element would be Font, the action is Adjust, and the
purpose is Considering the importance of information. The two authors assigned
codes only when these were specifically stated in the instance for element,
action, and purpose.

There were instances without a specified purpose (n=10) and instances that
could be revealed through all elements (n=22) in the collected data points.
For example, “The ability to find the right amount of blank space in design”
(P7) doesn’t indicate the purpose of this knowledge. Here, the authors did not
make additional interpretations but only performed coding for the scheme
specified in the instance (in this case, element and action). As another
example, the “The ability to design well by blending in, rather than just
following the trend” (P2) includes a purpose to In line with design trend, but
since it does not specifically state through which element it is, the authors
have assigned the element of this instance as an Overall code. The meaning of
the Overall code implies that this knowledge spans all elements.

As a result of iterative qualitative coding for each of the element, action,
and purpose of the collected 123 tacit knowledge instances, we defined 4
categories and 13 codes (exclude Overall) in the element, 3 categories and 11
codes in action, and 5 categories and 16 codes in purpose (Table 4).

Table 4. Tacit knowledge in graphic design. The results of the open coding
process are in Section 5.2.2. The table shows the defined code and category.
The relatively lowest/highest count of the category (in each coding scheme) is
highlighted in  red/ green.

Table 3 shows the code and category of tacit knowledge in graphic design.
Since our qualitative coding was conducted regarding the instance’s element,
action, and purpose, three coding schemes are on the left side. Then, the
category, code, code count, and category count are shown. There are five
categories (inner design element, design work pattern, contents, outer design
source, and overall) in element, three categories (cognition, utilization, and
manipulation) in action, and seven categories (environment, audience, visual,
individual design style, design contents, and empty) in purpose. The highest
number of categories is colored green, and the lowest number of categories is
colored red.  Coding scheme Category Code (example of instance) Code count
Category count (%) Element Inner design element Color (e.g., Pairing colors
based on what the target demographic favors by P9) 15 64 (56.64%) Font (e.g.,
Combining aesthetically pleasing fonts by P2) 12 Image (e.g., Harmoniously
arranging images to match the background by P2) 3 Layout (e.g., Creating a
layout that guides the audience’s actions by P4) 6 Size/ratio (e.g., A sense
knowledge of proper size and proportion for balanced design by P1) 8 Space
(e.g., Judging visually comfortable margin space by P6) 7 User experience
(e.g., Predicting the mental model of the target audience by P5) 13 Design
work pattern Design tool (e.g., Knowing how to use various tools according to
the design situation by P8) 2 8 (7.08%) Work style (e.g., A work pattern
conducting sufficient design research by P10) 6 Contents Information hierarchy
(e.g., Judging the importance of information based on the content by P9) 3 5
(4.42%) Writing (e.g., Understanding subtle differences in nuance in text
phrases by P5) 2 Outer design source Design theory (e.g., Arranging the layout
based on the golden ratio rule by P6) 6 11 (9.73%) Reference (e.g., Feeling
inspired by unrelated sources by P6) 5 Overall Overall (e.g., Adapting styles
that are in line with the current trends by P5 -¿ “Various elements affect the
style”) 25 25 (22.12%) Action Cognition Possess (e.g., Possessing knowledge
about the usage of easily readable fonts by P10) 10 37 (32.74%) Feel (e.g.,
Feeling inspired by unrelated sources by P1) 8 Predict (e.g., Predicting
design error cases that others may not consider by P6) 9 Judge (e.g., Judging
the harmony between text and image by P8) 10 Utilization Find (e.g., Finding
fonts that fit well with the style/mood by P8) 3 15 (13.27%) Adapt (e.g.,
Appropriately adapting basic design principles according to the design
situation by P7) 12 Manipulation Create (e.g., Creating a layout that guides
the user’s actions by P4) 23 61 (53.98%) Arrange (e.g., Arranging objects so
that the space doesn’t look empty by P9) 6 Adjust (e.g., Adjusting the
intensity of visualization to balance the design elements by P2) 17 Combine
(e.g., Combining aesthetically pleasing colors by P2) 8 Define (e.g., Defining
the proper size of an element by P1) 7 Purpose Environment External constraint
(e.g., Possessing the knowledge of creating designs at a low cost by P5) 14 24
(21.24%) Design requirement (e.g., Adapting design elements to conform to the
brand’s identity by P7). 7 In line with trends (e.g., Adapting the design
style to match a trend by P5) 3 Audience Favored by the public (e.g., Creating
designs with a focus on mainstream appeal by P3) 2 32 (28.32%) User-friendly
(e.g., Predicting the design from the user’s perspective by P7) 7 Reducing
errors (e.g., Predicting design error cases that others may not consider by
P6) 7 Considering the target audience (e.g., Pairing colors based on what the
target demographic favors by P9) 7 Guiding the audience (e.g., Determining the
proper size of an element to guide the user’s attention by P9) 9 Visual
Enhancing readability (e.g., Increasing readability by adjusting the
attributes of the font by P2) 7 34 (30.09%) Balanced (e.g., Arranging objects
so that the space doesn’t look empty by P9) 5 Aesthetically pleasing (e.g.,
Combining aesthetically pleasing colors by P2) 13 Unexpected (e.g., Finding
unexpected color palette from completely different domains by P7) 4 Harmonious
(e.g., Judging the harmony between text and images by P8) 5 Individual design
style Preference (e.g., Possessing a personal style preference by P8) 3 3
(2.65%) Design contents Considering the importance of information (e.g.,
Adjusting the font’s emphasis according to the information importance by P2) 7
12 10.62%) In line with the design theme (e.g., Defining what part of the
topic to focus on when setting a design concept by P10) 5 Empty Empty (e.g.,
Defining an element’s size by P1 -¿ “There is no explicitly described
purpose”) 8 8 (7.08%)

####  5.2.3. Analysis on Characteristics of Instances

To specify the characteristics of the different elements, actions, and
purposes of tacit knowledge from the category level of instance, we analyzed
the interviewees’ annotations from the study. We calculated how often each
characteristic was annotated as present in each tacit knowledge instance.
Through this analysis, we report how the distribution of tacit knowledge
characteristics exists and what differences exist in each category.

Figure 2. Annotation analysis results of tacit knowledge characteristics. The
chart shows how many annotations are obtained from the 10 graphic designers in
each category of element, action, and purpose as a ratio (the calculation
method is in Section 5.2.3. The legend of (a) to (d) indicates each category
of Table 2.

Figure 1. Figure 1 shows that the annotation analysis results in tacit
knowledge instances and characteristics. The analysis was also conducted
regarding the three coding schemes (element, action, and purpose). The bar
chart is illustrated, and the ratio of each characteristic annotated is shown
in each category level. The below part contains the legend of characteristics
that came from Table 1. Each characteristic has a different color.

###  5.3. Findings

####  5.3.1. RQ1-1: Elements, Actions, and Purpose of Tacit Knowledge in
Graphic Design

Our analysis results show that graphic designers use tacit knowledge in
elements related to inner-design elements, through actions centered on
manipulation, and to create visually pleasing designs at the category level
(Table 4-Category).

The most salient elements where tacit knowledge is evident are the color,
font, and user experience at the code level (Table 4-Code). Among the actions,
graphic designers primarily engage in creation and manipulation-related
actions. With respect to purpose, their tacit knowledge is used to satisfy
external design constraints (e.g., a limited budget or time) while striving to
create visually appealing designs. Additionally, a significant portion of
tacit knowledge’s purpose is to consider the actual audience who will face the
design.

####  5.3.2. RQ1-2: Element, Where Tacit Knowledge is Revealed or Manifested

The coding results show that the categories of elements include those inner
design elements, design work pattern, contents, and outer design sources
(Table 4-Element’s Category). Regarding the characteristics of tacit
knowledge, the instances revealed through inner design elements require the
most intuition and simultaneously have characteristics of professional
knowledge (Figure 2-d).

On the other hand, the work pattern-related element’s instances are the least
intuitive and show the easiest characteristics in reasoning and communication.
“The ability to design various design proposals quickly and in large
quantities” mentioned by P7 is one of the instances revealed from the work
pattern. Regarding acquiring tacit knowledge, graphic designers mostly learn
while doing design work, regardless of the type of element (Figure 2-c). Tacit
knowledge instances appearing as inner design elements are somewhat
expressible through actions, but they show the most difficult characteristics
in terms of codification and communication (Figure 2-b). P1, P3, and P9 have
stated that fine adjustments of design elements can lead to a good design.
They also mentioned that while this can be discovered during the design
modification process, they often explain the rationale of modification to
others using abstract terms.

####  5.3.3. RQ1-3: Action, Designer’s Actions to Practice Tacit Knowledge

Graphic designers’ actions to use tacit knowledge can be largely categorized
into cognition, utilization, and manipulation aspects (Table 4-Action’s
Category). Cognition includes actions such as possess or feel. The utilization
contains tacit knowledge that can be performed through finding and adapting.
The manipulation category has tacit knowledge that can be represented through
actions such as creating or arranging.

From the perspective of characteristics, it is difficult to know that the
cognitive-related actions of tacit knowledge are owned compared to other
action categories (Figure 2-a). It also shows that it is the most difficult to
codify and communicate. Instances of tacit knowledge that can fall into
cognitive-related actions category include “Possessing knowledge about the
usage of easily readable fonts”(P10). In the interview, several designers
(P3-P5, P7, and P10) explained that making judgments or predictions about
certain design problems or situations is difficult to explain verbally and
appears differently among experts. They also mentioned that tacit knowledge
used by performing such actions highly depends on personal intuition. The most
dominant actions to use tacit knowledge are in the manipulation category Table
4. Specifically, creation-related action shows the characteristics of being
most influenced by personal intuition and being conventional (Figure 2-c and
d).

Relatively, the smallest actions are the utilization-related actions, which
include find and adapt (Table 4). Instances associated with these actions are
“Finding fonts that fit well with the style/mood” (P8) and “Appropriately
adapting basic design principles according to the design situation” (P7). In
Figure 2, utilization shows the characteristics of difficulty in communication
and reasoning.

####  5.3.4. RQ1-4: Purpose, Goal of Tacit Knowledge

Tacit knowledge in graphic design is used for five main purposes: Individual
design style, environment, visual, design content, and audience-related
purpose (Table 4-Purpose’s Category). Tacit knowledge is mostly used for
visual purposes, with the least associated with individual design style. Also,
audience- and environment-related purposes were shown as one of the main
purposes of tacit knowledge in graphic design.

The tacit knowledge instances with the most difficult codifying characteristic
are those performed for a visual-related purpose (Figure 2-b). The instances
aim to be aesthetically pleasing, like ‘‘Combining aesthetically pleasing
colors” (P2), or “Creating visually comfortable margins” (P6). P4 and P7 said
that a visually attractive graphic design is hard to explain, but a designer
with this knowledge reveals it through their editing or creating actions. The
most intuitive tacit knowledge instances are included here, and reasoning is
also rather hard (Figure 2-b and d).

Audience is a group that graphic designers always consider essential (P1-P10),
and tacit knowledge with a related purpose has the most conventional
characteristics and requires relatively high intuition (Figure 2). This
includes the instances of “Creating a layout that guides the audience’s
action” (P4). Tacit knowledge instances with an environment-related purpose
include instances of ”Possessing the knowledge of creating designs at a low
cost” (P5). The instances with this purpose show relatively high context-
specific characteristics, but it has the characteristic least influenced by
personal traits and intuition (Figure 2-c and d).

Instances with a purpose related to individual style are the least frequent
(Table 4). However, among all categories of tacit knowledge (Table 4),
instances are the most difficult to reason and communicate, and they can be
expressed through non-verbal channels (Figure 2-b). An example of an instance
including this category is ”Possessing a personal style preference” (P8). P3
and P8 commented that personal style or preference could make the design
communication difficult, but this is not tacit knowledge only for the
professional.

##  6\. Systematic Literature Review: Exploring Graphic Design Approaches

To investigate gaps between the investigated instances and the prior
approaches that support the graphic design process (e.g., design creation,
knowledge sharing, etc.), we conduct a systematic literature review (SLR) on
approaches, encompassing systems, techniques, and methodologies related to
graphic design. By annotating the elements, actions, and purposes of instances
supported explicitly by the approach, we investigate how existing approaches
address different types of tacit knowledge in graphic design. This SLR aims to
uncover what categories or codes of tacit knowledge instances remain
unsupported or under-supported and to analyze the reasons through the
characteristics of tacit knowledge in section 4.1, answering these research
questions:

  * •

RQ2: How do prior approaches in graphic design support the usage of tacit
knowledge, and what parts lack support?

    * –

RQ2-1: Which elements of tacit knowledge get adequate or insufficient coverage
by prior approaches?

    * –

RQ2-2: Which actions of tacit knowledge get adequate or insufficient coverage
by prior approaches?

    * –

RQ2-3: Which purposes of tacit knowledge get adequate or insufficient coverage
by prior approaches?

###  6.1. Sampling and Annotation

####  6.1.1. Search Keyword and Exclusion Criteria

We first searched for literature with broader keywords in the ACM Digital
Library to identify existing approaches without constraint for publication
year. The keywords were “design communication”, “design knowledge”, “design
expression”, “design rationale”, and “graphic design”. Then, we narrowed our
search to papers suggesting artifact contributions (e.g., interfaces,
algorithms) or those presenting frameworks or methods, that could be
implemented as tools, to support graphic designers. Accordingly, empirical
studies (e.g., (Herring et al., 2009)) found in the sampling process were
excluded from the review.

With this exclusion criterion, we collected 108 papers using the keywords.
Then, we filtered these papers based on their relatedness to the graphic
design domain. For example, we included the papers if they clearly explain
their focus on the graphic design domain. However, we excluded papers that
focused on domains unrelated to graphic design, such as architecture, fine
art, or painting. We filtered out 65 papers and arrived at a final set of 43
papers. To explore diverse approaches, we performed an exploratory search of
papers (19 papers) meeting the criteria but not identified in the initial
search, as well as papers citing the previously discovered papers.
Consequently, we examined a total of 62 papers.

####  6.1.2. Annotation Process

Two authors conducted the annotation process. An approach was considered to
cover a knowledge instance if it explicitly detailed the specific elements,
actions, and purpose of instances in their approach. Both authors primarily
reviewed the introduction and methodology sections of a sampled paper,
discussing and annotating the covered elements, actions, and purpose (Table
4). In the case of conflicts during the annotation, the authors engaged in
iterative discussions, establishing rules for conflicting annotations to
prevent further conflicts in subsequent annotations. Discussions continued
until the authors reached a consensus and completed the entire annotation
process.

The annotation process is illustrated with the following example: In the
methodology section, if the approach mentions element codes and indicates
system support for manipulation within those elements, the paper is marked as
addressing them. As an example, Vinci (Guo et al., 2021), a design system
supporting the process of creating advertisement posters, takes reference
images and the desired concept of the poster to adjust elements such as color,
font, image, and layout, ultimately providing a completed poster. In this
case, we annotated Vinci’s handled elements as color, font, image, layout, and
reference. The annotation was also performed for the actions explicitly
mentioned in the paper. Returning to the example of Vinci, this approach
automatically generates designs on behalf of the designer, and the designer
also arranges and adjusts the generated design. We annotated Vinci’s supported
actions as create, arrange, and adjust. Regarding the purpose of an approach,
we found their aims primarily in the introduction section. Vinci aims to
create visually appealing and readable posters within the given design
requirements, so we annotated the purpose as aesthetically pleasing, enhancing
readability, and satisfying design requirements codes in Table 4.

###  6.2. Analysis Method

To analyze the annotation result, the total amount of O marks was counted in
each element, action, and purpose code level (Table 4-Code). We divided this
by the total number of reviewed papers to calculate the percentage of coverage
within the sampled papers in each code level. Then, we averaged each code’s
coverage value in a category to get the average coverage values in each
category (Approach avg ratio in Table 5). We compared this ratio with Table
4’s category ratio to see which types of categories are relatively less
covered from prior approaches. This method reveals the differences between the
distribution of tacit knowledge instances and those covered by the approaches.
Additionally, drawing from the findings in Sections 5.3.2 to 5.3.4, we examine
the reasons behind the limited coverage of specific instances by describing
the characteristics of categories.

###  6.3. Findings

Table 5. Systematic literature review result. This table shows the SLR
results. We annotated 62 prior approaches for graphic design to the code of
Table 4 and calculated the ratio of coverage in each code. This table shows
the average ratio of each code at the category level. The relatively
lowest/highest coverage ratio of the category (in each coding scheme) is
highlighted in  red/ green. The rightest side shows the annotated approaches.

Table 4: Table 4 shows the annotation results of our systematic literature
review. The qualitative coding results are namely as shown in Table 3 same.
The right side columns show the prior approaches that were annotated to this
code with the average ratio of approaches in the category level. The highest
number of categories is colored green, and the lowest number of categories is
colored red.  Coding scheme Category Code Approach avg ratio (%) Approaches
Element Inner design element Color 21.66% (Chen et al., 2021; Wang et al.,
2020; Guo et al., 2021; Newman and Landay, 2000; Xie et al., 2021; Chilton et
al., 2019; Zhao et al., 2021, 2018; Yang, 2020; Chilton et al., 2021; Kim et
al., 2022; Tanner et al., 2019; Kumar et al., 2011; Ritchie et al., 2011) Font
(Guo et al., 2021; Lewien, 2021a; Newman and Landay, 2000; Ross et al., 2020;
Xie et al., 2021; Chilton et al., 2019; Zhao et al., 2018; Yang, 2020; Choi et
al., 2018; Lopes et al., 2020; Kim et al., 2022; Tanner et al., 2019; Kumar et
al., 2011; Ritchie et al., 2011) Image (Wang et al., 2020; Guo et al., 2021;
Ross et al., 2020; Xie et al., 2021; Chilton et al., 2019; Zhao et al., 2021,
2018; Yang, 2020; Chilton et al., 2021; Kumar et al., 2011; Ritchie et al.,
2011) Layout (Chen et al., 2021; Zheng et al., 2019; Wang et al., 2020; Roth
et al., 1994; Guo et al., 2021; Lewien, 2021a; Maudet et al., 2017; Kim and
Foley, 1993; Xie et al., 2021; Ueno and Satoh, 2021; Kikuchi et al., 2021;
Yang, 2020; Mackinlay, 1986; O’Donovan et al., 2015; Liu et al., 2018; Dayama
et al., 2020; Zhao et al., 2020; Swearngin et al., 2020; Kumar et al., 2011;
Ritchie et al., 2011) Size/ratio (Chen et al., 2021; Zheng et al., 2019; Wang
et al., 2020; Guo et al., 2021; Lewien, 2021a; Maudet et al., 2017; Kim and
Foley, 1993; Ross et al., 2020; Xie et al., 2021; Ueno and Satoh, 2021; Zhao
et al., 2018; Kikuchi et al., 2021; Yang, 2020; Chilton et al., 2021;
Mackinlay, 1986; O’Donovan et al., 2015; Liu et al., 2018; Dayama et al.,
2020; Kim et al., 2022; Swearngin et al., 2020; Tanner et al., 2019; Kumar et
al., 2011; Ritchie et al., 2011) Space (Zheng et al., 2019; Guo et al., 2021;
Kim and Foley, 1993; Xie et al., 2021; Ueno and Satoh, 2021; Kim et al., 2022;
Swearngin et al., 2020; Tanner et al., 2019; Kumar et al., 2011) User
experience (Newman and Landay, 2000; Kusano et al., 2013; Tan et al., 2012)
Design work pattern Design tool 44.35% (Chen et al., 2021; Haruno et al.,
2016; Milota, 2004; Roth et al., 1994; Lewien, 2021b; Klemmer et al., 2002;
Newman and Landay, 2000; Kim and Foley, 1993; O’Leary et al., 2018; Chen et
al., 2019; Kim et al., 2022; Swearngin et al., 2020; Fogarty et al., 2008;
Tanner et al., 2019; Lee et al., 2020) Work style (Li et al., 2012; Chen et
al., 2021; Wang et al., 2020; Roth et al., 1994; Guo et al., 2021; Klemmer et
al., 2002; Maudet et al., 2017; Newman and Landay, 2000; Kim and Foley, 1993;
Soria and van der Hoek, 2019; Gutierrez Lopez et al., 2018; Chilton et al.,
2019; Ueno and Satoh, 2021; O’Leary et al., 2018; Rasmussen et al., 2019;
Mackinlay, 1986; Date and Kakehi, 2018; O’Donovan et al., 2015; Kang et al.,
2021; Liu et al., 2018; Löwgren and Nordqvist, 1992; Lekschas et al., 2021;
Tan et al., 2012; Suleri et al., 2019; Yen et al., 2020; Ngoon et al., 2021;
Dayama et al., 2020; Kang et al., 2018; Swearngin et al., 2020; Fogarty et
al., 2008; Foong et al., 2021; Lee et al., 2020; Huang et al., 2019;
Zilouchian Moghaddam et al., 2015; Kim et al., 2021; Koch et al., 2020; Bunian
et al., 2021; Mozaffari et al., 2022; Kumar et al., 2011; Ritchie et al.,
2011) Contents Information hierarchy 2.42% (Lewien, 2021a; Xie et al., 2021;
Kumar et al., 2011) Writing [] Outer design source Design theory 25.00%
(Maudet et al., 2017; Kim and Foley, 1993; Löwgren and Nordqvist, 1992; Suleri
et al., 2019; Kang et al., 2018) Reference (Li et al., 2012; Wang et al.,
2020; Guo et al., 2021; Maudet et al., 2017; Ross et al., 2020; Gutierrez
Lopez et al., 2018; Xie et al., 2021; Chilton et al., 2019; Ueno and Satoh,
2021; Lin and Martelaro, 2021; Chen et al., 2019; Yang, 2020; Mackinlay, 1986;
Kang et al., 2021; Suleri et al., 2019; Zhao et al., 2020; Kang et al., 2018;
Kim et al., 2022; Fogarty et al., 2008; Lee et al., 2020; Huang et al., 2019;
Koch et al., 2020; Bunian et al., 2021; Mozaffari et al., 2022; Kumar et al.,
2011; Ritchie et al., 2011) Overall element Overall N/A N/A Action Cognition
Possess 13.71% (Lewien, 2021b; Klemmer et al., 2002; Soria and van der Hoek,
2019; Gutierrez Lopez et al., 2018; O’Leary et al., 2018; Rasmussen et al.,
2019; Tan et al., 2012; Kang et al., 2018; Foong et al., 2021; Lee et al.,
2020; Zilouchian Moghaddam et al., 2015; Ritchie et al., 2011) Feel (Kusano et
al., 2013) Predict (Chen et al., 2021; Newman and Landay, 2000; Zhao et al.,
2018; Date and Kakehi, 2018; Bylinskii et al., 2017; Lekschas et al., 2021;
Tan et al., 2012; Yen et al., 2020; Kang et al., 2018; Foong et al., 2021; Lee
et al., 2020) Judge (O’Leary et al., 2018; Date and Kakehi, 2018; Löwgren and
Nordqvist, 1992; Lekschas et al., 2021; Yen et al., 2020; Kang et al., 2018;
Foong et al., 2021; Lee et al., 2020; Zilouchian Moghaddam et al., 2015; Kim
et al., 2021) Utilization Find 31.45% (Klemmer et al., 2002; Kim and Foley,
1993; Soria and van der Hoek, 2019; Ross et al., 2020; Gutierrez Lopez et al.,
2018; Xie et al., 2021; Rasmussen et al., 2019; Chen et al., 2019; Kang et
al., 2021; Choi et al., 2018; Tan et al., 2012; Suleri et al., 2019; Yen et
al., 2020; Ngoon et al., 2021; Kang et al., 2018; Kim et al., 2022; Fogarty et
al., 2008; Tanner et al., 2019; Lee et al., 2020; Huang et al., 2019;
Zilouchian Moghaddam et al., 2015; Koch et al., 2020; Bunian et al., 2021;
Mozaffari et al., 2022; Ritchie et al., 2011) Adapt (Li et al., 2012; Chen et
al., 2021; Zheng et al., 2019; Wang et al., 2020; Roth et al., 1994; Maudet et
al., 2017; Kim and Foley, 1993; Chilton et al., 2019; Yang, 2020; Ngoon et
al., 2021; Zhao et al., 2020; Kang et al., 2018; Tanner et al., 2019; Kumar et
al., 2011) Manipulation Create 23.23% (Haruno et al., 2016; Zheng et al.,
2019; Wang et al., 2020; Guo et al., 2021; Lewien, 2021a; Maudet et al., 2017;
Kim and Foley, 1993; Ross et al., 2020; Xie et al., 2021; Chilton et al.,
2019; Ueno and Satoh, 2021; Kikuchi et al., 2021; Yang, 2020; Kusano et al.,
2013; Chilton et al., 2021; Mackinlay, 1986; Liu et al., 2018; Lopes et al.,
2020; Dayama et al., 2020; Swearngin et al., 2020; Mozaffari et al., 2022)
Arrange (Zheng et al., 2019; Guo et al., 2021; Lewien, 2021a; Kim and Foley,
1993; Chilton et al., 2019; Kikuchi et al., 2021; Yang, 2020; Mackinlay, 1986;
Liu et al., 2018; Dayama et al., 2020; Swearngin et al., 2020) Adjust (Li et
al., 2012; Chen et al., 2021; Zheng et al., 2019; Wang et al., 2020; Guo et
al., 2021; Lewien, 2021a; Maudet et al., 2017; Newman and Landay, 2000; Kim
and Foley, 1993; Ross et al., 2020; Chilton et al., 2019; Ueno and Satoh,
2021; Zhao et al., 2021, 2018; Yang, 2020; Kusano et al., 2013; Chilton et
al., 2021; Mackinlay, 1986; O’Donovan et al., 2015; Liu et al., 2018; Choi et
al., 2018; Dayama et al., 2020; Kim et al., 2022; Swearngin et al., 2020;
Tanner et al., 2019; Kumar et al., 2011) Combine (Wang et al., 2020; Ross et
al., 2020; Chilton et al., 2019; Yang, 2020; Chilton et al., 2021; Zhao et
al., 2020; Kumar et al., 2011) Define (Maudet et al., 2017; Kim and Foley,
1993; Lin and Martelaro, 2021; O’Donovan et al., 2015; Tan et al., 2012;
Fogarty et al., 2008; Koch et al., 2020) purpose Environment Outer constraint
29.03% (Li et al., 2012; Haruno et al., 2016; Wang et al., 2020; Roth et al.,
1994; Guo et al., 2021; Maudet et al., 2017; Newman and Landay, 2000;
Gutierrez Lopez et al., 2018; Chilton et al., 2019; Ueno and Satoh, 2021;
O’Leary et al., 2018; Rasmussen et al., 2019; Kikuchi et al., 2021; Chen et
al., 2019; Yang, 2020; Kusano et al., 2013; Chilton et al., 2021; Mackinlay,
1986; Date and Kakehi, 2018; O’Donovan et al., 2015; Kang et al., 2021; Liu et
al., 2018; Ngoon et al., 2021; Dayama et al., 2020; Zhao et al., 2020; Kim et
al., 2022; Swearngin et al., 2020; Tanner et al., 2019; Huang et al., 2019;
Zilouchian Moghaddam et al., 2015; Kim et al., 2021; Koch et al., 2020; Bunian
et al., 2021; Kumar et al., 2011; Ritchie et al., 2011) Design requirement
(Chen et al., 2021; Zheng et al., 2019; Wang et al., 2020; Roth et al., 1994;
Guo et al., 2021; O’Leary et al., 2018; Kikuchi et al., 2021; Chen et al.,
2019; O’Donovan et al., 2015; Choi et al., 2018; Lopes et al., 2020; Löwgren
and Nordqvist, 1992; Tan et al., 2012; Dayama et al., 2020; Swearngin et al.,
2020; Zilouchian Moghaddam et al., 2015; Kumar et al., 2011; Ritchie et al.,
2011) In line with trends (Lee et al., 2020) Audience Favored by the public
8.71% (Lekschas et al., 2021; Yen et al., 2020; Kang et al., 2018; Foong et
al., 2021; Lee et al., 2020) User-friendly (Kusano et al., 2013; Löwgren and
Nordqvist, 1992; Lekschas et al., 2021; Suleri et al., 2019; Yen et al., 2020;
Kang et al., 2018) Reducing errors (Guo et al., 2021; Löwgren and Nordqvist,
1992; Lekschas et al., 2021; Yen et al., 2020; Kang et al., 2018) Considering
the target audience (Zheng et al., 2019; Maudet et al., 2017; O’Leary et al.,
2018; Zhao et al., 2018; Bylinskii et al., 2017; Suleri et al., 2019; Yen et
al., 2020; Foong et al., 2021; Lee et al., 2020) Visual Guiding the audience
11.29% (Bylinskii et al., 2017; Lee et al., 2020) Enhancing readability (Zheng
et al., 2019; Guo et al., 2021; Lewien, 2021a; Ross et al., 2020) Balanced
(Zheng et al., 2019; Lewien, 2021a; Kim and Foley, 1993; Dayama et al., 2020;
Lee et al., 2020) Unexpected (O’Donovan et al., 2015; Kang et al., 2021; Lopes
et al., 2020; Dayama et al., 2020; Swearngin et al., 2020; Mozaffari et al.,
2022) Harmonious (Wang et al., 2020; Guo et al., 2021; Lewien, 2021a; Zhao et
al., 2021; Yang, 2020; Choi et al., 2018; Lee et al., 2020) Individual design
style Preference 8.06% (Guo et al., 2021; Kim and Foley, 1993; Lin and
Martelaro, 2021; Kikuchi et al., 2021; Kim et al., 2022) Design contents
Considering the importance of information 14.52% (Zheng et al., 2019; Guo et
al., 2021; Lewien, 2021a; Newman and Landay, 2000) In line with the design
theme (Zheng et al., 2019; Wang et al., 2020; Guo et al., 2021; Ross et al.,
2020; Ueno and Satoh, 2021; Zhao et al., 2018; Chen et al., 2019; Kang et al.,
2021; Suleri et al., 2019; Ngoon et al., 2021; Zhao et al., 2020; Fogarty et
al., 2008; Tanner et al., 2019; Koch et al., 2020) Empty Empty N/A N/A

####  6.3.1. RQ2-1: Which Elements of Tacit Knowledge Receive Adequate or
Insufficient Coverage?

Among the elements, the one least addressed by approaches is related to design
content, while the most is associated with work patterns (Table 5).
Specifically, design content category shows low occupies in both of tacit
knowledge instances (Table 4-Element’s Contents) and coverage within existing
approaches (Table 5-Element’s Contents). In contrast, the inner design
element, which reveals a significant amount of tacit knowledge instances
(Table 4-Element’s Inner design elements), demonstrates relatively low
coverage (Table 5-Element’s Inner design elements). Among the reviewed papers,
only an average of 22% of approaches address inner design elements such as
font, color, and layout. Notably, instances that can be expressed through user
experience exhibit a mere 5% coverage. Tacit knowledge revealed through inner
design element is characterized by its close connection to intuition (Figure
2-d), and among the four elements, it represents the most challenging tacit
knowledge to communicate (Figure 2-b). In this regard, P3 and P5 mentioned
that while knowledge related to inner design elements can be adequately
demonstrated through a designer’s actions, organizing and reusing such
knowledge might be difficult for those who do not possess it firsthand.

####  6.3.2. RQ2-2: Which Actions of Tacit Knowledge Receive Adequate or
Insufficient Coverage?

In prior approaches, the well-supported action is linked to utilization, while
the least-supported action pertains to cognition (Table 5-Action’s Utilization
and Cognition). The cognition category includes actions such as possess, feel,
predict, and judge. Tacit knowledge that can be performed through these
actions accounts for 33% of the explored instances (Table 4-Action’s
Cognition), yet it exhibits the lowest coverage within the approaches (Table
5-Action’s Cognition). Particularly, the feel action is only supported by one
of the surveyed papers. This means that tacit knowledge instances such as
“Feeling good inspiration from unrelated sources” (P1), related to
experiencing something in a sense, are not sufficiently supported yet. P1
further explained that sensing design issues, design inspiration, or
possessing design sensibility is difficult to acquire. Communicating such
knowledge with others is even more challenging. In terms of characteristics,
this action showcases the most challenging aspect in terms of expression
(Figure 2-b). In contrast, the utilization action is highly supported by prior
approaches (Table 5-Action’s Utilization). Notably, these approaches usually
provide support for finding design references (Huang et al., 2019) or adapting
external resources (Lee et al., 2020).

Tacit knowledge that can be utilized in manipulation-related actions exhibits
a high coverage in prior approaches (Table 5-Action’s Manipulation).
Specifically, this action occupies a significant portion of the instances
(Table 4-Action’s Manipulation). Actions like create, adjust, and combine are
encompassed, and many approaches use rule-based or generative methods to
manipulate designs directly. Instances applicable to these actions are
relatively easy to express and showcase a conventional characteristic (Figure
2-b and d). Designers (P2, P4, and P7-P10) explained that the knowledge to
create better designs through manipulation actions is relatively easier to
obtain since it can be explained to others relatively easily, and somewhat
established methods are available.

####  6.3.3. RQ2-3: What Purpose of Tacit Knowledge Receive Adequate or
Insufficient Coverage?

Instances have an amount of visual\- and audience-related purposes (Table
4-Purpose’s Visual and Audience), while prior approaches predominantly support
environment-related purposes (Table 5-Purpose’s Environment). Despite a
significant focus on goals for design requirements or external constraints, in
line with trends within the environment had notably low coverage (Table
5-Purpose’s Environment). P5 emphasized that aligning with rapidly changing
graphic design trends is a crucial but challenging aspect of tacit knowledge
in graphic design.

On the other hand, audience-related and individual design style-related
purposes exhibit the lowest coverage within the approaches (Table 5-Purpose’s
Audience and Individual design style). P1 and P2 explained that understanding
the public’s choices or anticipating audience behavior can be acquired through
only previous design experiences. Furthermore, they also stated such knowledge
is challenging to perceive even with related data, and individuals with an
innate sense of this aspect exist. From a characteristic perspective (Figure
2-d), instances linked to audience-related purpose often demand substantial
personal intuition and are simultaneously possessed by professionals.
Instances having a purpose of individual design styles are relatively scarce
in the graphic design domain and existing approaches (Table 4\- and Table
5-Individual design style). Although instances that reflect personal
preferences and styles exist, expressing this knowledge itself is exceedingly
difficult (Figure 2-b), hence its limited coverage within approaches.

##  7\. Design Guidelines for Capturing and Applying Tacit Design Knowledge

In this section, we propose two sets of design guidelines: 1) to capture tacit
knowledge from experienced designers, and 2) to support those that lack the
knowledge in applying it in their own design processes. Specifically, we aim
to provide guidelines aligned with contemporary graphic design practices,
predominantly conducted on web and app-based design tools or systems. The main
findings from our work can be summarized as three-fold:

  * •

Graphic designers primarily use tacit knowledge by doing manipulation- and
cognition-related actions on inner design elements with the purposes of
creating visually pleasing artifacts and satisfying the potential audience
(Table 4)

  * •

Graphic design tacit knowledge is acquired through experience and practice
(learning-by-doing) and is influenced by personal traits, intuition, and
context (Table 4-c and d). However, it can be adequately reasoned about,
communicated, and expressed through non-verbal channels like design actions
(Table 4-b).

  * •

Prior approaches have shown low coverage for the elements (contents/inner-
design elements), actions (cognition/manipulation), and purposes
(visual/audience) of tacit knowledge (Table 5) because this knowledge is more
associated with personal intuition and is more specific to the context.

Based on these findings, we aim to answer the following research questions:

  * •

RQ3. What are the design guidelines for graphic design tools to share,
communicate, and use tacit knowledge?

    * –

RQ3-1. What are the design guidelines for capturing the tacit knowledge from
the designer who possesses the tacit knowledge?

    * –

RQ3-2. What are the design guidelines for supporting designers without tacit
knowledge to apply others’ tacit knowledge in their design process?

###  7.1. RQ3-1: Guidelines for Capturing Tacit Knowledge of Graphic Design

The tacit knowledge of graphic designers is primarily revealed through design
elements and actions in the design process (P1-P3, P5, and P7-P10), such as
creating, arranging, adjusting, combining, or defining (Table 4-Action). We
have confirmed that graphic design knowledge is manifested through inner
design elements, outer design sources, and design work patterns (Table
4-Element ). Also, tacit knowledge related to visual purposes is relatively
difficult to reason about (Table 4-Purpose and Figure 2-b). Related to this,
participants (P1, P3, and P8) mentioned that designers often intuitively
modify designs based on high-level goals, making it difficult to reason about
their intentions at the time.

However, prior work (Yen et al., 2017) and P3 emphasized how reasoning is
possible through reflection. Specifically, P3 stated that reflecting on the
process along with the final result makes it possible to reason about their
decisions after the fact. Furthermore, we found that tacit knowledge can be
sufficiently explained through various elements (e.g., fine-grained design
editing action, references, prototypes, or design process) beyond only verbal
channels (Figure 2-b). Based on these findings, we propose the following
system design guidelines for capturing tacit knowledge from professional
designers who possess it.

DG1: Action and Element Tracking

  * •

Capture: Continuously record the designer’s low-level actions (e.g.,
adding/modifying elements, adjusting properties, undo/redo) and corresponding
design elements (e.g., shapes, images, text) within and beyond the design
tool. This includes actions related to importing/adjusting external resources.

  * •

Data Integration: Seamlessly integrate tracking across the design tool and
external activities to create a unified action and element sequence.

DG2: Pattern Detection

  * •

Uncover Hidden Patterns: Analyze the collected action data to identify
recurring patterns in actions or command sequences, revealing the designer’s
implicit design process.

  * •

Focus on Sequences: Identify and analyze frequent action sequences as they are
likely to indicate significant design decisions or strategies.

DG3: Intent Interpretation

  * •

Metadata Leveraging: Utilize relevant metadata associated with the designer’s
workflow to interpret the intent behind the identified patterns.

  * •

Contextual Understanding: Consider the design context (e.g., project goals,
target audience, design brief) during interpretations of patterns to gain
deeper insights into the designer’s thinking process.

DG4: Intent Delivery

  * •

Real-time Assistance: When similar action patterns are detected in a
designer’s workflow, provide the designer with the interpreted intent along
with the associated elements.

  * •

Adaptive Delivery: Tailor the delivery of intent information based on the
designer’s preferences and current context.

DG5: Intent Refinement

  * •

Prompt for Annotations: Prompt the designer to annotate their own
interpretations or actual intents on automatically detected action patterns or
intents.

  * •

Multiple Channels: Allow diverse channels for annotating the intent, including
text, visuals, and elements in Table 4, to cater to various aspects of tacit
knowledge.

DG6: Tacit Knowledge Storage

  * •

Structured Representation: Store captured tacit knowledge instances as
structured data, which includes action patterns, corresponding elements, and
the final intent (based on both automatic interpretations and designer
annotations).

  * •

Categorization and Labeling: Organize and categorize the stored knowledge
based on design principles, project types, or other relevant criteria to
facilitate retrieval and reuse.

###  7.2. RQ3-2: Guidelines for Applying Tacit Knowledge in Graphic Design

Learning-by-doing is the most noticeable characteristic of tacit knowledge in
graphic design (Figure 2-c). Much tacit knowledge becomes evident when
designers perceive and manipulate inner design elements. Therefore, to acquire
this knowledge, it is essential to closely observe design patterns (i.g.,
editing or creating action) and reasons (i.g., intent or design rationale) for
using tacit knowledge within the actual design process. According to P5,
providing design feedback is a natural and effective form to support these
processes. Prior work (Kotturi and Kingston, 2019; Krishna Kumaran et al.,
2021; Yen et al., 2017) also explains that tacit knowledge can be conveyed
through design feedback. Based on this, we propose guidelines that specify the
format, timing, and content of feedback in the design process to help novice
designers acquire and apply tacit knowledge more concretely:

DG1: Predictive Design Assistance

  * •

Predicting the Designer’s Next Action: Employ accumulated tacit knowledge from
professional designers to predict the designer’s next action.

  * •

Explain Reasoning: Provide a rationale for suggested actions, drawing upon
relevant design principles and previously captured knowledge.

  * •

Offer Diverse Suggestions: Recommend actions beyond simple manipulation, such
as suggesting relevant design trends and references.

DG2: Design Knowledge Acquisition

  * •

Prompt for Intent Reflection: Ask novice designers to express their intent or
rationale behind specific design actions.

  * •

Showcase Alternative Perspectives: Present professional designers’ potential
actions and intents based on captured knowledge, fostering inspiration, and
acquiring professional knowledge.

DG3: Self-Evaluation and Critical Reflection

  * •

Prompt Design Evaluation: Encourage designers to critically assess their own
work-in-progress, considering its visual impact and potential response from
the audience.

  * •

Focus on Specific Aspects: Pose targeted questions to stimulate reflection on
key design elements like readability, contrast, and visual hierarchy.

DG4: Personalized Design Feedback

  * •

Analyze Current Design Elements: Leverage captured knowledge to offer
insightful feedback on the designer’s current design choices.

  * •

Identify Patterns and Trends: Analyze design work patterns to identify areas
for improvement and suggest alternative approaches.

  * •

Encourage Exploration and Experimentation: Motivate designers to explore
various design possibilities and generate diverse alternatives.

##  8\. Discussion

Our work probes the characteristics, instances, and prior approaches of tacit
knowledge in graphic design to propose design guidelines for capturing and
applying tacit knowledge. In this section, we discuss the implications of our
findings, limitations, and future work.

###  8.1. Personal Viewpoints Towards Tacitness: Understanding and Supporting

In the study with designers, we observed that perspectives on designers’ tacit
knowledge slightly differed across domains or individuals. For example, some
designers said color sense was innate, while others thought it was acquired
through practice. This implies that each designer could define the
characteristics of tacit knowledge differently and this further explains why
tacit knowledge is also called personal knowledge (Polanyi, 2012). In this
regard, the collection and annotation of tacit knowledge could be conducted at
a larger scale and diversity in terms of domain, experience, or work position.
This could reveal more general distributions regarding the characteristics of
the instances and the diversity of perspectives by domain or level of
experience. Since different characteristics would be assigned to the same
instances, this could allow for investigating diverse viewpoints toward tacit
knowledge. This direction of future work will lead to a more in-depth
understanding of tacit knowledge in graphic design, and reveal future
directions for the design of more detailed or personalized system support for
tacit knowledge.

###  8.2. Creativity from the Perspective of Tacit Knowledge

Instances related to design inspiration, like “The ability to draw good motifs
or inspiration from unexpected sources” (P6), revealed varied perspectives on
creativity-related tacit knowledge. Some designers (P1, P8) view it as
internalized and innate, while others (P2, P4, and P6) believe it naturally
develops through diverse projects. Instances aiming for unexpectedly creative
designs (Table 2-Purpose, Visual category) require high intuition and
expertise, described as tacit knowledge needing both experience and personal
intuition. All designers mentioned it is challenging to explain creativity,
but when asked about capturing and reusing creative knowledge, P4 and P6
affirmed its feasibility. They highlighted the importance of tracking
observations for creativity sources that lead to inspiration inside and
outside the design process. P6 suggested that although explaining real-time
creative thinking might be challenging, reflecting on observed elements and
personal experiences can facilitate the description. Therefore, despite
creativity-related knowledge’s high tacitness, we envision a future where
designers can share their design creativity as knowledge by capturing and
connecting encountered information.

###  8.3. Towards Over-the-Shoulder-Learning with Systematic Approaches

One of the major characteristics of tacit knowledge is that it is a
professional’s knowledge (Koskinen et al., 2003; Boiral, 2002; Leonard and
Sensiper, 2011). Although it depends on the instance, Figure 2 shows that a
high proportion of instances were considered to be characterized as
professional knowledge except for the instances of individual design style-
related purpose. The most common method for learning design knowledge is
through apprenticeship or design studios. While providing these learning
methods online could benefit large populations of remote novices, these
methods are not suitable for existing forms of online learning, such as video-
based learning. This is due to how design knowledge learning and tacit
knowledge acquisition necessitate the learner to learn by doing (i.e.,
designing), instead of only watching a teacher explain the process. In that
sense, our work could guide the design of future “over-the-shoulder learning”
(Twidale, 2005) support systems that follow the learners’ design process and
provide suggestions that help them learn about and follow tacit knowledge.

###  8.4. Generalization to Other Domains: Coding, Writing, and Research

In this work, we explore and investigate tacit knowledge in graphic design.
Our research method can be applied to other domains, such as coding, writing,
and research, but tacit knowledge will likely exist in different forms
depending on the field. In each domain, future work can identify specific
tacit knowledge instances and characteristics first. Further, future
researchers can follow our methodology of mapping instances to existing
approaches to explore what sort of knowledge is covered by prior work and to
reveal under-explored tacit knowledge. For example, in the research domain,
there might be tacit knowledge about how to search for references, the order
in which to read a paper, and scientific writing skills on how to compose
paragraphs during writing. By first revealing what instances of tacit
knowledge exist in the research process, future work could follow our research
process to then define guidelines for capturing and applying the identified
tacit knowledge, and, finally, design systems that employ these guidelines to
help early-stage researchers acquire these pieces of tacit knowledge.

###  8.5. Limitations and Future Work

Our work has several limitations. First, although we collected 123 instances
of tacit knowledge, it might not be representative enough because the number
of designers was ten. A larger number of participants could be recruited and,
thus, a larger number of instances could be collected in future work. Second,
the recruitment could be expanded for each domain to identify more
distinguished features of tacit knowledge in each domain. Third, more various
instances of tacit knowledge could be explored and identified in actual design
tasks. In our interview study, professional designers replied that the tacit
knowledge instance is deeply embedded in their design process. Fourth, the
unexplored grounding related to tacit knowledge can be further explored from
different viewpoints. The HCI field is highly interested in supporting
abstract and tacit concepts like Professional Vision (Beaudouin-Lafon et al.,
2023) and Creativity Support (Li et al., 2023) through a tool. In this sense,
future work can explore the perspective of what definitions and purposes tacit
knowledge is used within the tool. Lastly, if we can capture various instances
of tacit knowledge in graphic designers’ actual processes, we can adopt
further analysis schemes beyond element, action, and purpose. For example, to
analyze when tacit knowledge is employed, additional specific contextual
dimensions could be introduced, such as the stages of the design process where
tacit knowledge is manifested (e.g., design ideation) or the forms of design
tasks (e.g., design collaboration). In this regard, future studies could
uncover more diverse types of tacit knowledge by collecting data through in-
situ design tasks and adopting additional analysis schemes.

##  9\. Conclusion

This work investigates the tacit knowledge in graphic design by identifying
the instances, characteristics, and prior approaches. Through the interview
and annotation study with professional graphic designers, this study proposes
specific elements, actions, and purposes of tacit knowledge instances in
graphic design with their characteristics. Also, systematic literature review
and annotation process reveal the less covered elements, actions, and purposes
of tacit knowledge by prior approaches that support the graphic design
process. Finally, this study proposes design guidelines to capture and support
the application of tacit knowledge, considering the characteristics of the
instances in graphic design. Our work contributes to the future where tacit
knowledge could be actively shared by demystifying tacit knowledge in graphic
design.

###### Acknowledgements.

This work was supported by Institute of Information & Communications
Technology Planning & Evaluation (IITP) grant funded by the Korea government
(MSIT) (No.2021-0-01347,Video Interaction Technologies Using Object-Oriented
Video Modeling). This work was also supported by Institute of Information &
communications Technology Planning & Evaluation (IITP) grant funded by the
Korea government(MSIT) (No.2019-0-00075, Artificial Intelligence Graduate
School Program(KAIST)) We thank all of our participants for engaging
positively in our various studies. We also thank all of the members of KIXLAB
for their helpful discussions and constructive feedback.

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