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Synthesizing a Solution Space for Prescriptive Design Knowledge Codification

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One of Design Science Research's (DSR) principal purposes is to generate and codify design knowledge. Codification in DSR is done by providing clear chunks of prescriptive knowledge that guide the design of future solutions, including instructions on how to design (parts of) artifacts. Although various codification mechanisms have emerged over the last years, design principles are among the most prominent mechanisms. Yet, distinguishing between different codification mechanisms is often blurry, hindering designers from making informed decisions regarding appropriate mechanisms for their research aim and leveraging the full potential of the prescriptive knowledge. We seek to bridge the challenge of selecting from the fuzzy array of codification mechanisms by proposing an inductively generated solution space. We provide a taxonomy to organize essential elements of prescriptive knowledge based on an analysis of design-oriented literature in four meta-dimensions (i.e., communication, application, development, and justification). These meta-dimensions make transparent how codified prescriptive design knowledge works. Overall, the taxonomy guides designers in reflecting on and selecting from the set of suitable elements for their statements. Also, providing a synthesis of options for codifying prescriptive design knowledge will simplify the identification and advance the positioning of DSR contributions.
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Accepted Author Version Forthcoming Winter 2022
This is the accepted author version of Synthezising a Solution Space for
Prescriptive Design Knowledge Codification” by Frederik Möller, Magnus
Rotvit Perlt Hansen, and Thorsten Schoormann to appear in the 2022 Winter
issue of the Scandinavian Journal of Information Systems (SJIS).
The final published version will not be identical (e.g., visual design, pages)
Accepted Author Version Forthcoming Winter 2022
Synthesizing a Solution
Space for Prescriptive Design
Knowledge Codification
Frederik Möller
TU Dortmund University and Fraunhofer ISST
Magnus Rotvit Perlt Hansen
Roskilde University
Thorsten Schoormann
University of Hildesheim
Abstract. One of Design Science Research's (DSR) principal purposes is to
generate and codify design knowledge. Codification in DSR is done by
providing clear chunks of prescriptive knowledge that guide the design of future
solutions, including instructions on how to design (parts of) artifacts. Although
various codification mechanisms have emerged over the last years, design
principles are among the most prominent mechanisms. Yet, distinguishing
between different codification mechanisms is often blurry, hindering designers
from making informed decisions regarding appropriate mechanisms for their
research aim and leveraging the full potential of the prescriptive knowledge.
We seek to bridge the challenge of selecting from the fuzzy array of codification
mechanisms by proposing an inductively generated solution space. We
provide a taxonomy to organize essential elements of prescriptive knowledge
based on an analysis of design-oriented literature in four meta-dimensions (i.e.,
communication, application, development, and justification). These meta-
dimensions make transparent how codified prescriptive design knowledge
works. Overall, the taxonomy guides designers in reflecting on and selecting
from the set of suitable elements for their statements. Also, providing a
synthesis of options for codifying prescriptive design knowledge will simplify
the identification and advance the positioning of DSR contributions.
Accepted Author Version Forthcoming Winter 2022
Key words: Design Science, Design Knowledge, Prescriptive Statements, and
1 Introduction
Design Science Research (DSR) is fundamentally different from other sciences
as per its focus on artifacts (Baker 2008). Artifacts translate a set of
requirements from a problem state to a more satisfactory solution state that
fulfills these requirements (Purao et al. 2001; Simon 1996). The paradigmatic
difference refers to the mutandum, i.e., such objects of observation that change
their form over time, enabling the problem state to be transformed into a
preferable solution state (Järvinen 2007; Simon 1996; van Strien 1997). Design
science focuses on generating novel and purposeful solutions brought into
existence artificially, contrary to explaining natural phenomena (Gregor 2006).
Through these artifacts, designers aim to solve organizational problems with a
real-world impact (Romme 2003). The designer generates different design
knowledge types during building artifacts, usually prescriptive, which is one
of the most critical outcomes of design science (Denyer et al. 2008; Möller et
al. 2020; Seidel et al. 2017; van Aken 2004; van Aken 2005a). Significantly,
design knowledge differs from other types of knowledge per its inherent focus
on prescription rather than description (Gregor and Jones 2007; Romme 2003).
Only by accumulating and codifying prescriptive design knowledge a
successful design can transcend the boundaries of a single instance and be
reused by others (Chandra Kruse et al. 2019; McAdams 2003; Schoormann et
al. 2021). Codification is the process of condensing knowledge that enables
other designers to adopt such knowledge in different scenarios at different
times (Cohendet and Meyer-Krahmer 2001; Hall 2006; Nowack 1997).
Given the importance of prescriptive design knowledge, scholars have
proposed numerous mechanisms for codification (Gregor and Jones 2007),
including design principles (e.g., Chandra Kruse et al. 2015), technological
rules (e.g., Bunge 2012), design rules (e.g., Romme and Endenburg 2006), and
design propositions (e.g., Denyer et al. 2008). Although different termini are
used to describe these codifications, we see potential and a significant
contribution in identifying the mechanism’s actual differences and
underpinning assumptions, which need to be considered when formulating
design knowledge. For example, while design rules (Baldwin and Clark 2000)
are associated with the modularization of an artifact, technological rules are
typically specified sequentially (Bunge 2012), yet these properties are not
mandatory for other mechanisms. Moreover, in a recent study, Gregor et al.
(2020) refer to different codification mechanisms, including technological
rules and design guidelines, as “(…) range of view and nomenclature for
design principles.” To the best of our knowledge, little research investigates
the characteristic attributes of codification mechanisms in detail; a notable
exception is Hansen and Haj-Bolouri (2020).
Since the research stream on codification mechanisms is vast and
unstructured, leading to a high degree of blurriness in distinguishing each
mechanism's properties, we believe disclosing potential trade-offs will guide
Accepted Author Version Forthcoming Winter 2022
designers in selecting appropriate mechanisms and considering relevant
characteristics for their design knowledge. We see a promising potential for
creating a holistic view of prescriptive design knowledge codification in the
basic transferability and differences. Distinguishing between mechanisms and
highlighting central characteristics can ease the instantiation of an artifact (e.g.,
provide less room to misapply the prescriptions if it is indicated that users
should follow a sequence), leverage the full potential of such knowledge, and
make the building process more transparent to reviewers. Against this
backdrop, we follow a taxonomic approach to “structure or organize the body
of knowledge that constitutes a field” (Glass and Vessey 1995 p. 65). We draw
from the notion of a solution space, which we see as the overview of possible
options to codify prescriptive design knowledge (Purao et al. 2001; Simon
1995). Hence, we asked: What are the options to codify prescriptive design
knowledge based on their inherent characteristic elements?
To answer this, we structure prescriptive design knowledge characteristics
in the form of a taxonomy that first breaks down existing prescriptive
knowledge into its inherent characteristic elements. A taxonomy with its many-
faceted visualization options (Szopinski et al. 2020) is a powerful tool to
contrast objects of interest against each other. We choose to visualize the
taxonomy morphologically, as it gives intuitive insights into the dimensions
and characteristics of prescriptive design knowledge (i.e., their Gestalt, e.g.,
Ritchey 2014). Once developed, the taxonomy should represent elements of
codification mechanisms for prescriptive design knowledge. In doing so, we
aim at making the spectrum of prescriptive design knowledge codification
transparent and accessible. Our work synthesizes codification mechanisms that
share commonalities but often differ in terminology and origin. This is
important from a research point of view as it means that researchers should not
quarrel too much with finding a “correct” codification mechanism but instead
focus on which dimensions are needed to provide the most clarity for sharing
prescriptive knowledge. It is essential from a practitioner's viewpoint since
more informed and clearly communicated design knowledge can aid in finding
new ways to follow and instantiate the knowledge into usable solutions.
The paper is structured as follows. Following the introduction, Section 2
illustrates the theoretical background of prescriptions in design science.
Section 3 details the research design. Section 4 reports on the final taxonomy,
the primary outcome of the paper. In Section 5, we show the potential by
outlining the value of the solution space. Section 6 discusses our findings and
implications for design science as a research field. Lastly, Section 7 outlines
contributions, limitations, and avenues for further research.
2 The relevance of prescriptions for design
The design sciences produce actionable prescriptive knowledge that enables
their users to instantiate an artifact more efficiently (van Aken 2005a). Unlike
behavioral sciences, the design sciences bridge the gap between a problem
space that captures problems, needs, goals, and requirements and a solution
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space containing solutions to address the problems through artifacts (Maedche
et al. 2019; Purao et al. 2001; Simon 1996). In that process, the designer
generates design knowledge that must be stored and accumulated to advance
the knowledge base on artifact design (Vom Brocke et al. 2020). The “(…)
practical ethos (…)” of design science research implies a necessity to make its
products reusable in other instances that exceed the initial scenario of their
development (Iivari et al. 2018, p. 1). Codification is the mechanism used to
store, elevate, and make chunks of design knowledge reusable that emerge
during designing and its research (Hall 2006). Codification is the process of
accumulating knowledge and representing it in a format so that it can be reused
in additional instances, by other designers and at a different point in time
(Cohendet and Meyer-Krahmer 2001; Hall 2006). Examples of these formats
include prescriptive statements (Chandra Kruse et al. 2015), books (Davenport
and Prusak 1998), or design exemplars (van Aken 2005a). Codification
mechanisms enable designers to leverage the past experiences of other
designers and surmount errors that have already been made (McAdams 2003).
They allow transcending singularity and go beyond a “single success story”
(Chandra Kruse and Seidel 2017, p. 180). Rather than repeating problems made
in the prior projects or activities, codified, prescriptive design knowledge
“eases the burden of applying the problem-situational knowledge” (Nowack
1997, p. 51). Naturally, using design knowledge in other instances enhances
the probability of requiring fewer design iterations in subsequent design
projects, reducing cost and effort (Kim 2010). Effectiveness is especially
important, as prescriptive design knowledge often lags behind artifact design
processes and requires someone to ‘take the first step’ (Gurzick and Lutters
2005; Kim 2010).
Most types of prescriptive statements in design science are heuristics.
Rather than guaranteeing an outcome, heuristics give guidance to increase the
chance of succeeding in successful design (Fu et al. 2015). These outcomes
usually require grounding in some primary mechanism, explaining why and
how it should work (Romme and Endenburg 2006). For instance, prescriptive
design knowledge can be grounded in several input sources, such as a kernel
theory, natural law, or empirical evidence (Goldkuhl 2004; Romme 2003;
Romme and Endenburg 2006; van Aken 2004; Walls et al. 1992). The
codification mechanisms are usually targeted to enhance a designer's ability to
achieve a particular outcome yet require the designer to possess adequate
knowledge to implement them (Kim 2010; van Aken 2004; van Aken 2005a).
Usually, prescriptive design knowledge explains some form of causality,
implying that a particular outcome can be achieved if one follows a set of
specific steps. Goldkuhl (2004, p. 64) defines prescriptiveness as “[i]f act A
then Goal G (“ought”) where act A equals cause C and Goal G equals effect E
in the explanatory statement.” Table 1 gives an overview of the underlying
prescriptive logic of codification mechanisms that we consider in the article
(based on the list in Gregor et al. 2020).
Prescriptive logic
“if we want to achieve the aim A, and the
situation is of type B, then we should bring
about the cause X” (Niiniluoto 1993 p. 13)
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Design Law1
“Functional property A in situation B can be
achieved by imposing structural property X.”
(Kuipers 2013, p. 460)
“if you want to achieve Y in situation Z, then
perform action X” (van Aken 2001 p. 3)
“if you want to achieve outcome O in context
C, then use intervention type I”. (Denyer et
al. 2008 p. 395)
“if S(G,C) do A and achieve E(sG).”
Roozenburg and Eekels (1995) as cited in
(Nowack 1997, p. 45)
“If you want to design intervention X [for the
purpose/function Y in context Z], then you
are best advised to give that intervention the
characteristics A, B, and C [substantive
emphasis], and to do that via procedures K,
L, and M [procedural emphasis], because of
arguments P, Q, and R.” (van den Akker
1999, p. 9)
Design Rule
“to achieve A in situation S, do D” (Romme
2003, p. 566)
Table 1. Underlying prescriptive logic of different codification mechanisms (adapted
from and based on Gregor et al. 2020)
To stress the relevance of the selected codification mechanisms, we explored
their distribution in the IS disciplines. Therefore, we searched for ‘term of
codification’ and “information systems” in Google Scholar using Harzing
(2007)’s citation tool ‘Publish & Perish’ and analyzed the frequency of
occurrences since 2000, performed in 09/2021. We, of course, have to note that
while we cannot ensure that all of the papers produce knowledge on the
different mechanisms, they can indicate a degree of interest within the IS
community. Figure 1 shows that all of the mentioned mechanisms are
addressed and that Design principles are the most dominant ones, with 12.102
hits. This is followed by design guidelines (2.356 hits) and design rules (1.657
Kuipers (2013) explains that design laws cover what ‘ought’ to be, i.e., clear prescriptiveness
implicitly in comparison to Niiniluoto (1993)’s definitions of technical norms.
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Figure 1. Distribution of codification mechanisms in the literature
3 Research design
To codify the solution space for prescriptive codification mechanisms, we used
a taxonomy because it encloses the available options visually and intuitively
as well as enables the deconstruction of an object of analysis into designable
dimensions and characteristics (Nickerson et al. 2013). For collecting
information on codification mechanisms, we combine the taxonomy approach
with Webster and Watson (2002)’s notion of a concept-matrix. We screened a
sub-sample of papers for elements on prescriptive design knowledge
codification and jointly discussed each dimension and characteristic in the
team of authors (see Table 3). Because of the heterogeneity of the papers and
terminology, we south to scope the review to include predominantly seminal
or pivotal papers (Cooper 1988). Following our method, our literature review
can be positioned as narrative. We strive to give an overview of previous
codification mechanisms and highlight the issues that we have learned during
the process (Schryen et al. 2020).
Facing the heterogeneity of this field, we began with gathering potential
codification mechanism candidates from our experience individually and in
brainstorming sessions. We combined those from our experience with the
‘views of design principles’ by Gregor et al. (2020). From this, in line with this
study’s focus, we explicitly sought codification mechanisms that pointed to a
prescriptive logic and used this as the primary inclusion criterium (see Table
1). For example, we used Gregor et al. (2020)’s list as a starting point and
excluded those that did not follow a clear prescriptive logic (e.g., computing
principles). We also excluded technological knowledge since it is not a
codification mechanism per se but a concept that differentiates knowledge
produced in engineering disciplines against knowledge produced in other
disciplines (e.g., Houkes 2009). Next to the list of Gregor et al. (2020), we also
looked for additional mechanisms referenced in the literature corpus:
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Principle of form and function Principle of implementation Design principle Design guideline
Design proposition Design rule Technological rule Technical norm
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incorporating any paper that included further evidence of other codification
mechanisms in successively studied papers. Examples included Romme
(2003), who named design propositions and design rules (the latter was not
part of Gregor et al. 2020), as well as the exclusion of design laws due to their
normative nature that only implicitly refers to a prescriptive logic (Kuipers
We also filtered mechanisms alongside their conceptual position, which
means that we excluded preceding and subsequent concepts. For example,
design principles are often (but not always) located between (meta-)
requirements and features (e.g., see Meth et al. 2015; Wache et al. 2022). While
requirements are mostly used to describe a class of goals (e.g., Walls et al.
1992) and address “(…) the opportunity/problem to be addressed (…)”
(Hevner 2007, p. 89), design principles typically serve as the central concept
for the codification of prescriptive knowledge. Following this, we decided to
primarily consider the ‘design principle level.’ Three observations argued for
the usefulness of the above-mentioned reviewing and filtering strategy. First,
there seems to be no structured way to extract prescriptive design knowledge
codification mechanisms because the typical terms (e.g., principles, guidelines,
or rules) are considered as regular expressions and elements in the academic
literature that do not necessarily refer to design-oriented research. Second,
there are large differences between the cumulative body of literature on the
different mechanisms. Many publications develop design principles, but few
develop technical norms (see Figure 1). In this respect, the focus on
predominantly conceptual papers was necessary. Third, in some cases, it was
rather intuitive to identify seminal papers (e.g., the works of Bunge 1966;
Bunge 2012 for technological rules or Niiniluoto 1993 for technical norms). In
other cases, we relied on finding relevant papers either through cross-
references (e.g., Gregor et al. 2020; Romme and Endenburg 2006) or high-
ranking hits on Google scholar. Using an interdisciplinary search engine was,
in our case, the most sensible option since it has no restriction in terms of
domain or community and gave us a way to find adjacent terms and highly
relevant papers; see also Figure 1 and the list of Gregor et al. (2020).
After collecting a sample of papers, we examined how a particular
codification mechanism presented in a paper is described and specified. Table
3 lists the codification mechanisms that we considered in our literature review
and analysis with corresponding definitions. Our approach to analysis is
concept-centric (Webster and Watson 2002), in which we have examined
conceptual papers on codification mechanisms and inductively generated
codes. We did this threefold. First, we looked for properties that are
expressively mentioned in the literature (e.g., design principles addressing
form, function, and implementation and technological rules being sequential).
Second, we looked for information on properties that we can infer from context
or use other terminology in the original sources. In a third step, we used our
findings to synthesize the final taxonomy through a series of discussions and
activities (e.g., promoting, merging, or renaming dimensions, see Kundisch et
al. 2021), resulting in a taxonomy that was a product of numerous elaborations
among the team of authors.
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4 The solution space for prescriptive design
knowledge codification
In a nutshell, we collected a variety of codification mechanisms that are more
similar to each other than they are different. Naturally, all of the mechanisms
require the knowledge to be prescriptive, i.e., instructing users to achieve a
specific goal through some action. Furthermore, the codification mechanisms
used seem to aim to address a class of problems rather than an instance.
Addressing a class of problems is beneficial because it enables reuse in other
cases and the accumulation of design knowledge (Chandra Kruse et al. 2016).
In terms of users, these chunks of prescriptive design knowledge are intended
for use by professionals, such as designers, with the relevant expertise to
instantiate it. For example, how and if prescriptive design knowledge is
instantiated might strongly differ depending on the various levels of skill,
experience, and designer's environment (Chandra Kruse et al. 2016). Table 2
summarizes the main commonalities we see. In contrast, some elements either
pose blurry or clear differences. For example, technological rules are
distinctively part of a sequence that determines the order they are supposed to
be executed (Bunge 1966; Bunge 2012). Another example is that design rules
should be strictly followed, while other codification mechanisms are
recommendations (Baldwin and Clark 2000).
See Table 1.
Adress a class
of problem
Design propositions: “A design proposition can be seen as offering
a general template for the creation of solutions for a particular class
of field problems” (Denyer et al. 2008, p. 395)
Technological rule: “(…) means that it is not a specific prescription
for a specific situation, but a general prescription for a class of
problems.” (van Aken 2004, p. 228)
Design guideline: “It follows that the end user of the design
guidelines is the designer of the interface” (Kim 2010, p. 670)
Design propositions: “Typically demand much professional
knowledge and expertise (…)” (Denyer et al. 2008, p. 396)
Table 2. Commonalities of prescriptive design knowledge codification
From our analysis, we choose to construct the taxonomy of the solution space
in the form of a morphology. Using a morphology has the significant advantage
of being an intuitive tool for understanding and building objects based on
various design characteristics (Ritchey 2014; Szopinski et al. 2020). We
inductively derived four meta-dimensions, namely Communication,
Application, Development, and Justification, which serve as aggregated
theoretical lenses for the dimensions. The inductive process was done by
sorting each dimension to intuitively superordinate meta-dimensions and
attaching it with a label that subsumes them logically. In the following, we will
explain each dimension structured through the meta-dimensions. Table 3 lists
codification mechanisms considered in the study and the corresponding
sources that we have analyzed to construct the solution space.
Accepted Author Version Forthcoming Winter 2022
Principles of
Form and
“The abstract “blueprint” or
architecture that describes an IS
artifact, either product or
(Gregor and Jones 2007, p. 322)
(Gregor et al. 2013; Gregor
and Jones 2007; Markus et al.
Principles of
“A description of processes for
implementing the theory (either
product or method) in specific
contexts.” (Gregor and Jones
2007, p. 322)
(Gregor et al. 2013; Gregor
and Jones 2007; Markus et al.
“(…) a recommendation or
suggestion for a course of action
to help solve a design issue.”
(McAdams 2003, p. 357)
(Chandra Kruse et al. 2015;
Gregor and Jones 2007;
McAdams 2003; van den
Akker 1999)
“A design guideline is a
prescriptive recommendation for
a context sensitive course of
action to address a design issue.”
(Nowack 1997, p. 62)
(Greer et al. 2002; Gurzick
and Lutters 2005; Kim 2010;
Nowack 1997)
“Design propositions, as the core
of design knowledge, are similar
to knowledge claims in science-
based research, irrespective of
differences in epistemology and
notions of causality.” (Romme
2003, p. 567)
(Carlsson 2007; Denyer et al.
2008; Romme 2003; Romme
and Endenburg 2006; van
Aken et al. 2016)
Design Rules
“Design rules are elaborate
solution-oriented guidelines for
the design process (e.g., “if
condition C is present, to achieve
A, do B”). These rules serve as
the instrumental basis for design
work in any organizational
setting.” (Romme and Endenburg
2006, p. 288)
(Baldwin and Clark 2000;
Brusoni et al. 2006; Romme
2003; Romme and Endenburg
“(…) a chunk of general
knowledge, linking an
intervention or artefact with a
desired outcome or performance
in a certain field of application.”
(van Aken 2004, p. 228)
(Bunge 1966; Bunge 2012;
van Aken 2001; van Aken
2004; van Aken 2005a,
Technical Norm
“Technical norms are concerned
with the means to be used for the
sake of attaining a certain end.”
(Bulygin 1992, p. 212)
(Niiniluoto 1993, 2014;
Wright 1963)
Table 3. Overview of prescriptive codification mechanisms used in the study
We derived a taxonomy with 11 dimensions and several corresponding
characteristics based on selected mechanisms (see Figure 2). Below, we
explain the solution space alongside the four inductively generated meta-
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dimensions (MD): Communication that subsumes dimensions describing the
extent of the prescriptive design knowledge, e.g., who develops it, what
medium is used to codify it, or whether it addresses the artifact as a product or
the process of designing it. Application comprises dimensions describing the
prescriptive knowledge ‘looks like, e.g., whether it is modular, sequential, or
heuristic. Development that contains dimensions describing the method used
to codify the design knowledge, e.g., whether the approach reflects on a
finished design or synthesizes a priori data into design knowledge.
Justification entails dimensions for evaluation and grounding of the
mechanisms. Each dimension must at least have two characteristics to enable
decision-making (Nickerson et al. 2013). Usually, taxonomies should strive for
mutually exclusive dimensions (e.g., Bailey 1994). However, in terms of
usability and conciseness (see Nickerson et al. 2013), we refrained from adding
more characteristics that would subsume multiple other characteristics (e.g.,
through a characteristic called both or other). We did this to enhance
understandability and prevent potential patterns consisting of many
characteristics that subsume others. A notable exception is the dimension of
developers since the characteristic both here also indicates a notion of working
in a transdisciplinary team to codify prescriptive design knowledge.
Figure 2. Solution space of prescriptive design knowledge codification using two
examples of papers contributing with design guidelines and design principles,
We can show the applicability of the taxonomy in two illustrative cases. The
taxonomy results from conceptual papers, and not all dimensions are made
transparent in papers proposing prescriptive design knowledge. For the
purpose of a simple illustrative application scenario, we will assume that the
two following examples were developed by researchers and are
recommendations (see the patterns in Figure 2). First, Greer et al. (2002)
Form & Function
Follow Loosely
Follow Rigorously
Ex Ante
Ex Post
Formative Synthesis
Summative Extraction
Knowledge Base
(Greer et al. 2002)
(Design Guidelines)
(Hansen and Pries-Heje 2018)
(Design Principle)
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present design guidelines for product evolution modularized in four domains
(relative motion, graph structure, function, and analysis). Next to prescriptive
statements, the design guidelines are supported by visual illustrations. The
guidelines are extracted through observation of empirical examples (products).
Second, contrarily, the design principles in Hansen and Pries-Heje (2018) stem
from the analysis of two in-depth cases and are to be used in a sequence of both
principles of form and function and principles of implementation. Both
examples show typical features of prescriptive design knowledge that only
differ slightly. For instance, while the design principle case proposes five
condensed design principles (typical for these studies, e.g., Iivari et al. 2020),
the design guideline case proposes 29 guidelines. From the cases, we can
extract knowledge that prescriptive design knowledge codification would
benefit from finding similarities and differences and generating transparency
and clarity by elaborating on essential elements of the solution space in Figure
2. We will describe these issues in more detail in the following section.
4.1 Meta dimension 1: Communication
Communication (MD1) subsumes design parameters that logically outline the
prescriptive codification mechanisms they are supposed to look like and
achieve. It illustrates the format the prescriptive design knowledge is codified
in (Format) and who develops the prescriptive design knowledge (Developer).
Prescriptive design knowledge varies in how it is codified (Format),
ranging from graphical illustrations, short statements, longer text (e.g., books),
formulas, or exemplars. For example, while design principles are usually short
prescriptive statements or structures (e.g., see the formulation template of
Chandra Kruse et al. 2015 or structure of Gregor et al. 2020), van Aken (2005a,
p. 23) states that “the actual description of a rule may fill an article, a report or
even a whole book.” Greer et al. (2002) propose a set of design rules that
include prescriptive statements complemented through graphical aides and
In the examples given above, the Developers of the prescriptive design
knowledge are academics. However, multiple sources point to developers
being also practitioners (e.g., see Romme and Endenburg 2006) or both (van
Aken 2005a).
4.2 Meta dimension 2: Application
Application (MD2) includes all dimensions influencing the form and
application of the prescriptive knowledge.
Object addresses the notion of design as both a verb and a noun and
equivocally describes the design product and design process (Walls et al.
1992). The design product usually refers to principles that outline an artifact's
form and function. Complementarily, principles of implementation address the
process required to design the artifact.
The dimensions Sequence and Modularity binarily indicate whether the
codified set of design knowledge is supposed to be executed sequentially
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(coupled in a sequence) or whether they are to be seen as modular (decoupled
from one another) design system components, respectively.
The Application of the prescriptive design knowledge can either be a
recommendation that is to be followed loosely or a strictly to be followed
instruction (e.g., a medical recipe). Most codification mechanisms are
recommendations, yet, Baldwin and Clark (2000, p. 6) explain design rules to
be “(…) not just guidelines or recommendations: they must be rigorously
obeyed in all phases of design and production.
Guidance detailed how the prescriptive design knowledge is to be designed
regarding the ‘guaranteed’ effectiveness of its outcome. The dichotomy is
between heuristic and algorithmic prescriptions. van Aken (2004, p. 227)
differentiates heuristic and algorithmic technological rules by the example of
treating disorders as follows: Algorithmic technological rules are as follows
“in order to cure disorder Y, you follow a course of treatment consisting of
taking 0.3 milligrams of medicine X during 14 days.” In contrast, heuristic
technological rules “in order to cure disorder Y, you follow a course of
treatment consisting of rest, exercising and a fat-free diet.” The pivotal
difference is that heuristic prescriptions infer no guarantee for success, while
the effectiveness of algorithmic prescriptions (often formulated quantitatively)
can be proven (van Aken 2004; van Aken 2005a).
4.3 Meta dimension 3: Development
Development (MD3) includes all dimensions describing the activities
employed to develop the codified prescriptive design knowledge. First, the
general distinction (Process) is between ex post prescriptive statements that
come into existence reflectively through experience or ex ante design
knowledge, for instance, collected in multiple case studies before the design
process. In our sample, we obtained different terminology that refers to the
actual development of prescriptions: For example, design principles can be
derived reflectively or supportive (Möller et al. 2020). Technical norms are
developed by following the two approaches ‘from above’ (e.g., from
theoretical input) and ‘from below’ (e.g., from case-based input) (Niiniluoto
1993, p. 13), as well as technological rules which can be developed or extracted
(van Aken 2005a). We chose the terminology ‘ex ante’ and ‘ex post,’ since it
is the most generical formulation that is not associated with one specific
codification mechanism to differentiate ‘before designing artifact’ and ‘after
designing the artifact.’
Second, the actual Product can be generated through formative synthesis
or summative extraction. Practically, that happens in two ways. In terms of
formative development, through the synthesis of data from various sources,
such as theory, scientific literature, or qualitative studies (Möller et al. 2020;
van Aken 2004; van Aken 2005b). Alternatively, prescriptive design
knowledge can be developed after a project is completed by means of
extraction from finished cases or design projects (Gregor 2009; Möller et al.
2020; van Aken 2004). We used the terminology formative synthesis and
summative extraction since they convey the action taking place more precisely.
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4.4 Meta dimension 4: Justification
The meta-dimension Justification (MD4) subsumes dimensions of
argumentative mechanisms on why the design principles should work and how
they are evaluated. Concerning the grounding (Grounding), we draw from the
well-established dichotomy in DSR proposed by Hevner et al. (2004), namely
environment and knowledge base. Grounding “(…) services to develop a robust
understanding of how and why the design (rules) operates” (Romme and
Endenburg 2006, p. 289). In terms of the environment, there are different ways
to ground prescriptive design knowledge, including in the designer’s own
experience, experiments, or empirical evidence. For example, Kim (2010)
explains that design rules can be grounded in laboratory experiments or expert
opinions. Contrarily and complementarily, design knowledge should be
grounded in the existing knowledge base, which, from our analysis, comprises
scientific literature, natural laws, or kernel theories. We distinguish between
literature and kernel theories to demarcate a systematic literature review from
using kernel theories, such as the Resource-Based View (RBV) (Barney 1991).
For instance, van Aken (2004) points to technological rules being grounded in
natural laws.
In terms of evaluation (Evaluation), we distinguish between theoretical
saturation, meaning that prescriptive design knowledge is evaluated
progressively through accumulating empirical evidence, for example, in a
series of case studies (Eisenhardt 1989; van Aken 2005a). Contrarily,
prescriptive design knowledge might be provable, which usually is possible
when they are algorithmic (van Aken 2005a). Also, collecting supporting
evidence can support the validity of prescriptive design knowledge.
5 Discussion and reflection
Based on our analysis, we reflect on what can be learned for prescriptive design
knowledge codification and formulate two of them as a proposition for further
guidance in formulating prescriptive design knowledge. In detail, we see two
significant propositions that we discuss in detail below. First, identifying
paradigmatic differences between design knowledge codification (e.g.,
deciding on algorithmic or heuristic codification). Second, thinking in a
comprehensive solution space and entangling it with the particular design
context opens up the opportunity for formulating specific, well-fitting
prescriptions rather than using a pre-defined one. For example, consider the
artifact required to be built in a specific sequence. The prescription should
reflect that.
5.1 Highlighting paradigmatic differences in
Prescriptive design knowledge codification has many similarities across the
individual concepts we have analyzed. We can derive a ‘smallest common
denominator in the literature agreeing that codified prescriptive design
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knowledge as instructions for professional users to address a class of problems.
Necessarily, they also require to be prescriptively formulated to guide action,
i.e., formulate a clear causal chain to achieve some goal through a specific
action (Goldkuhl 2004) (see commonalities summarized in Table 2).
Contrarily, some characteristics let us distinguish different types of
codification. The characteristics in Table 4 are not, per se, different for each
mechanism, yet they show more variance than other characteristics.
Subsequently, we see them as paradigmatic because they are distinguishing
features shaping the underlying logic of the prescription. For example,
technological rules can both be algorithmic as well as heuristic (van Aken
2004). That is contrasted with the other codification mechanisms, which are
typically heuristics (e.g., see van den Akker 1999 or Kim 2010). In terms of
developing prescriptive design knowledge, most codification mechanisms
have a bottom-up empirical route and a top-down theoretical, deductive route
pointing to multiple grounding mechanisms (see Figure 2). Perhaps the most
significant unique selling points, at least de nomine, are the sequentiality
(Bunge 1966; Bunge 2012) of technological rules and the modularity of design
rules (Baldwin and Clark 2000). Sequentiality is to “(…) perform a finite
number of acts in a given order and with a given aim” (Bunge 2012, p. 338),
and modularity is “(…) based on the twinned principles of interface
standardization and components decoupling.” (Baldwin and Clark 2000, p.
179). While we did not find evidence in other types of prescriptive design
knowledge for the opposite, i.e., for non-sequentiality or non-modularity,
technological and design rules are the only two that have explicitly mentioned
these concepts (see Table 4).
Sequential (Bunge
Algorithmic or Heuristic (van
Aken 2005b)
Design Rules
(Brusoni et al.
Algorithmic or Heuristic (Greer
et al. 2002; Romme 2003;
Roozenburg and Eekels 1995)
Heuristic (Niiniluoto 1993)
Heuristic (van den Akker 1999)
Heuristic (Kim 2010; Nowack
Alogirthmic or Heuristic
(Carlsson 2007; Romme and
Endenburg 2006)
Table 4. Comparison of different paradigmatic properties of codification mechanisms.
*We did not find evidence for dimensions being non-sequential or non-modular. Yet,
these concepts are only explicitly mentioned in technological rules and design rules,
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5.2 Configuring prescriptive design knowledge
codification based on context
Reflecting on the solution space, we see no combinations of characteristics that
seem impossible to use. However, in terms of face validity, some combinations
might not work as well as others or make as much sense. Suppose that the
designer uses a formula to express the prescriptions yet also selects it to be
heuristic. Most likely, that would not make sense given that a formula usually
does not leave room for interpretation but normally is algorithmic and needs to
be followed rigorously. Given the span of forms a design project can take and
the options that we have identified, we propose combining the solution space
with the notion of design context (e.g., Herwix and zur Heiden 2021) to find
sensible combinations. We see this as necessary since designing something that
works in practice is fundamentally shaped by the requirements an artifact is
supposed to fulfill and the people that do it, i.e., the context in which it takes
place (Cross 1999; Purao et al. 2001). As a result, we see a need to make users
aware of the taxonomy that configuring their solution space fundamentally
reflects what they want to achieve for what purpose. Visualization might be a
preferable way to codify knowledge instead of written statements in some
cases. In other cases, it might be crucial to formulating prescriptive knowledge
algorithmically to minimize the degrees of freedom the user has to apply them
(e.g., in the case of medical recipes).
In the following, we will provide an illustrative example that underlines the
utility of our solution space in light of contexts in DSR. For that purpose, we
will draw from the example of designing a jug to illustrate how design
principles work based on Gregor et al. (2013). In the example, Gregor et al.
(2013) propose prescriptions for designing the form and function of a jug with
an array of prescriptions. Consider the options you would have to communicate
how to design a jug to a different person. Naturally, one could use textual
prescriptions, such as (Gregor et al. 2013, p. 7):
“1. Choose a shape that has the capacity to hold liquid.”
“2. Provide an opening through which liquid can be added.”
“3. Provide a feature that allows liquid to be poured (i.e., a spout), which can
be contiguous with the main opening, or not.”
“4. Provide a feature that allows it to be picked up by a human (i.e., a handle).”
“5. Ensure it is of a suitable size and weight when full to be lifted and
manipulated by a human.”
“6. Ensure that the jug can stay upright on a horizontal surface.”
“7. Place a handle opposite the spout in order to get maximum leverage.
“8. Ensure that the spout of the jug is above the highest point at which liquid
can be held in order to make maximum usage of capacity.”
Alternatively, other means of communicating prescriptions could be
sensible. In this case, our solution space proposes, for instance, visual aids. A
conceptual, visual representation might guide the designer in placing the
handle more concretely. However, there is room for interpretation and
communicating prescriptions even here. Figure 3 shows three easy examples
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of conceptualizations of jugs. Conceptualization I simply prescribes putting
the handle somewhere on the right side of the jug. The designer now has the
freedom to choose the exact spot, maybe depending on the shape of the jug and
the shape of the handle. Conceptualization II is one step more concrete,
prescribing the handle to be placed somewhere in the middle. Yet, given that
the prescription is a heuristic recommendation, ‘the middle’ might differ from
where the designer perceives to measure the height of the jug (e.g., including
the foundation, just using the body, and so on). Last, Conceptualization III
includes a formulaic prescription, communicating to designers that the handle
has to be placed exactly at L/2 with the visual aid indicating that L is the
complete height of the jug, including all elements. The simple example
outlined in Figure 3 illustratively shows the conundrum in formulating
prescriptions. They need to prescribe a course of action, yet, the spectrum of
how they are communicated leaves more or less expansive room for
interpretation. It also shows that there is more than one way to communicate
prescriptions and even combine them.
Figure 3. An illustration of different ways to communicate prescriptions
Since the options we illustrate in Figure 3 are reasonable in different situations
and scenarios, we use the concept of context in DSR (Herwix and zur Heiden
2021) to derive abstract learnings for each dimension. Table 5 summarizes the
illustrative scenario of designing a jug based on Gregor et al. (2013) and
derives abstracted context-sensitive learnings from it. We use the definition of
Herwix and zur Heiden (2021)
, who see context as “the environment which
surrounds an artifact or the source of the requirements that an artifact is to be
evaluated against.”
Illustrative scenario
Abstracted context-sensitive learning
Suppose the jug requires a
concrete visual design. Using
visual aids or existing models
might be more helpful than
textual prescription.
Subsequently, using additional
communication mechanisms
could benefit in designing a jug.
Depending on the context of the
artifact-to-be-designed, it might be
more sensible and practical to use
specific means of communicating
prescriptions above others. For
example, providing visual aides or
formulas might be more beneficial than
giving highly interpretable short text.
The definition in Herwix and zur Heiden(2021) is based on the understanding of context in
DSR outlined in Hevner et al. (2004) and Maedche et al.(2019).
Place the handle
somewhere here Place the handle
in the middle
Place the
exactly at
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In the context of designing a jug,
having developers that are
practitioners and have design
experience is different than
academics researching them. It is
not unreasonable to assume that
practitioners and academics (or
both as a team) would formalize
prescriptions differently.
Depending on the context of the
artifact-to-be-designed, the codified
prescriptions may vary based on who
codified them. For example,
knowledge codified by practitioners
might be closer to an instance level,
while academics might reach a more
abstracted level.
Designing a jug can be done
regarding how to use it, how it
works, and how it is designed (see
Depending on the context of the
artifact-to-be-designed, indicating
whether prescriptions address form,
function, and/or implementation can
provide users with easier access to use
Designing a jug requires, at least
in part, following a sequence. If
there is no jug to place a handle
on, it is impossible to execute
principle seven. However, it
might be irrelevant whether first
to attach the handle or first to
attach a spout as they have no
direct coupling.
Depending on the context of the
artifact-to-be-designed, prescriptions
should be entirely or partly sequential
since some components might require
others to exist beforehand. However,
there can also be cases where no
sequence is necessary. Indicating
sequentiality can prevent errors in use.
Designing a jug has multiple
pieces that depend on each other.
For example, the shape of the jug
indicates where a handle can be
sensibly placed. So, adjusting the
shape once the handle is already
placed would mean that,
potentially, the handle would also
need to be adjusted.
Depending on the context of the
artifact-to-be-designed, prescriptions
should indicate that some components
of an artifact might depend on each
other, meaning that the design of one
component influences the design of
another. Indicating these
interdependencies through explicating
whether the prescriptions are modular
or non-modular is of value to clarify
these relationships and prevent
potential errors.
The principles outlined above
indicate a heuristic prescription to
designing a jug. They recommend
a course of action rather than
prescribe strictly which
dimensions the jug should have.
The principles assist designers in
bringing a jug into existence, yet,
they leave a broad room of
freedom in how the resulting jug
will look and work.
Depending on the context of the
artifact-to-be-designed, there might be
different requirements on whether the
prescription requires to be heuristic or
algorithmic. For example, designing
prescriptions with implications for
human health should be indicated as
algorithmic to narrow the room for
interpretation as closely as possible.
Designing a chair, room for
interpretation, and flexibility in
heuristic prescriptions might be
sufficient or even more suitable to
allow room for creativity.
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The principles outlined above can
either be followed loosely or
rigorously. Yet, designing might
allow for more freedom. So, it
might not be necessary to follow
the order exactly. The third
principle indicates that the spout
can be contiguous with the main
opening or not. Subsequently, the
principle has a degree of
looseness inherently built into it.
Depending on the context of the
artifact-to-be-designed, prescriptions
should be indicated as to be followed
loosely or rigorously. Take, for
example, the design of a bridge. Here,
it is paramount that design
prescriptions are followed rigorously.
In designing a jug, the designer
can develop prescriptions by
accumulating knowledge before
the design process, observing a
jug in use, and inferring design
Depending on the context of the
artifact-to-be-designed, there might be
enough design knowledge available
prior to design, which merits
formulating prescriptions a priori.
Contrarily, prescriptions can be derived
from successful implementation
through reflections on the outcome and
underlying process.
Complementing the dimension
process generating prescriptions
for designing jugs can be
achieved through synthesizing
available design knowledge (.e.g.,
from the literature)
Depending on the context of the
artifact-to-be-designed, either
synthesizing prior knowledge or
extracting new knowledge might be
more efficient or even necessary.
Designing a jug has multiple
reasonable grounding
mechanisms, such as prior
experience in grounding other
jugs (or similar artifacts) or laws
of nature that somewhat prescribe
some aspects of a jug (e.g., the
hole should be at the top).
Depending on the context of the
artifact-to-be-designed, prescriptions
may be grounded in either the
Environment (e.g., experience) and/or
the Knowledge Base (e.g., theories or
Evaluating if a jug works as
intended, most likely, is easiest
done by instantiating the
prescription and seeing whether
the artifact works.
Depending on the context of the
artifact-to-be-designed, there are
different strategies for evaluation. For
example, algorithmic prescriptions
should be provable, while heuristics are
usually supportable with empirical
Table 5. Taxonomy application for designing a jug based on Gregor et al. (2013). * D
= Dimension from the taxonomy.
6 Implications
6.1 Implications for design science
Prescriptive design knowledge is a critical outcome that enables designers to
elevate their findings to a more abstract level and apply their learned
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knowledge in different scenarios. Our work has multiple implications for
prescriptive design knowledge in design science as a research field and paves
the ground for future investigations on prescriptive knowledge. Our work
extends the existing knowledge on codifying prescriptive design knowledge
through a cross-disciplinary and cross-conceptual element. Thus, our work is
not a replacement for templates and structures (e.g., the anatomy of a design
principle by Gregor et al. 2020) but enriches the field by highlighting options,
i.e., visualization of prescriptive design knowledge instead of textual
codification or algorithmic against heuristic formulation. The dominant
mechanism in IS research is the design principle, which is frequently published
in high-ranking journals and premiere conference proceedings (e.g., Möller et
al. 2021). The existing body of literature includes formulation templates and
structures for constructing and communicating design principles (e.g.,
Cronholm and Göbel 2018; Heinrich and Schwabe 2014). This is
complemented by many conceptual works investigating the actual use of
design principles (e.g., Chandra Kruse et al. 2022), tensions in their
formulation (Chandra Kruse and Seidel 2017), investigations of their origins
(e.g., Purao et al. 2020), propositions for their reusability (e.g., Iivari et al.
2020), or papers conceptualizing design principles in relation to design theory
(e.g., Gregor and Jones 2007). In relation to these conceptual works and the
existing body of literature in IS research, we see two ways our solution space
advances the field. First (1), by expanding the array of options, one has to
codify prescriptive design knowledge through synthesizing and learning from
other disciplines. Second (2), using our solution space as an additional tool to
support transparency and clarity when formulating and communicating
prescriptive design knowledge.
Design options for prescriptive design knowledge codification.
Arguably, the general process of designing is messy, especially when
addressing ill-structured problems that demand a search process (Cross 2007;
Hevner et al. 2004). Subsequently, we can argue that codifying what has been
learned during the design process is hard to grasp and actually achieve. Mainly
since codification for reuse, to some degree, requires abstraction from detail,
generalization, and focus on ‘important’ aspects (Hansen and Haj-Bolouri
2020; Walls et al. 1992). Thinking in comprehensive solution spaces that
transcend singular codification mechanisms, we intend to spur a discussion in
the DSR community on finding common ground in codifying prescriptive
design knowledge. By continuously developing the solution space (e.g.,
finding fundamental dimensions and characteristics), the DSR community can
position their knowledge and find new ways to codify prescriptive design
knowledge. Subsequently, our work contributes to prior research addressing
the issue of consistency in DSR knowledge contributions (Dwivedi et al. 2014).
Given the above, our approach is also a proposition to learn from individual
concepts, be they design principles or technological rules, and to cross-profit
from their particular advantages. For example, design principles are usually
codified linguistically in textual form. Yet, we argue, they could be
complemented through design examples or visual aids.
Clarity in formulating and instantiating. Clarifying how prescriptive
design knowledge should be used as the potential to strengthen its reliability
and operationalizability (Nowack 1997). For example, design principles
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usually do not seem sequential or modular, yet, implementing these concepts
could maintain rigor and usability. Differentiating and indicating whether
prescriptive design knowledge is a loose recommendation or needs to be
followed strictly is essential for formulating, publishing, and instantiating such
knowledge. It enables users to understand better how instantiation is supposed
to happen and what codifier of prescriptive design knowledge had in mind.
Algorithmic prescriptions are highly specific in what constitutes the
prescriptions (e.g., as known from chemistry: take x or y mg of a particular
substance to achieve a reaction), which also has implications for the probability
of attaining a specific outcome. Contrarily, heuristic prescriptions, which are
context-based and only “increase[s] the chance of reaching a satisfactory but
not necessarily the optimal solution.” (Fu et al. 2015, p. 4). Carlsson (2007, p.
80) explains that “[a]n algorithmic design proposition can in principle
guarantee a good (best) outcome, and that “[a] heuristic design proposition
does not guarantee success, but it supports the development of a successful
system.” Explaining why prescriptive design knowledge works are of
paramount importance to convince others of its utility. The range of different
variants of grounding mechanisms requires strong argumentation and
complementation. For example, while initial prescriptive knowledge might be
derivable from theory, empirical evidence could complement it. That is
primarily important in heuristic prescriptive design knowledge, which usually
cannot be proven but can only be argued based on grounding mechanisms and
theoretical saturation of empirical evidence (van Aken 2004).
6.2 Limitations and outlook
Our work is subject to limitations. First, our sample of papers only is an excerpt
that we have identified to be seminal (e.g., see Bunge 2012) or that we have
found following our procedure outlined in Section 3. Hence, there likely exist
more articles that should be included when the study is developed further.
Given that the field of prescriptive codification mechanisms is unstructured, it
is likely that there are more mechanisms that we have missed and that could be
uncovered in subsequent studies. As our sample is not comprehensive, other
papers might explicitly attribute prescriptive codification mechanisms with a
characteristic that we might have missed. Yet, as we strive for
comprehensiveness in the solution space, we see that issue as mitigated, as
additional individual characteristics already covered would not change the
outcome. Additionally, using that taxonomic approach explicitly demands
extendability once new characteristics or dimensions of the phenomenon under
investigation emerge (Nickerson et al. 2013). Second, as our research is
qualitative, other researchers might find other dimensions and characteristics
more significant or use different terminology. Third, we have assumed that
each codification mechanism is, conceptually, on a similar level and of equal
value. Subsequently, we did not include transformation mechanisms between
codification mechanisms in our study. Fourth, we have not distinguished
between sets of prescriptive knowledge mechanisms and singular mechanisms
and instead treated both as a single piece of prescriptive knowledge. Using the
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dimensions of modularity and sequentiality, the value of a set of prescriptive
knowledge mechanisms could be further uncovered.
With the taxonomy, multiple avenues for further research are opened. First,
as we took a narrow view of selected codification mechanisms, there is an
opportunity to widen the frame to additional concepts. We strictly worked with
mostly seminal, conceptual papers. Subsequently, there is potential to integrate
papers into the study to develop prescriptive design knowledge in one of the
above forms. A possible road ahead is to identify transformation mechanisms
between specific types of design knowledge. For example, Greer et al. (2002)
refer to the possibility of transforming design rules and design guidelines into
each other by scoping the level of detail and abstraction. Lastly, as taxonomies
(i.e., our solution space) should, generally, be extendable (Nickerson et al.
2013), we hope that it is used and extended by other researchers in subsequent
works analyzing prescriptive design knowledge codification. Based on our
findings, a fruitful road for further research is using specific dimensions (e.g.,
grounding) as a basis to indicate the maturity of design knowledge. Our study
did not analyze the usefulness or weaknesses of specific configurations.
However, we see this as a potential avenue for further research, resulting in
overarching strategies or archetypical configurations tailored to particular
contexts and design projects that can exceed existing knowledge on codifying
prescriptive design knowledge. Another fruitful route for research is to extract
combinations in our solution space and analyze how they contribute to
spanning boundaries in an interdisciplinary team in socio-technical system
designs and what tensions arise in the transfer of codified design knowledge
(e.g., see Baxter and Sommerville 2011; Guzman and Trivelato 2008).
Scholars already propose viewing prescriptive design knowledge (e.g.,
principles and design rules) as boundary objects (e.g., Romme and Endenburg
2006). A deeper analysis of different codification mechanisms with the
resulting implications (e.g., the implementability of heuristic versus
algorithmic prescriptions) would be an interesting route for new research.
Consider the following: Although algorithmic prescriptions usually have to be
followed rigorously, there may be reasons in reality that prevent this.
Alternatively, using heuristics requires to ‘pay the price’ that success is not
guaranteed, and more interpretation is required. Both can result in tensions that
might need to be mitigated. Last, our work can be a starting point to find new
ways of combining characteristics of prescriptive design knowledge
codification and to assess the validity of specific configurations.
7 Conclusion
With our in-depth analysis of codification mechanisms for prescriptive design
knowledge, we make contributions to academia and practice. Our results
indicate that most of the codification mechanisms in our sample are highly
similar yet differ in nuances. Our work provides a comprehensive solution
space for prescriptive design knowledge codification that transcends the
borders of singular concepts enclosed in silos and illustratively explicates these
nuances against each other. For example, the notion of sequentiality in
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technological rules could easily be integrated into studies developing design
principles, giving them (if it provides merit for the study) additional structure
that can enhance insatiability. Given that we synthesize a solution space and
show that it is sensible to think of these dimensions depending on the context
one develops prescriptions in, we see a significant contribution to the
efficiency, usability, and usefulness of prescriptions in DSR. As another
example, researchers and practitioners should be specific about whether their
prescriptive design knowledge is heuristic or algorithmic, whether it is a
recommendation or strictly to be followed to increase reliability. Next to the
reliability, our research makes prescriptive design knowledge codification
more transparent, potentially benefitting researchers in presenting their work
to peer-reviewers, practitioners, and other researchers. Accordingly, this study
enables researchers to leverage the entire solution space of prescriptive design
knowledge rather than singular concepts and transcend the field of their origin.
We see that avenue as fruitful, as our analysis shows, that the primary paradigm
behind the codification mechanisms is a shared understanding of prescriptive
action, addressing a class of problems, and guiding designers. For practitioners,
we see increasing reliability of prescriptive design knowledge as a way to make
instantiation easier, operationalizable, and more accessible. For instance, it
might be easier to apply design principles once they have clear instructions on
how to be used (e.g., strictly) and for what (e.g., for the form or function).
We found promising indications for promoting prescriptive design
knowledge within the DSR community and beyond and shed light on the
discourse of what are fundamental differences and components of such
prescriptions, which will help academia and practice alike.
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... We see design principles as a valuable object of investigation because focusing on this more conceptually narrow object (contrary to fully-fletched design theories or large-scale DSR studies) allows us to study it in-depth. It also is a pivotal component in design theories (e.g., Markus et al. 2002) and experiences significant attention in the IS community (e.g., Möller et al. 2022). Also, we can move toward understanding the specific relationship between design principles and kernel theory, following Kuechler and Vaishnavi (2012, p. 400), who observed that "no guidance on their [kernel theories] refinement to design principles is given." ...
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