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An Integrated Model of Dynamic Problem Solving Within Organizational Constraints

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University of Nebraska, Omaha, NE, USA
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University of Nebraska Medical Center, Omaha, NE, USA
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University of Connecticut, Storrs, CT, USA
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Individual Creativity in the Workplace 53 © 2018 Elsevier Inc. All rights reserved.
An Integrated Model of
Dynamic Problem Solving
Within Organizational
Johnathan R. Cromwell*, Teresa M. Amabile,
Jean-François Harvey
Department of Entrepreneurship, Innovation, and Strategy, University of San
Francisco, San Francisco, CA, United States, Entrepreneurial Management
Unit, Harvard Business School, Boston, MA, United States, Department of
Entrepreneurship and Innovation, HEC Montréal, Montréal, QC, Canada
The journey that people take to produce creative ideas is often a
winding path that involves several twists, turns, detours, and reversals of
direction. At many points throughout the process, people are confronted
with questions of whether they should keep investing resources into an
existing idea, start searching for a new idea, or change the problem they
are working on altogether. The result is that creative projects often take
seemingly unique paths to success, making it difficult to predict the cir-
cumstances of creative success. Consider, for example, the creation of the
commercial light bulb and the Nintendo Wii, two technological inventions
that took quite different paths to development.
In 1878, more than 75 years after the electric light bulb was invented,
Thomas Edison began a research program with the goal of developing a
commercially viable light bulb, which needed to satisfy the criteria of be-
ing long-lasting, cheap to produce, and energy efficient (Israel, 1998).
Throughout the course of development, Edison and his team conducted
thousands of experiments using different combinations of designs and ma-
terials. After more than a year of experimentation, in October 1879, they
developed a viable solution that went on to revolutionize the energy in-
dustry. By contrast, the Nintendo Wii was created in 2006 based on a tech-
nology that was developed nearly 30 years earlier by people working in a
completely different industry (Verganti, 2009). In the late 1970s, a company
called STMicroelectronics developed a new semiconductor that could de-
tect three-dimensional movement. After creating the technology, engineers
searched for commercial applications across a broad range of industries,
but found limited success with applications in computers, home appliances,
and automobiles. It was not until 2005, after meeting with game developers
at Nintendo, that they learned how their technology could be used to create
a highly novel gaming experience. Shortly thereafter, the Nintendo Wii de-
buted on the market and went on to transform the gaming industry.
Both of these stories illustrate creative invention, but they differ in two
important ways. First, the creative process seemed to take two different
paths to success. In the case of the light bulb, Edison and his team began
with a well-defined problem and then searched broadly for materials and
technology that could solve the problem, generating thousands of different
idea combinations until they finally developed a viable solution. This cre-
ative process resembles the “problem-solving” model of creativity that is
commonly described in the organizational creativity literature. According
to this model, an inventor first finds, defines, or formulates a problem (e.g.,
Getzels & Csikszentmihalyi, 1976; Mumford, Reiter-Palmon, & Redmond,
1994), and then engages in a dynamic process of gathering information,
generating ideas, elaborating ideas, evaluating ideas, and selecting ideas
until a solution has been created (Amabile, 1983; Amabile & Pratt, 2016;
Mumford, Mobley, Reiter-Palmon, Uhlman, & Doares, 1991).
In the case of the Nintendo Wii, however, engineers started with a
well-defined technology and then searched broadly for commercial ap-
plications across different industries, eventually discovering a problem in
the gaming industry that could be solved with their technology. This pro-
cess resembles the “Geneplore” model of creativity (Finke, Ward, & Smith,
1992), in which an inventor first generates a potentially useful idea—what
is known as a “preinventive structure”—and then explores how it may
solve problems across a wide variety of problem domains until a problem
and solution emerge together. While the problem-solving model begins
with the definition of a problem and is followed by a search for solutions,
the Geneplore model begins with the creation of a preinventive idea and
is followed by a search for problems. At first blush, these problem-first and
idea-first models seem to describe entirely different creative processes.
The second way in which these two examples differ is that the condi-
tions of constraint for each group of inventors appear to have been quite
different. Edison and his team were heavily constrained by the problem
they were trying to solve, but they had great flexibility when searching
for a solution to that problem. The semiconductor engineers, on the other
hand, were heavily constrained by the technology they were working
with, but had great flexibility when searching for a problem that could be
solved with that particular preinventive idea.
In each case, inventors experienced a different confluence of constraints
and engaged in a different creative process. We argue that this is no mere
coincidence. Indeed, these examples suggest that constraints might sys-
tematically influence the creative process—an idea that has received little
attention in the creativity literature (see Caniëls & Rietzschel, 2015 for a
review). While prior literature has developed extensive theory on how
constraints can influence creative outcomes (e.g., Amabile, Conti, Coon,
Lazenby, & Herron, 1996; Baer & Oldham, 2006; Byron, Khazanchi, &
Nazarian, 2010; Finke etal., 1992; Hunter, Bedell, & Mumford, 2007), it has
developed little theory on how they might shape the creative process.
Taken as a whole, prior literature presents two competing models of
the creative process, each of which is compelling. But it is unclear when
and why people are likely to engage in one process over the other. In this
chapter, we address this puzzle by first reviewing the theoretical founda-
tions of each model, showing that they originate from the same underlying
cognitive framework of problem solving. However, we find that a clear
differentiator between the two models is based on the level and type of
constraint that people face at different times during the creative process.
We u se t hi s ob se r va ti o n— th a t co ns tr ai nt s c an s ha pe t he c re at iv e pr oc es s
as the underlying premise for our own model, and we expand upon these
arguments to account for more recent findings on creativity and constraint.
We build our model by first developing a typology of constraints that is
based on two fundamental dimensions of constraint—types of constraint
(resource constraints vs problem constraints) and sources of constraint (internal
constraints vs external constraints). We then synthesize the problem-first and
idea-first models of the creative process into a new dynamic problem-solving
model. The result is an integrated model in which different creative pro-
cesses unfold depending on the confluence of constraints that people face
on a creative project. The model is dynamic, in that it allows for dynamic
iteration between these two models, so that as constraints shift over time,
inventors may shift from one creative process to the other.
Creativity in organizations is the creation of novel and useful (or ap-
propriate) products, processes, services, or ideas (Amabile, 1983, 1988;
Oldham & Cummings, 1996; Shalley & Zhou, 2008; Woodman, Sawyer,
& Griffin, 1993). To produce these outcomes, people engage in a messy
and unpredictable process that includes a wide range of activities such
as defining problems, generating ideas, and evaluating ideas against cri-
teria—among others. While many of these activities are often necessary
for creativity, the order and sequence by which they produce creative out-
comes can vary widely. Scholars have broadly codified these activities into
one of two general models, which we refer to as the problem-first and idea-
first models of the creative process. We summarize these models in Fig.1,
showing how each model theorizes the set of activities and sequence of
activities that characterize the creative process.
A majority of organizational creativity research has adopted the
problem-first model of the creative process (Amabile, 1983, 1988; Amabile
& Pratt, 2016; Mumford etal., 1991; Wallas, 1926). According to this model,
organizational creativity begins when a problem is defined, which is often
considered the most important step of the process. As Einstein described
it, “The formulation of a problem is often more essential than its solu-
tion, which may be merely a matter of mathematical or experimental skill.
To raise new questions, new possibilities, to regard old problems from a
new angle, requires creative imagination and marks real advance in sci-
ence” (Einstein & Infeld, 1938). In organizations, problems are usually de-
fined when a higher-level manager presents a problem to an employee,
but it can also occur when employees define their own problem to solve
Problem-first model of the creative process
Idea-first model of the creative process
(Abstracted from Amabile, 1983; Amabile & Pratt, 2016; Mumford et al., 1991; Wallas, 1926)
(Abstracted from Finke, Ward, & Smith, 1992)
new ideas
new ideas
Notes: Dotted lines refer to feedback loops in which individuals can return to earlier activities
of the creative process. According to the theorists cited, the activities in each creative process
need not occur in the exact sequence shown.
FIG.1 Summary of two general models of the creative process.
(Getzels & Csikszentmihalyi, 1976; Unsworth, 2001). Once a problem has
been defined, people engage in a sequence of activities—such as gathering
information, generating ideas, and evaluating ideas against criteria—until
a viable solution has been reached. Gathering information involves the
collection of data and materials that will help individuals solve a problem.
Generating ideas involves drawing on resources to develop a set of ideas
that can potentially solve the problem. And evaluating ideas involves tak-
ing a subset of promising ideas and evaluating them against the problem
When an idea satisfies all the criteria, it becomes a viable solution to
the problem and can be selected for implementation, thereby ending the
creative process. But if an idea fails to satisfy all the criteria, people must
revert back to earlier stages of the process. For instance, they may need to
generate a new set of ideas or elaborate upon existing ideas (Perry-Smith
& Mannucci, 2017). They may need to gather additional information about
the problem, or they can set the problem aside and not consciously work
on it for a time—a process known as incubation that can sometimes result
in a breakthrough idea (Guilford, 1950; Kounios & Beeman, 2015; Wallas,
1926). They may also need to change the problem they are working on
by applying existing resources to new problems (e.g., Baker & Nelson,
2005; Sonenshein, 2014). In most versions of the problem-first model (e.g.,
Amabile & Pratt, 2016), people can return to an earlier point from any later
point in the process, resulting in a cyclical process until (ideally) a viable
solution is created that satisfies all the problem criteria.
By contrast, a growing body of literature draws from an idea-first
model of the creative process (Finke etal., 1992). According to this model,
the creative process begins when people generate a new idea that has the
potential to be useful, but the problem has not yet been defined. These
ideas are called “preinventive structures” because they do not reflect fully
formed solutions, but rather, reflect ideas that are precursors to solutions
that will eventually emerge from the creative process. For example, the
STM engineers began their creative process by developing a semiconduc-
tor that could detect movement in three-dimensional space. While this ini-
tial technology represented a potentially useful idea, they did not know
specifically what problems could be solved with it (Verganti, 2009), and
thus it was only a preinventive idea.
Once a preinventive idea has been created, people can explore whether
it can solve a specific problem within a problem domain. If a problem can-
not be defined, then people must return to an earlier activity in the pro-
cess, either generating a new preinventive idea, elaborating an idea that
they already generated, or exploring their idea in the context of a different
problem domain. If a problem can be defined, a viable solution emerges
to become a creative outcome. For example, the STM engineers explored
whether their new semiconductor could solve a problem within a broad
range of industries, but they struggled to develop creative solutions in
many of them. Once they came across the gaming industry, however, they
discovered a specific problem that could be solved with their technology,
and a solution emerged in the form of a new videogame system.
Theoretical Origins of the Problem-First and Idea-First Models
of the Creative Process
An initial comparison of these two models suggests that they reflect en-
tirely different creative processes. However, both models are actually built
on a cognitive framework of problem solving (Newell, Shaw, & Simon,
1962; Newell & Simon, 1972), and they differ in their assumptions about
individuals who are engaged in this problem-solving process. According
to the cognitive framework, problem solving occurs within a problem
space, which consists of (a) an initial state, (b) a desired goal state, and (c)
all intermediate states in between. The process begins when an individ-
ual reads a problem statement, which triggers a variety of memories, con-
cepts, or other cognitive elements that are relevant to the problem at hand.
Then, drawing from these cognitive elements, the individual generates
ideas that could solve the problem. For example, imagine a movie pro-
ducer who is presented with the problem of creating her 10th film. When
presented with this problem, she will draw on all her prior expertise and
knowledge from developing her previous nine films (as well as her formal
and informal training), which could then be applied to developing her
10th film.
After generating a potential pool of ideas, the individual evaluates
these ideas against the criteria of the goal state. If an idea satisfies all
the criteria, it is considered a viable solution to the problem and the
process can end. But if it fails to satisfy the criteria, the individual must
revert back to earlier stages of the process. She can generate new ideas
based on cognitive elements she already possesses, or gather new in-
formation from the environment to build a larger base of cognitive el-
ements to use for generating ideas. The particular set of ideas and the
sequence in which they are produced define the intermediate states of
the problem space.
A key feature of this process is that problems exist along a continuum,
ranging from being “well-structured,” in which all three parts of the prob-
lem space are well known, to being “ill-structured,” in which at least one
part of the problem space is unknown (Reitman, 1965; Simon, 1973). When
a problem is well structured, it can be solved algorithmically: That is, an in-
dividual can apply her existing knowledge to the new problem, and prob-
lem solving will be fairly linear. For example, a movie producer who has
extensive experience creating Spider-Man movies will be able to develop
much of a new Spider-Man movie by relying on her existing knowledge.
Although audience tastes may change and resources or equipment can
evolve, developing the film will follow a fairly linear and predictable se-
quence of steps.
Problems become ill structured when aspects of the problem space are
unknown. The simplest form of ill-structured problems occurs when the
goal state is known and the initial state and intermediate states are un-
known. Under these conditions, a problem must be solved heuristically:
That is, an individual must use their intuition to develop ideas that might
solve the problem, but there is more uncertainty because they are operat-
ing with incomplete information. For example, if the movie producer who
specializes in Spider-Man movies tries to develop a movie for a different
superhero—such as Superman—much of her knowledge might still be
relevant, but she may need to spend more time gathering new informa-
tion, elaborating ideas, and evaluating ideas against criteria throughout
the process. If she were creating a film in an entirely different genre—such
as a comedy—she would need to spend even more time engaging in these
activities, resulting in an even more cyclical creative process. Generally
speaking, the degree to which problems are ill structured determines the
degree to which the creative process is cyclical.
The most complex form of ill-structured problems occurs when all
three aspects of the problem space are unknown, including the initial
state, intermediate states, and final goal state. These problems have been
described as “open problems,” whereas situations in which the goal
state is known are called “closed problems” (Unsworth, 2001). When
problems are open, problem statements are vague and poorly defined,
and they fail to trigger specific cognitive elements that can be used
to solve the problem at hand. For example, a movie producer may be
given the problem statement, “develop a new breakthrough movie,” but
this can be so vague—even for an experienced movie producer—that
it does not trigger any specific cognitive elements that can be used to
produce a viable solution. This was the situation that movie producers
at Pixar found themselves in when they were developing the world’s
first feature-length computer-generated film in the late 1990s (Catmull,
2014). Aided with new computer technology that gave them the ability
to create any plotline, character, or setting they could imagine, they ex-
perienced extreme levels of ambiguity and struggled to develop ideas
that could solve their problem. It was not until they focused their efforts
on developing a compelling storyline that their idea for Toy Story began
to take shape.
It is under these open-problem conditions that the two models of the
creative process begin to diverge. According to problem-solving scholars,
people can approach open problems by engaging in a multitiered version
of the problem-first model (Simon, 1973). First, they read the vague prob-
lem statement, which triggers several vague cognitive elements that are
relevant to the problem. For example, the movie producer who is asked
to “develop a new breakthrough movie” will think of several vague
cognitive elements such as “genre, plotline, cast, characters, and break-
through movies.” Each of these vague cognitive elements then becomes a
new subproblem to solve, which triggers a new set of cognitive elements.
For example, addressing the “genre” subproblem might trigger cognitive
elements such as “action, adventure, comedy, etc.,” which in turn could
trigger a new set of cognitive elements that become new subproblems.
With each tier of the process, problem statements and cognitive elements
become more specific; eventually, the open problem is adapted into a net-
work of smaller, more specific, and closed subproblems.
An important consequence of this process is that the various subprob-
lems become interdependent, meaning that solving one subproblem
changes the problem space for other subproblems. For example, the movie
producer may develop a solution to the “character” subproblem, which in
turn may change the initial state of the “genre” subproblem; and devel-
oping a solution to the “genre” subproblem may change the initial state
of the “plotline” subproblem, and so on. Each time the problem space for
one subproblem changes as a result of progress made on other subprob-
lems, the problem solver has to reassess whether the goal states across all
subproblems are aligned. If there is any misalignment, she must modify
some of the goal states of the subproblems. Therefore when people are
working on open problems, a new activity emerges that does not exist
for closed problems: They must constantly redefine problems during the
problem-solving process. Eventually, open problems become closed prob-
lems, and people can then adopt the problem-first model to developing
By contrast, the idea-first model proposes an alternative set of activ-
ities when working on open problems (Finke etal., 1992). Rather than
adapting open problems into a network of smaller, closed subproblems,
the opposite can be done, that is, the problem statement can be removed
altogether, allowing people to generate ideas in the absence of thinking
about a specific problem at all. To demonstrate this point, Finke (1990)
conducted an experiment in which subjects were given a subset of 3 out of
15 materials (e.g., hook, ball, and spring, etc.) to develop inventions in 1
out of 8 problem domains (e.g., furniture, toys, or appliances, etc.). In the
first condition, subjects received both the materials and problem domain
at the beginning of the task, and they were given 2 min to develop an idea
for an invention. In the second condition, subjects received the materials
at the beginning of the task, and they were given 1 min to generate a “po-
tentially useful” idea; then, they received the problem domain and were
given an additional minute to explore their ideas within that problem
domain. Results showed that subjects in the second condition produced
more creative ideas than those in the first condition, revealing that people
can be highly creative when they first generate an idea and then explore
that idea in the context of a problem domain.
Two Paths or One? The Role of Constraint
Finke and colleagues argue that while the problem-first model is a
useful tool to understand the creative process for closed problems, it is
ultimately limited, because in the real world, few meaningful problems
are truly closed: “[Problem-solving] approaches detail specific processes,
but they apply to highly restricted domains rather than to creative func-
tioning in general. We believe that in order to understand the true nature
of creativity, cognitive processes must be considered in a much broader
perspective, where the problems and solutions are not necessarily restricted
or known(Finke etal., 1992, p. 5; emphasis added). They go on to argue
that the key difference between their framework and the problem-first
model comes down to a difference in when constraint appears in the
problem-solving process: “[Our] approach can complement the more
usual [problem-solving] approach… whether one approach or the other
should be used depends on how early in the creative process product con-
straints would need to be imposed” (Finke etal., 1992, p. 191). Therefore,
according to these scholars, when constraints are applied early in the pro-
cess, the problem-first model unfolds, and when constraints are applied
late in the process, the idea-first model unfolds.
While we agree with this general notion, we also believe that these
scholars have defined constraints too narrowly as they focus on constraints
related to the problem definition. When considering more recent research
on creativity and constraint, a much broader range of constraints have
been shown to influence the creative process. For instance, constraints on
resources such as time, finances, materials, or knowledge can limit cre-
ativity by undermining a person’s engagement in the creative process
(e.g., Amabile etal., 1996; Byron etal., 2010; Hunter etal., 2007). Therefore
we believe the picture is more complex than what has been previously
In the following sections, we aim to develop a more complete theoret-
ical model that presents a more complex view of constraint and encom-
passes both the problem-first and idea-first models of the creative process.
First, we draw on research in organizational creativity to derive two di-
mensions of constraint that we use to build a typology of constraints that
affect creative problem solving in organizational settings. Then, we syn-
thesize the creative activities that make up the problem-first and idea-first
models into a new model that we call the dynamic problem-solving process.
Finally, building on Finke’s assertion about the timing of constraint, we
develop a set of propositions that describe when particular confluences
of constraint influence the dynamic problem-solving process to result in
different creative processes.
Prior researchers have defined constraint in one of two ways: as any
element that influences problem solving (e.g., Finke etal., 1992; Reitman,
1965), or as any external factor that in some way limits—or could be per-
ceived as limiting—the way that a problem solver completes a task (e.g.,
Amabile, 1979; Amabile & Gitomer, 1984; Deci & Ryan, 1985). To encom-
pass these somewhat different views, we define constraint quite broadly
as any factor that places limits or boundaries on creative problem solving. With
this definition in mind, we derive two dimensions of constraint that we
believe to be important in all problem-solving situations.
The first dimension is type of constraint, which is based on Rosso’s find-
ing that there are two fundamentally different types of constraint that
influence the creative process (Rosso, 2014). First, he identified “process
constraints,” which include limitations on time, materials, finances, and
equipment that restrict people’s ability to engage in the creative process.
Second, he identified “product constraints,” which include product re-
quirements, customer preferences, and organizational needs that struc-
ture the goals that people pursue during the creative process. He argued
that while process constraints usually place detrimental limits on creative
problem solving, product constraints can provide structure to problems
that improve the creative process.
Viewing process constraints and product constraints through the lens
of the cognitive framework on problem solving (Newell & Simon, 1972),
we reason that these two types of constraint are related to different factors
that structure the problem space. Process constraints place limits on the
resources that people use when generating ideas as they navigate through
the problem space; and product constraints establish how the problem
is defined by the goal state. Therefore we differentiate between resource
constraints (what Rosso calls “process constraints”) and problem constraints
(what Rosso calls “product constraints”) to reflect the qualitatively differ-
ent effects that constraints can have on problem solving.
The second dimension of constraint is the source of constraint (Deci &
Ryan, 1985; Ryan & Deci, 2000), which influences the degree to which
people perceive themselves to be constrained during the problem-solving
process. According to Deci and Ryan, an individual’s autonomy is deter-
mined by the extent to which they feel like they have control over the fac-
tors that influence their behavior. When an individual feels like they have
a high degree of self-determination of their own behavior, they perceive
higher levels of autonomy; when they feel like external factors determine
their behaviors, they perceive lower levels of autonomy.
In the context of creative problem solving, resource constraints and
problem constraints determine the behaviors that people can engage in
during the problem-solving process. Therefore the degree to which people
feel like they have control over the resources and problems that structure
their problem space determines how constrained they feel during the cre-
ative process. For the sake of simplicity, we dichotomize this dimension,
differentiating between internal constraints and external constraints, but
we acknowledge that this dimension can be more accurately depicted as a
bipolar continuum, such that constraints can range from being completely
internally controlled to completely externally controlled (e.g., Ryan &
Deci, 2000). Internal constraints refer to resource or problem constraints
that are self-imposed on creative problem solving; external constraints re-
fer to resource or problem constraints that are imposed by an external
source—for instance, by a higher-level manager or supervisor.
Typology of Constraints
Together, these two dimensions of constraint form a typology of con-
straints that serves as the foundation of our model. In the typology, each
dimension has two categories, creating four quadrants of constraint: (a)
internal resource constraints, (b) external resource constraints, (c) internal prob-
lem constraints, and (d) external problem constraints. Before describing the
specific list of constraints that occupy each quadrant in more detail, we
note two caveats. First, we tried to include constraints that we believe to be
relevant in organizational settings, but we recognize that our list may not
be entirely exhaustive. We expect that the four quadrants of constraint will
generalize across all problem-solving situations, but specific constraints
may differ according to the particular setting. Second, we categorized con-
straints based on what we believe to be typical in most organizational
settings, that is, when an individual employee is working on a creative
project under a manager in an organization. However, we understand that
constraints can also be categorized differently, depending on the situation.
Internal Resource Constraints
This quadrant includes resource constraints that are implicitly “im-
posed” on the creative process by the individual who is engaged in cre-
ative problem solving. These resources include the individual’s creativity
skills, which reflect the person’s ability to combine ideas in novel ways,
and domain-relevant skills, which include knowledge and technical exper-
tise that is necessary for navigating the problem space and generating
ideas (Amabile, 1983). People who have more diverse sets of knowledge
and greater skills can represent a problem in multiple ways, giving them
a greater capacity to generate ideas through conceptual combination and
analogic thinking (Finke etal., 1992; Newell & Simon, 1972). Such knowl-
edge and skills are also valuable for defining or formulating problems
(Mumford etal., 1994; Runco, 1994), which can also result in more creative
outcomes (Getzels & Csikszentmihalyi, 1976; Reiter-Palmon, Mumford,
O'Connor Boes, & Runco, 1997). These resources are constrained based on
the extent to which the individual lacks creativity and domain- relevant
skills; therefore it can be difficult and time consuming for people to de-
crease these constraints over time. Although an individual can acquire
new knowledge or learn how to think more creatively, each of these re-
sources can take a long time to develop and may not be readily applied to
solving an immediate problem.
External Resource Constraints
This quadrant includes resource constraints that are imposed on the
creative process by external forces such as managers or supervisors.
These constraints include limitations on resources such as time, materials,
or finances, all of which help people generate ideas (e.g., Amabile etal.,
1996; Baer & Oldham, 2006; Weiss, Hoegl, & Gibbert, 2011). We also in-
clude a resource that we call extrinsic knowledge, which is different from
domain- relevant knowledge in that it exists in the external world and can
be acquired by the individual problem solver through search activities
(Fleming, 2001; Taylor & Greve, 2006). For example, designers at IDEO
regularly solved problems by taking ideas from one industry and ap-
plying them as solutions to problems in another industry (Hargadon &
Sutton, 1997). Similarly, individuals may communicate with people inside
or outside their organization to search for ideas that can help them solve
a problem they are working on (Hargadon & Bechky, 2006; Lingo and
O'Mahony, 2010; Perry-Smith & Shalley, 2003).
Internal Problem Constraints
All creative problems are defined by at least two primary criteria: nov-
elty and usefulness (or appropriateness) (Amabile, 1982, 1983; Shalley &
Zhou, 2008; Woodman etal., 1993). Novelty is based on the extent to which
an idea is original or different from previous ideas that solve a problem;
and usefulness is based on the extent to which an idea provides some ob-
jective or useful value to a designated audience, which in organizational
settings, is typically a customer. Prior research shows that managers have
a fairly strong bias against novelty and a preference for usefulness (Berg,
2016; Ford & Gioia, 2000; Mueller, Melwani, & Goncalo, 2012; Rietzschel,
Nijstad, & Stroebe, 2006, 2010). Therefore we reason that in most organiza-
tional settings, novelty is determined primarily by an individual’s desire
to generate novel ideas (i.e., is an internal constraint), and usefulness is
determined primarily by a manager’s preferences or customer demands
(i.e., is an external constraint). Other internal problem constraints include
domain-relevant goals, which are goals that are motivated by a person’s de-
sire to have an impact in a particular domain of expertise, but may seem
superfluous in the eyes of other stakeholders. For example, circus per-
formers who are creating new performances may be motivated to create
new acts that showcase their particular talent or skill (and thus impress
other circus professionals), but managers primarily care about how much
a paying audience likes the act, and may therefore discount ideas that do
not appeal to a mass audience (Berg, 2016).
External Problem Constraints
Finally, this quadrant includes constraints that place external limits or
boundaries on the problem definition. In most organizational settings,
problems are largely defined by other stakeholders such as managers, or-
ganizational leaders, teammates, customers, colleagues, or collaborators
from other organizations. These various stakeholder demands represent cri-
teria that an individual must satisfy that are beyond the scope of the prob-
lem that they would otherwise try to meet. For example, an engineer who
developed the world’s first digital camera at Kodak created a product that
was novel and potentially useful for customers, but failed to meet the cri-
terion of aligning with the organization’s strategy, which was imposed by
senior managers, and therefore the product was rejected (Lucas & Goh,
2009). Similarly, a problem may be constrained by broader contextual factors
such as an organization’s culture, societal values, or institutional norms
coming from the environment. For example, when Edison developed the
commercial light bulb, he was constrained by customers’ expectations
about gas-lighting technology, so he artificially reduced the power of his
light bulbs to conform to external institutional pressures (Hargadon &
Douglas, 2001). Finally, there are other situational factors that are viewed
as inherent to the task itself that may also constrain the problem. For
example, during the Apollo 13 space mission to the moon, an unexpected
explosion damaged the air filtration system within the space capsule.
NASA engineers in Houston were confronted with the problem of need-
ing to change the oxygen-to-carbon dioxide ratio to a certain level within
a certain period of time, or else the astronauts would die.
The Dynamic Problem-Solving Process
The four types of constraint described previously are always operating
on people as they engage in the creative process, but they are not always
operating in equal strength. It is possible for people to experience high lev-
els of one type of constraint and low levels of another type of constraint,
which can influence the overall creative process differently. For instance,
Edison and his colleagues were operating under low levels of internal and
external resource constraint, as they had ample amounts of time, materi-
als, finances, and expertise to use for experimentation; but they also had a
high level of external problem constraint, because the specific criteria that
had to be satisfied to develop a commercially viable light bulb were de-
termined primarily by factors outside of their control—such as customer
expectations about the technology and scientific limitations inherent in
the problem itself. Alternatively, the STM engineers experienced a differ-
ent confluence of constraints, in which they experienced high levels of
external resource constraints—in the form of a restricted technology—and
low levels of internal and external problem constraints.
We visually depict how various constraints relate to the creative pro-
cess in Fig.2. The horizontal dimension of constraint depicts the two types
of constraint, with the left half representing resource constraints and the
right half representing problem constraints. The vertical dimension de-
picts the two sources of constraint, with the lower half representing in-
ternal constraints and the upper half representing external constraints.
In the center of Fig.2 lies the dynamic problem-solving process, which syn-
thesizes all the activities from the problem-first and idea-first models of
Stakeholder demandsslairetaM
Creativity skills
Broader contextual factors
Domain-relevant skills Domain-relevant goals
Resource constraints Problem constraints
Internal constraintsExternal constraints
Two sources of constraint
Two types of constraint
Situational factors
* Indicates activities that can begin the creative process ** Indicates activities that can end the creative process
problem solving
*Define problem
Evaluate ideas
Change problem
*Generate ideas
Elaborate ideas
Incubate ideas
Gather information
Explore ideas
**Choose solution
**Solution emerges
FIG.2 Typology of constraints on the dynamic problem-solving process of individuals in
organizational settings.
the creative process into a more unified, comprehensive model. We call
it “dynamic” specifically because it accounts for both prior models while
also allowing for dynamic iteration between them.
Creative activities such as generating ideas, elaborating ideas, and in-
cubating ideas are represented together on the left side of the figure, be-
cause they are all directly influenced by the level of resources available
during the creative process. By contrast, activities such as defining prob-
lems, evaluating ideas, and changing problems are represented together
on the right side of the figure, because they are all directly influenced by
the problem constraints that people face during the process. Gathering
information and exploring ideas are represented near the top of the figure
because they are directly influenced by constraints that are external to the
problem solver. Finally, the activities of choosing a solution or a solution
emerging are represented near the bottom of the figure because they are
directly influenced by constraints that are internal to the problem solver.
One important difference between our model and prior models is that,
rather than linking creative activities together through cyclical feedback
loops, as shown in Fig.1, we connect different sets of activities with dou-
ble- or single-headed arrows. This allows for a dynamic model that can ac-
count for both models that are depicted in Fig.1. On one hand, individuals
can begin the dynamic process by defining the problem and then moving
through an iterative process of gathering information, evaluating ideas
against criteria, and elaborating ideas until a solution is chosen—as de-
picted by the problem-first model in Fig.1 (Amabile, 1983; Amabile & Pratt,
2016; Mumford etal., 1991). On the other hand, individuals can begin the
dynamic process by generating new ideas and then exploring them in the
context of different problem domains until a problem and solution emerge
together—as depicted by the idea-first model in Fig.1 (Finke etal., 1992).
Our model also allows for alternative paths that involve a dynamic
iteration between the problem-first and idea-first models. We believe that
such flexibility is important to capture a broad range of creative processes
that might occur in real organizational settings. For example, problem
solvers may begin the creative process by trying to develop a solution
to a well-defined problem, thereby following the problem-first model.
But they may confront an obstacle that forces them to pivot their efforts
to pursue a new problem (e.g., Baker & Nelson, 2005), at which point
they may need to transition from a problem-first model to an idea-first
model. Once they discover a new problem to solve, they can transition
back to a problem-first model. Although there are many possible paths
that individuals can take to produce creative outcomes, the dynamic
problem-solving process begins either when people define a problem or
generate ideas, and it ends either when people choose a viable solution
to a problem or a solution emerges by discovering a problem that can be
solved with a previously created preinventive idea.
The degree to which constraints shape the problem-solving process is
primarily a function of the level of each type of constraint. Prior research
has focused on how levels of constraint influence creative outcomes, but it
presents a confusing picture. For instance, research has shown that people
can be more creative when they experience lower levels of resource con-
straint (e.g., Amabile etal., 1996; Hunter etal., 2007), higher levels of re-
source constraint (e.g., Hoegl, Gibbert, & Mazursky, 2008; Moreau & Dahl,
2005), lower levels of problem constraint (e.g., Getzels & Csikszentmihalyi,
1976; Unsworth, 2001), and higher levels of problem constraint (e.g., Finke,
1990; Ward, 1994). In an effort to bring more clarity to this picture, we de-
velop theory that explains how different confluences of constraint shape
the creative process. For the sake of simplicity, we theorize about very
high or very low levels of each type of constraint, while acknowledging
that each type of constraint can be depicted as a continuum (similar to
sources of constraint). By dichotomizing this dimension into extreme lev-
els of constraint, we outline the boundary conditions for which our theory
explains creative phenomena in organizations.
We also consider how the source of constraint can have a moderating
influence on the relationship between the levels of each type of constraint
and the dynamic problem-solving process. Many constraints are based
on concrete limitations to problem solving such as the amount of time,
finances, materials, or knowledge that are available for generating ideas.
However, the source of these constraints can have a strong influence on
the perceived level of constraint (e.g., Amabile & Gitomer, 1984; Byron etal.,
2010; Deci & Ryan, 1985). Thus we develop theory that also explains how
the source of constraint interacts with the levels of each type of constraint
to influence the creative process.
We summarize our theory in Fig.3, which shows how two particular
confluences of constraint can shape the activities that people engage in
during the dynamic problem-solving process, which yield two effective
modes of problem solving. When people perceive a low level of resource
constraint and a high level of problem constraint (depicted on the left side
of Fig.3), they feel like they are working on a well-defined problem and
have the flexibility to engage in a wide range of creative activities in the
pursuit of developing a solution to the problem. We call the mode of prob-
lem solving under this particular confluence of constraints deliberate prob-
lem solving, because it reflects the experience of deliberately generating
ideas with the intent of solving a well-defined problem (e.g., Amabile &
Pratt, 2016).
Alternatively, when people perceive a high level of resource constraint
and a low level of problem constraint (depicted on the right side of Fig.3),
they might feel limited when generating ideas, but they have the flexibil-
ity to explore their ideas across several problem domains as they try to
discover a problem that can be solved by one of their ideas. Sometimes, a
solution never emerges, and other times—as in the case of the Nintendo
Wii—a breakthrough solution emerges. Therefore we call this mode of
problem solving emergent problem solving, because it reflects the experi-
ence of generating an idea and then exploring that idea in the context of a
problem domain until a problem and solution emerge together (e.g., Finke
etal., 1992). Together, our model shows how two seemingly different cre-
ative processes—as shown in Fig.1—can transpire from the same under-
lying dynamic problem-solving process.
The Dynamic Problem-Solving Process as Shaped by Two
Confluences of Constraint
As defined earlier, resource constraints place limits on the time, fi-
nances, materials, and knowledge that people use when generating ideas
during the problem-solving process. Generating new ideas involves the
conceptual combination of existing ideas (Guilford, 1950; Koestler, 1964),
and consequently, increasing the amount of resources available (i.e., reduc-
ing the level of resource constraint) exponentially increases the number of
ideas that can be generated during problem solving. For example, when
participants in Finke’s (1990) problem-solving experiment were given a
subset of 3 out of 15 materials, they had the potential to create 455 unique
material combinations, which could then be used to develop thousands
of new conceptual ideas. If they were given just one additional material
Low perceived level of resource constraint
*Define problem
Evaluate ideas
Change problem
Generate ideas
Elaborate ideas
Incubate ideas
Gather information
Explore ideas
**Choose solution
Define problem
Evaluate ideas
Change problem
*Generate ideas
Elaborate ideas
Incubate ideas
Gather information
Explore ideas
**Solution emerges
High perceived level of resource constraint
High perceived level of problem constraint Low perceived level of problem constraint
* Activity occurs once to begin the creative process
** Activity occurs once to end the creative process
Emergent problem solving (P2)
Deliberate problem solving (P1)
External source increases perceived level of resource constraint (P3)
External source increases perceived level of problem constraint (P3)
Width of the enclosed area reflects the
relative level of each type of constraint.
FIG.3 Two types of problem solving as emergent from perceived levels of two types of
(for a total of four), they would have been able to create 1365 unique ma-
terial combinations or three times as many.
Therefore when people perceive low levels of resource constraint, they
have a high degree of flexibility when generating ideas, elaborating ideas,
and incubating ideas. For example, Edison and his colleagues had access
to a large number of financial and material resources, in addition to great
expertise, enabling them to generate and elaborate upon thousands of
ideas in their quest to develop a commercially viable light bulb. As the
level of resource constraint increases, flexibility decreases, and people
suffer from a reduced capacity to engage in these activities. At the most
extreme levels of resource constraint, they can only generate one initial
idea, which can make it difficult to solve problems, but not impossible.
For example, the STM engineers took a single new idea—the semicon-
ductor technology that detected three-dimensional motion—and explored
broadly for commercial applications across several industries.
Problem constraints, on the other hand, refer to the internal and exter-
nal demands that define the goal state. When there are low levels of prob-
lem constraint, problem statements are vague or poorly defined, meaning
that people have a high degree of flexibility when engaging in activities
such as defining problems, evaluating ideas against criteria, and changing
problems. For example, the STM engineers were not confined to using their
technology within a particular industry, and each time they explored their
technology in the context of a new industry, they defined new problems
or changed problems in their quest to discover a commercial application
of their technology. As the level of problem constraint increases, flexibility
decreases, and people become more restricted when defining or changing
problems. At the most extreme levels of problem constraint, problems are
clearly defined and highly rigid, meaning that the problem is defined once
during the problem-solving process and cannot be changed. For example,
Edison and his colleagues identified several criteria that needed to be met
in order to produce a commercially viable light bulb; once defined, the
problem did not change over the course of the entire project.
Levels of these two types of constraint work together to produce two
stylized versions of the dynamic problem-solving process. First, people
may perceive a low level of resource constraint and a high level of prob-
lem constraint, which creates conditions for deliberate problem solving to
unfold—as depicted on the left side of Fig.3. Under these conditions, the
problem-solving process begins when an individual defines a problem (or
is presented with a well-defined problem). It continues as the individual
engages in a wide range of activities that include gathering information,
generating ideas, elaborating ideas, incubating ideas, exploring ideas,
and evaluating ideas against criteria. The process ends when an idea that
fully satisfies all the problem criteria is selected as a viable solution. Note
that this process closely resembles the problem-first model of the creative
process (e.g., Amabile, 1983; Amabile & Pratt, 2016; Mumford etal., 1991;
Wallas, 1926). Therefore we propose the following:
Proposition 1: When people perceive a low level of resource constraint in combina-
tion with a high level of problem constraint, they are likely to engage in a deliberate
problem-solving process that resembles the problem-first model: The problem will be
defined once at the beginning of the process, and then people will engage in a variety
of creative activities until they choose a solution to end the process.
By contrast, people may perceive a high level of resource constraint and
a low level of problem constraint, which creates conditions for emergent
problem solving to unfold—as depicted on the right side of Fig.3. Under
these conditions, problem solving begins when an individual generates an
idea, which is followed by a series of activities such as exploring ideas in
the context of numerous problem domains, defining problems that could
be solved by that idea, and elaborating on the original idea until a solu-
tion emerges to a discovered problem. Note that this process resembles
the idea-first model of the creative process (e.g., Finke etal., 1992), but
our synthesized model also includes a broader range of activities. Many
of these activities—such as gathering information, evaluating ideas, and
changing criteria—were actually discussed by Finke and his colleagues
in their original work, but were only implicitly included in the model.
Therefore our model provides a more complete, explicit version of activi-
ties that can take place during emergent problem solving.
One notable change that we make to the idea-first model is that we also
include incubating ideas, which Finke and colleagues specifically rejected.
They developed their model under the assumption that creative thinking
is a purposeful cognitive activity rather than a passive, unconscious one.
While we agree that much creativity occurs through purposeful cognitive
thought, recent research has provided physiological evidence that people
can indeed produce creative breakthroughs that arise into consciousness
seemingly out of nowhere (Kounios & Beeman, 2015). Therefore we in-
clude idea incubation to complement the purposeful creative activity that
Finke and colleagues described. Therefore we propose the following:
Proposition 2: When people perceive a high level of resource constraint in combi-
nation with a low level of problem constraint, they are likely to engage in an emergent
problem-solving process that resembles the idea-first model: An idea will be gener-
ated once to begin the process, and then people will engage in a variety of creative
activities until a solution emerges to end the process.
The source of constraint can also shape dynamic problem solving—
specifically by changing the individual’s perception of the level of con-
straint during the process (Deci & Ryan, 1985). Amabile and Gitomer
(1984) conducted an experiment to demonstrate this effect for resource
constraints. In their experiment, they asked subjects to create a collage
using various materials. In the first, “choice” condition, subjects were
presented with 10 closed boxes of materials and were told to choose 5
of the 10 boxes to use for the task. In the second, “no choice” condition,
the experimenter presented all 10 boxes and then chose 5 boxes for the
subjects to use. Results showed that subjects in the “choice” condition
produced more creative outcomes than subjects in the “no choice” con-
dition, despite using an identical set of materials and spending an equal
amount of time on the task. These results are consistent with a broad
range of research showing that externally controlled resource constraints
can decrease the feeling of autonomy, which in turn can inhibit creativity
by increasing the perceived level of constraint (Amabile etal., 1996; Deci
& Ryan, 1985; Hunter etal., 2007).
These effects can also be applied to problem constraints. Ryan and Deci
(2000) argue that any factor that reduces the feeling of having control over
one’s own behaviors can increase the perception of constraint. According
to problem-solving scholars (Newell & Simon, 1972), the problem defi-
nition is the primary driver of behavior during the creative process.
Therefore when people have control over the problem, they should also
feel like they have control over their behaviors during problem solving,
and thus perceive a lower level of constraint. When external forces control
the problem, people feel like they have less control over their behaviors,
and thus perceive a higher level of constraint. Altogether, we reason that
the extent to which resources or problems are external determines the
degree to which people perceive themselves to be constrained. Internal
constraints decrease the perceived level of constraint, whereas external
constraints increase the perceived level of constraint. We summarize these
possibilities in the following proposition:
Proposition 3: The source of constraint will moderate the perceived levels of re-
source or problem constraints, such that external constraints will be perceived as
more constraining than internal constraints.
Ineffective Problem Solving as Shaped by Two Other
Confluences of Constraint
The two forms of problem solving described in the previous section,
each arising from a different confluence of constraint, can be highly ef-
fective in leading to creative outcomes. However, two other confluences
of constraint are also possible, which result in ineffective modes of prob-
lem solving. First, when people perceive high levels of both resource and
problem constraints, they feel like they are working on a well-defined
and highly rigid problem, but they do not have the flexibility to generate,
elaborate, or incubate ideas as they try developing a solution to the prob-
lem. When working on a well-defined problem, people need flexibility to
generate a wide variety of ideas to increase their chances of developing a
viable solution. Without that flexibility, people are likely to feel that their
problem-solving efforts are futile, which will inhibit their capacity to pro-
duce viable solutions.
Second, when people perceive low levels of both resource and problem
constraints, they feel like they can develop a large number of potential
solutions to a large number of potential problems. While these conditions
may seem favorable because they provide people with a high level of flex-
ibility throughout the entire problem-solving process, research shows that
people can actually struggle to think creatively under these conditions
(Goldenberg, Mazursky, & Solomon, 1999; Moreau & Dahl, 2005; Ward,
1994). People may have little direction on which idea to pursue, resulting
in a feeling of ambiguity that increases levels of stress, anxiety, and frus-
tration (Schwartz, 2000). These negative feelings can reduce a person’s en-
gagement in the problem-solving process (Iyengar & Lepper, 2000), which
can subsequently inhibit creativity (Chua & Iyengar, 2006, 2008). In sum-
mary, then, our model describes the confluences of constraints that are
most likely to result in successful creative problem-solving efforts, and it
also points to confluences of constraints that are likely to undermine the
problem-solving process.
Creativity is a challenging endeavor in which people must navigate
through a complex confluence of constraints on their journey to discovery
and creation. Most research in organizational creativity has been conducted
under the assumption that problems are closed (or clearly defined at the
outset) (Unsworth, 2001), and that people can be creative when they have a
high degree of flexibility to develop a wide range of ideas during the creative
process. According to this view, the most important tools that organizations
have at their disposal to foster a more creative workforce are to give people
a well-defined problem and provide them with enough resources to solve it
creatively (Amabile etal., 1996; Anderson, Potocnik, & Zhou, 2014; Shalley,
Zhou, & Oldham, 2004). Within this paradigm, resources play an import-
ant role in facilitating the creative process, but perhaps the most important
step is defining the right problem to solve in the first place (Amabile, 1983;
Getzels & Csikszentmihalyi, 1976; Mumford etal., 1994).
However, these approaches overlook an alternative model of problem
solving that may be more appropriate when people are working on open,
ill-defined problems. Under such circumstances, rather than focusing on
adapting a vague, poorly defined problem into several well-defined subprob-
lems (Simon, 1973), it may be wiser to start the process by generating ideas in
the absence of a clear problem definition altogether (Finke etal., 1992).
We believe that the main contribution of this chapter is the synthesis of
these two previously disparate views on the creative process. We show
how they emerge from the same underlying dynamic problem-solving
model, but differ as a function of the confluence of constraints that people
perceive as operating on them during the process. Understanding these
dynamics can be important for organizational creativity scholars, because
creativity in the workplace is often subject to a shifting set of demands
from numerous stakeholders (Drazin, Glynn, & Kazanjian, 1999; Kanter,
1988), resulting in an ever-evolving set of constraints. Problems in real
organizations are also considerably more open than what prior theory on
organizational creativity has assumed (Unsworth, 2001), and our theory
provides guidance on how to better understand and explain creativity un-
der these challenging conditions.
Implications for Theory and Research
In this chapter, we developed an integrated model of dynamic problem
solving, which makes theoretical contributions to both prior models. We
contribute to literature citing the problem-first model by allowing for a
more dynamic sequence of activities during the creative process, which
helps account for a broader range of phenomena in organizations that the
problem-first model could not previously explain. Most importantly, it
could not explain creative processes that begin with generating ideas and
end when a solution emerges to a discovered problem (e.g., Finke etal.,
1992; von Hippel & von Krogh, 2016).
We also contribute to literature citing the idea-first model by expand-
ing the scope of activities that take place during the creative process and
providing a clearer structure to explain how various activities are affected
by constraints. For example, Finke and colleagues built their model on the
assumption that constraints play a fundamental role in shaping the gen-
eration and exploration of ideas, but they failed to recognize that different
types of constraints can have a stronger effect on different parts of the
process. Building on more recent research that studies creativity and con-
straint (e.g., Rosso, 2014; Ryan & Deci, 2000), we develop a more detailed
model that explains how different types of constraint can systematically
shape different parts of the creative process.
Our second contribution is that we develop theory that explains how
seemingly different models of the creative process are derived from the
same underlying problem-solving process. Previous researchers have de-
scribed two unique paths in developing creative outcomes—summarized
as the problem-first and idea-first models of the creative process—but for
the most part, literature citing each model has remained isolated from
each other. The majority of organizational creativity literature draws from
the problem-first model to frame research (e.g., Amabile & Pratt, 2016;
Anderson etal., 2014; Perry-Smith & Mannucci, 2017; Shalley & Zhou,
2008), overlooking the apparent contradiction with the idea-first model.
By contrast, literature drawing from the idea-first model addresses the
contradictory nature that constraints can have on creative outcomes
(e.g., Caniëls & Rietzschel, 2015; Chua & Iyengar, 2008; Hoegl etal., 2008;
Moreau & Dahl, 2005; Roskes, 2015), but has not explicitly addressed how
constraints might influence the creative process. We take a broad view
to show how various constraints work together to shape the problem-
solving process, thereby providing a more comprehensive picture of the
relationship between creative problem solving and constraint.
By integrating these two disparate views of the creative process into
a coherent model, we also raise several new questions that can be inves-
tigated in future research. Primarily, we describe two modes of problem
solving that are optimal under two confluences of extreme levels of con-
straint: deliberate problem solving (low resource constraint, high problem
constraint) and emergent problem solving (high resource constraint, low
problem constraint). Yet, in organizational settings, the process of creating
new products, ideas, processes, or services is highly dynamic, and the lev-
els of resource and problem constraints are constantly shifting over time.
While recent research has provided glimpses into how people can engage
in different creative processes to deal with different confluences of con-
straint (e.g., Frishammar, Dahlskog, Krumlinde, & Yazgan, 2016; Harrison
& Rouse, 2014; Harvey & Kou, 2013; Sonenshein, 2014), it is still unclear
how people might transition between deliberate and emergent modes of
problem solving over time. How might people impose or relax constraints
on themselves to facilitate the problem-solving process? How might they
react to unexpected shifts in their constraint environment? What role do
organizations and managers play in facilitating these dynamics? These re-
flect only a few of the numerous questions that can be addressed in future
Another possibility is to investigate the degree to which individuals
with different characteristics thrive under different confluences of con-
straint. For example, one of the earliest streams of creativity research
focused on explaining creative outcomes through individual traits and
abilities (Guilford, 1950; Koestler, 1964; Nicholls, 1972; Rothenberg &
Greenberg, 1976). One useful distinction that came from this research was
that people have different problem-solving styles. For example, Kirton
(1976) differentiated between adaptors, or people who excel at “doing
things better”; and innovators, or people who excel at “doing things differ-
ently.” Similarly, Jabri (1991) differentiated between logical problem solv-
ers, or people who prefer to solve problems in a logical sequence of steps,
and intuitive problem solvers, or people who prefer to solve problems by
making unexpected connections between ideas and embracing the uncer-
tainty associated with creative thinking.
In light of our theory, it may be possible that individuals with differ-
ent problem-solving styles may be better suited to different modes of
problem solving. For example, logical problem solvers (or adaptors) may
thrive when they are engaged in deliberate problem solving, but struggle
when they are engaged in emergent problem solving. Alternatively, intu-
itive problem solvers (or innovators) may thrive when they are engaged
in emergent problem solving, but struggle when they are engaged in de-
liberate problem solving. This reflects only one of many opportunities to
investigate how individual characteristics and traits may influence an in-
dividual’s ability to engage in different modes of problem solving.
Similar research can also be developed at the team level of analysis,
which may offer even more opportunities to develop novel insights given
the relative lack of research on team creativity compared to individual
creativity (Anderson etal., 2014; Shalley & Zhou, 2008). For instance, prior
research shows that teams can benefit from both broad search and local
search during creative problem solving (Perretti & Negro, 2006; Taylor
& Greve, 2006). In the context of our theory, it is possible that broad or
local search can apply to either searching for solutions or searching for
problems, and teams may be most successful when engaging in a com-
bined process of broad search for solutions and local search for problems
or vice versa. Rather than viewing search as a single continuum, different
combinations of search may be optimal based on different confluences of
constraint. Such distinctions have neither been theorized nor empirically
tested, providing rich opportunities for future research.
Managerial Implications
Organizations are complex environments in which problems are often ill
structured and open, and our theory offers insights that could help manag-
ers better understand how to manage the creative process under these dif-
ficult conditions. One dilemma that managers face is how to give workers
enough flexibility to solve problems creatively while also asserting enough
control so that workers produce solutions that are aligned with the organi-
zation’s goals. Perhaps the most powerful application of our model is that
it provides managers with a tool to understand how they can manipulate
constraints to strike this flexibility-control balance more effectively.
One approach is that managers can adopt a deliberate mode of problem
solving, in which they give people a well-defined problem that aligns with
the organization’s goals, but also give people enough resources so that they
can develop an optimal solution to the problem. The search for solutions
can be done within the organization’s boundaries, where workers draw
on the available skills and resources to solve the problems themselves (for
a review, see Shalley & Zhou, 2008), or it can be expanded beyond the or-
ganization’s boundaries, allowing people to gather and evaluate solutions
developed by a crowd of problem solvers (e.g., Lifshitz-Assaf, 2017). This
mode of problem solving can help managers simultaneously maintain
control over the outcomes of the problem-solving process while provid-
ing flexibility to workers so that they may determine the best way to solve
the problem.
Another approach would be to adopt an emergent mode of problem
solving, which might be particularly useful when organizations experi-
ence greater ambiguity in their mission and goals. For example, any orga-
nization that is trying to develop a breakthrough technology is operating
under conditions of high ambiguity (Kaplan & Tripsas, 2008), and startup
organizations are often trying to identify target market needs while simul-
taneously trying to develop the product (e.g., Navis & Glynn, 2010). In
these conditions, organizations can maintain control by restricting the set
of resources that they are committing to a new project, but also provide
flexibility by allowing workers to search broadly for problems that could
be solved with those resources. This approach could help organizations
shorten product development cycles (Ries, 2011) or foster the creative use
of resources (Sonenshein, 2014), which may be particularly valuable when
organizations are operating in highly dynamic markets that are changing
quickly (e.g., Brown & Eisenhardt, 1997; Eisenhardt & Tabrizi, 1995).
Creativity is an essential source of new ideas, products, processes, or
services that help organizations gain a competitive advantage against ri-
vals and flourish, but creativity is a challenging endeavor because people
must work within a complex confluence of constraints that are constantly
changing over time. Prior research has described two fundamentally dif-
ferent models of creativity, but each model provides contradicting views
on the creative process, raising questions about how people can success-
fully navigate through these complex environments to achieve highly
creative outcomes. This chapter reconciles these contradictions by synthe-
sizing these models into a coherent model, revealing that different modes
of problem solving emerge from different confluences of constraints. In so
doing, we advance a new integrated model of dynamic problem solving
within organizational constraints, which offers guidance to both scholars
and practitioners who are interested in understanding and facilitating cre-
ativity in organizational settings.
This work was supported by the Harvard Business School Division of Research and the
Social Sciences and Humanities Research Council of Canada [430-2017-00527]. An earlier
version of this chapter was presented at the Academy of Management meeting in Atlanta,
GA. We also gratefully acknowledge the help of several colleagues for their insightful
comments and feedback in preparing this manuscript, including Matthew Cronin, Spencer
Harrison, Jennifer Mueller, and Ryan Raffaelli.
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... These examples illustrate how extreme levels of constraint can promote different types of creative problem solving (Cromwell et al., 2018;Unsworth, 2001). For example, individuals in the DARPA example were working on a highly constrained problem that provided clear goals to achieve (Byron & Khazanchi, 2012;Hunter et al., 2007;Shalley, 1991), whereas individuals in the archaeology example were working an open-ended problem that allowed them to develop much more diverse interpretations of their goal (Dillon, 1982;Getzels & Csikszentmihalyi, 1976;Unsworth, 2001). ...
... To answer this question, this paper begins with the assumption that all creativity comes from a cognitive problem-solving process (Cronin & Loewenstein, 2018;Lubart, 2001;Mumford et al., 1991;Newell & Simon, 1972), which can be affected by external dimensions of a task such as constraint (Acar et al., 2019;Amabile & Pratt, 2016;Cromwell et al., 2018;Unsworth, 2001). The theoretical framework to emerge from this analysis suggests that the underlying problem space for creativity is systematically shaped by two dimensions: (1) constraint on the problem, which ranges from open to closed and (2) constraint on resources, which ranges from limited to abundant. ...
... When exploring how multiple dimensions of constraint affect creativity, it is possible for individuals to become more creative when they experience a high level of constraint on one dimension coupled with a low level of constraint on the other, producing an overall balanced combination that enhances creativity. However, the particular combination of high versus low constraints on each dimension can dramatically change the experience of problem solving, leading to fundamentally different types of creativity that affect both processes and performance (e.g., Cromwell et al., 2018;Unsworth, 2001). They can also struggle to be creative under a combined high or low level of constraint on a task. ...
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Research suggests that extreme levels of constraint can push people to use different types of creative problem solving, but this conflicts with recent theory arguing that individuals are most creative under a moderate level of constraint. To resolve this issue, this paper proposes a combinatorial theory of constraints that argues it is necessary to understand how multiple dimensions of constraint (e.g., on problems and resources) work together to influence creativity, rather than study them in isolation. Accordingly, two conditions can enhance creativity—either through divergent problem solving or emergent problem solving—because they produce an overall balanced combination of constraint that improves important psychological mechanisms of creativity such as intrinsic motivation and creative search. Alternatively, two other conditions can hinder creativity—either due to ambiguous opportunity or futile effort—because they produce a combined low or high level of constraint on a task.
... This theory provides a valuable perspective to understand the dynamics of team learning; however, when applied to innovation teams, it faces limitations. Innovation often requires teams to engage in activities that have conflicting short-term goals, such as exploration and exploitation (March, 1991;Edmondson, 2002), and longer-term goals often change during a project as new possibilities emerge (Cromwell, Amabile, and Harvey, 2018). Therefore, teams pursuing innovation must be adept at coordinating different learning activities over time, but existing theory provides little guidance to understand how learning activities should occur within and across episodes. ...
... Although vicarious learning can help teams identify additional insights they did not consider before , these insights are likely to focus on improving the efficiency and predictability of existing task strategies (Bresman, 2010), limiting the search for more-creative strategies to accomplish goals. As a result, teams are unlikely to discover unexpected breakthrough solutions that can vastly improve project performance (Amabile and Pratt, 2016;Edmondson and Harvey, 2017;Cromwell, Amabile, and Harvey, 2018). Therefore, vicarious learning does not introduce as much tension to an innovation project, compared to experimental or contextual learning, which can weaken the positive rhythm of team learning that is important for overall performance. ...
... The last assignment was a ''final presentation,'' which was a recorded 15-minute PowerPoint presentation describing both the problem and solution. We collected survey data at the time of submission for each assignment, creating well-defined teamwork episodes with clear short-term goals that are consistent with the innovation process (Anderson, Potočnik, and Zhou, 2014;Perry-Smith and Mannucci, 2017;Cromwell, Amabile, and Harvey, 2018). ...
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Innovation teams must navigate inherent tensions between different learning activities to produce high levels of performance. Yet, we know little about how teams combine these activities—notably reflexive, experimental, vicarious, and contextual learning—most effectively over time. In this article, we integrate research on teamwork episodes with insights from music theory to develop a new theoretical perspective on team dynamics, which explains how team activities can produce harmony, dissonance, or rhythm in teamwork arrangements that lead to either positive or negative effects on overall performance. We first tested our theory in a field study using longitudinal data from 102 innovation teams at a Fortune Global 500 company; then, we replicated and elaborated our theory in a study of 61 MBA project teams at an elite North American university. Results show that some learning activities can occur within the same teamwork episode to have harmonious positive effects on team performance, while other activities combine to have dissonant negative effects when occurring in the same episode. We argue that dissonant activities must be spread across teamwork episodes to help teams achieve a positive rhythm of team learning over time. Our findings contribute to theory on team dynamics, team learning, and ambidexterity.
... Specifically, exaptations can occur following a creative search process, in which a user-innovator begins with a product he wishes to hack and then seeks other purposes for which that product can be modified to serve. This dynamic is what we call a product-first search process (Cromwell et al. 2018). Exaptations can also occur via problem identification and formulation that occur before the search for a product, which could be modified to address the problem at hand; here, we have the classic problem-first search process (Erat and Krishnan 2012, Posen et al. 2018, Sommer et al. 2020. ...
... Context of User Innovation Scholars have noted many cases of exaptation in the history of technology innovation and markets (Mokyr 2000, Dew et al. 2004. For example, STMicroelectronics developed the technological basis for threedimensional movement sensors in the late 1970s, with their intended applications in the areas of computers, home appliances, and automobiles (Cromwell et al. 2018). Yet, the breakthrough use for this technology did not arrive until 2006, when Nintendo integrated it into the Wii gaming console-delivering a novel gaming experience and transforming the gaming industry (Verganti 2009). ...
... We can visualize the difference between these two approaches through the need versus solution space analogy of von Hippel and von Krogh (2016). A product that initiates problem-solving involves putting initial constraints on the product with which the solver is working, but allows for greater flexibility in terms of searching for a need that the product might satisfy (Cromwell et al. 2018). Put differently, the user-innovator is clear on what product to hack, but is figuring out what to hack it into. ...
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Exaptation refers to the emergence of novel functionalities in existing products. Exaptations frequently arise in the context of users who creatively modify (or hack) existing products to accommodate new needs. Here, we examine how product-first (compared with problem-first) search affects the occurrence of exaptations. In a product-first search, the user identifies the product to be hacked before seeking a viable need. In a problem-first search, the user has a defined problem before seeking a viable solution. We argue that users are more likely to achieve exaptations following a problem-first (compared with a product-first) search. Indeed, with problem-first search, they are less likely to face functional fixedness, and they can leverage their greater awareness of problems that may not have readily adaptable solutions, which leads them to generate exaptations. Using a novel data set comprising user hacks of IKEA products, we present evidence that hacks originating from a product-first search are less likely to generate exaptations than hacks originating from a problem-first search. We also show that this difference is mitigated when the user has hacking experience or when the IKEA product being hacked is more modular. We also explore how the mitigation happens. Increased hacking experience appears to reduce functional fixedness; meanwhile, increased product modularity increases the likelihood that users will make serendipitous discoveries leading to exaptations. We contribute to the growing literature on exaptation as a source of novelty and discuss the implications of this phenomenon for managing user innovation. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation.
... This model is called the "Geneplore" model of creativity because it includes a combination of generating (gene-) and exploring (− plore) ideas, which helps people discover new problems to solve as they engage in problem solving. People can iterate between these activities multiple times, but the process typically begins with generating an idea and it ends with the discovery of a problem (Cromwell, Amabile, & Harvey, 2018). ...
... Establishing such effects can also have the potential of making broader and more general contributions to literature, because it can help scholars better understand how different cognitive processes used during problem solving might translate to longer-term productivity and performance of individuals. Since the earliest research on creativity (e. g., Duncker, 1945;Guilford, 1967;Wallas, 1926), scholars have generally agreed that cognitive processes for problem solving lie at the foundation of human creativity (Amabile, 1983;Cromwell et al., 2018;Finke et al., 1992;Lubart, 2001;Mumford et al., 1991;Newell & Simon, 1972). Building on this foundation, scholars have developed models to understand how the external social environment interacts with these cognitive processes to influence performance on various activities related to creativity such as idea generation, evaluation, and implementation (e.g., Amabile et al., 1996;Hunter et al., 2007;Shalley et al., 2004). ...
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Intrinsic motivation is widely considered essential to creativity because it facilitates more divergent thinking during problem solving. However, we argue that intrinsic motivation has been theorized too heavily as a unitary construct, overlooking various internal factors of a task that can shape the baseline level of intrinsic motivation people have for working on the task. Drawing on theories of cognitive styles, we develop a new scale that measures individual preferences for three different creative thinking styles that we call divergent thinking, bri-coleurgent thinking, and emergent thinking. Through a multi-study approach consisting of exploratory factor analysis, confirmatory factor analysis, and convergent validity, we provide psychometric evidence showing that people can have distinct preferences for each cognitive process when generating ideas. Furthermore, when validating this scale through an experiment, we find that each style becomes more dominant in predicting overall enjoyment, engagement, and creativity based on different underlying structures of a task. Therefore, this paper makes both theoretical and empirical contributions to literature by unpacking intrinsic motivation, showing how the alignment between different creative thinking styles and task can be essential to predicting intrinsic motivation , thus reversing the direction of causality between the motivational and cognitive components of creativity typically assumed in literature.
... Similarly, strong regulations limit the realm in which creative work is possible, often limiting motivation to engage and actual opportunities to find new ways (Acar et al., 2019). However, constraints and limitations can also foster creative endeavors under certain conditions, as they create the need for creative solutions and limit the realm of possibilities, which is often too overwhelming to engage in (Cromwell, Amabile, & Harvey, 2018). The constraints aspect is thus 48 2.4. ...
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Creativity – developing something new and useful – is a constant challenge in the working world. Work processes, services, or products must be sensibly adapted to changing times. To be able to analyze and, if necessary, adapt creativity in work processes, a precise understanding of these creative activities is necessary. Process modeling techniques are often used to capture business processes, represent them graphically and analyze them for adaptation possibilities. This has been very limited for creative work. An accurate understanding of creative work is subject to the challenge that, on the one hand, it is usually very complex and iterative. On the other hand, it is at least partially unpredictable as new things emerge. How can the complexity of creative business processes be adequately addressed and simultaneously manageable? This dissertation attempts to answer this question by first developing a precise process understanding of creative work. In an interdisciplinary approach, the literature on the process description of creativity-intensive work is analyzed from the perspective of psychology, organizational studies, and business informatics. In addition, a digital ethnographic study in the context of software development is used to analyze creative work. A model is developed based on which four elementary process components can be analyzed: Intention of the creative activity, Creation to develop the new, Evaluation to assess its meaningfulness, and Planning of the activities arising in the process – in short, the ICEP model. These four process elements are then translated into the Knockledge Modeling Description Language (KMDL), which was developed to capture and represent knowledge-intensive business processes. The modeling extension based on the ICEP model enables creative business processes to be identified and specified without the need for extensive modeling of all process details. The modeling extension proposed here was developed using ethnographic data and then applied to other organizational process contexts. The modeling method was applied to other business contexts and evaluated by external parties as part of two expert studies. The developed ICEP model provides an analytical framework for complex creative work processes. It can be comprehensively integrated into process models by transforming it into a modeling method, thus expanding the understanding of existing creative work in as-is process analyses.
... The understanding of the goals desired by the change vis-à-vis one's behavior have been shown to determine such experimentation and adaptation during strategic change (Ng & Lucianetti, 2016;Rafferty & Minbashian, 2019). As emphasized by Woodman and colleagues (1993), this is like creative problem solving, for which the effect of goal ambiguity is also striking (Cromwell et al., 2018;Zhou & Shalley, 2003). Without clear goals, one's attention is set in no meaningful direction and, as a result, one tends to experiment less with new ideas (Amabile, 1998;Thomke, 2019). ...
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Strategic change in organizations prompts pervasive ambiguity. As change initiatives cascade down the hierarchy, they can be met with habitual, inertial responses that ultimately generate negatively charged emotions—or they can prompt novel, experimental behaviors that forestall them. What remains unclear, however, is which factors drive teams, and the leaders that guide them, toward or away from this negative emotional reaction to change. In this study, we integrate social cognitive theory and research on mindfulness to unpack collective responses to change through a field study on 88 teams in a mortgage industry firm undergoing strategic change. We theorize that, when faced with ambiguous goals, team leaders low on mindful attention will lack the necessary cognitive capabilities to enact experimental behaviors—as they neither have clear external goals from senior managers nor internal dispositions to drive their attention into noticing novel information and eliciting unscripted experimental responses. In contrast, the experimental behaviors of team leaders who are high on mindful attention will not be affected by ambiguous goals—and the experimental behaviors of team leaders, in turn, will prompt greater experimental behaviors within their team, thereby lowering the team’s negative emotional reaction to change. Finding support for these hypotheses, our study contributes to research on dynamic managerial capabilities, collective responses to organizational change, and mindfulness.
A new playbook can help leaders determine which changes will endure and when to invest more in opportunistic transformation
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Americans now live in a time and a place in which freedom and autonomy are valued above all else and in which expanded opportunities for self-determination are regarded as a sign of the psychological well-being of individuals and the moral well-being of the culture. This article argues that freedom, autonomy, and self-determination can become excessive, and that when that happens, freedom can be experienced as a kind of tyranny. The article further argues that unduly influenced by the ideology of economics and rational-choice theory, modern American society has created an excess of freedom, with resulting increases in people's dissatisfaction with their lives and in clinical depression. One significant task for a future psychology of optimal functioning is to deemphasize individual freedom and to determine which cultural constraints are necessary for people to live meaningful and satisfying lives.
The subject of creativity has been neglected by psychologists. The immediate problem has two aspects. (1) How can we discover creative promise in our children and our youth, (2) How can we promote the development of creative personalities. Creative talent cannot be accounted for adequately in terms of I.Q. A new way of thinking about creativity and creative productivity is seen in the factorial conceptions of personality. By application of factor analysis a fruitful exploratory approach can be made. Carefully constructed hypotheses concerning primary abilities will lead to the use of novel types of tests. New factors will be discovered that will provide us with means to select individuals with creative personalities. The properties of primary abilities should be studied to improve educational methods and further their utilization. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Leveraging insights gained through a burgeoning research literature over the past 28 years, this paper presents a significant revision of the model of creativity and innovation in organizations published in Research in Organizational Behavior in 1988. This update focuses primarily on the individual-level psychological processes implicated in creativity that have been illuminated by recent research, and highlights organizational work environment influences on those processes. We revisit basic assumptions underlying the 1988 model, modify certain components and causal connections, and introduce four new constructs into the model: (1) a sense of progress in creative idea development; (2) the meaningfulness of the work to those carrying it out; (3) affect; and (4) synergistic extrinsic motivation. Throughout, we propose ways in which the components underlying individual and team creativity can both influence and be influenced by organizational factors crucial to innovation.
Problem-solving research and formal problem-solving practice begin with the assumption that a problem has been identified or formulated for solving. The problem-solving process then involves a search for a satisfactory or optimal solution to that problem. In contrast, we propose that, in informal problem solving, a need and a solution are often discovered together and tested for viability as a "need-solution pair.'' For example, one may serendipitously discover a new solution and assess it to be worth adopting although the "problem" it would address had not previously been in mind as an object of search or even awareness. In such a case, problem identification and formulation, if done at all, come only after the discovery of the need-solution pair. We propose the identification of need-solution pairs as an approach to problem solving in which problem formulation is not required. We argue that discovery of viable need-solution pairs without problem formulation may have advantages over problem-initiated problem-solving methods under some conditions. First, it removes the often considerable costs associated with problem formulation. Second, it eliminates the constraints on possible solutions that any problem formulation will inevitably apply.
A firm's ability to develop radical innovation is heavily contingent on the front-end phases where ideas and concepts are created, yet few empirical studies provide detailed insights into radical idea and concept development. Using literature on problem finding and problem solving, we explore how radically new ideas and concepts emerge, and outline the process by which they are created. Based on multiple case studies of five completed and two ongoing projects conducted by a highly innovative consultancy firm, Prime Group, the article proposes a six-step process for radical idea and concept development. The insights provide theoretical implications and advice for how firms can increase novelty and success rates of emergent radical ideas and concepts.