Traditionally, creativity was equated with the generation of multiple ideas for solving problems. Thus, creativity was equated with divergent thinking or the generation of multiple ideas (Guilford, 1950; Runco & Acar, 2012). As Welling (2007) has pointed out, however, creative performance, ultimately, involves problem-solving. More specifically, creative thought is held to involve the generation of high quality, original, and elegant solutions (Christiaans, 2002; O’Quin & Besemer, 1989) to complex, novel, ill-defined, or poorly structured, problems (Mumford &
Gustafson, 2012). Of course, peoples’ willingness, and ability, to generate high quality, original, and elegant solutions to novel, complex, ill-defined problems is influenced by many variables including personality (Feist, 2006), motivation (DeDreu, Baas, &
Nijstad, 2012), and intelligence (Vincent, Decker, & Mumford, 2002) among other variables. Nonetheless, those scholars who study creative problem-solving typically stress the importance of three key forces shaping creative thought: (1) expertise, (2) processing operations, and (3) strategies for effective process execution.
Expertise, viable and well organized procedural and declarative knowledge structures, appears critical for formulating viable creative problem solutions (Ericsson
& Charness, 1994; Finke, Ward, & Smith, 1992). One illustration of this point may be found in the Wright brothers’ development of powered flight. Here, knowledge gained in working with bicycles proved critical in the development of controlled flight (Crouch, 1992). More recent work by Weisberg (2011) has shown how Frank Lloyd Wright’s development of Fallingwater was, in part, based on his knowledge of, and willingness to, manipulate architectural design constraints. Perhaps the most compelling demonstration of the role of knowledge, and expertise, in creative problem solving has been provided in an experimental study conducted by Hunter, Bedell-Avers, Hunsicker, Mumford, and Ligon (2008).
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In this study, some two hundred undergraduates were asked to solve a complex social innovation problem. Problem solutions were evaluated for quality, originality, and elegance. A training manipulation was used to encourage application of schematic, case-based (or experiential), and associational knowledge. It was found that the highest quality, most original, and most elegant problem solutions were obtained when participants employed either schematic or case-based knowledge but not associational knowledge. Other research by Dailey and Mumford (2006) and Vincent et al. (2002) has shown that the expertise arising from schematic and case-based knowledge is strongly related to creative problem-solving and effective, real-world performance in managing critical incidents.
Of course, if one had only extant expertise to work with, creative problem-solving would be impossible. Indeed, this observation led Mumford and Gustafson (1988) to argue that exceptionally high levels of expertise may at times inhibit creative problem-solving. As a result, students of creativity have sought to identify the processes which allow people to work with expertise in generating creative problem solutions. Over the years, a number of models of the key processes involved in creative-problem solving have been proposed (e.g., Finke, Ward, & Smith, 1992;
Dewey, 1910; Parnes & Noller, 1972; Sternberg, 1986; Wallas, 1926). Mumford, Mobley, Reiter-Palmon, Uhlman, and Doares (1991) in a review of these models proposed a general model of creative processes which holds that eight critical processes, processes of varying significance depending on the domain in which the individual is working (Mumford, Antes, Caughron, Connelly, & Beeler, 2010), are involved in most creative problem-solving efforts: (1) problem definition, (2) information gathering, (3) concept selection, (4) conceptual combination, (5) idea generation, (6) idea evaluation, (7) implementation planning, and (8) solution monitoring.
In recent years, a variety of evidence has been acquired which has led Mumford et al.’s (1991) model to be accepted as the best available model of creative thinking
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processes (Lubart, 2003; Puccio & Cabra, 2009). For example, Baughman and Mumford (1995) and Mobley, Doares, and Mumford (1992) have provided evidence for the importance of conceptual combination and reorganization to creative thought.
Other studies by Reiter-Palmon, Mumford, O’Connor-Boess, and Runco (1997), Mumford, Baughman, Supinski, and Maher (1996), Lonengan, Scott, and Mumford (2004), and Osburn and Mumford (2006) have provided evidence for the impact of other processes such as problem definition, information gathering, idea evaluation, and implementation planning. Moreover, Mumford, Supinski, Baughman, Costanza, and Threlfall (1997) have shown each of these processes makes a unique contribution to the prediction of performance on multiple, creative problem-solving tasks, yielding multiple correlations in the .50s or .60s. Still other work by Friedrich and Mumford (2009) has shown errors in the execution of any one process flow through execution of later processes––a phenomena that would be expected when the products of earlier processing activities are used as “raw material” in later processing activities.
Not only do we now have a good understanding of the critical cognitive processing activities underlying creative thought we have also begun to develop a sound understanding of the strategies contributing to effective process execution. One set of studies has sought to identify the strategies contributing to effective execution of any given process. Thus, Mumford, Baughman, Supinski, and Maher (1996) found that a search for key facts accompanied by a search for anomalies contributed to creative problem-solving through their effects on information gathering. Mumford, Baughman, Threlfall, Supinski, and Costanza (1996) found that problem construction improved when people sought to define problems with respect to processes rather than goals.
Mumford, Supinski, Threlfall, and Baughman (1996) found that concept selection improved when people sought broad, flexible concepts. Baughman and Mumford (1995) found that systematic feature search and mapping, sometimes search and mapping based on metaphors, contributed to conceptual combination. Moreover, their findings indicate that elaboration, extensive elaboration, of new concepts emerging
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from conceptual combination is important in viable conceptual combination efforts. For idea generation, extended generation with appraisal of the viability of ideas accompanied by a search for linkages among ideas appears important (Ward, Smith, &
Finke, 1999). Idea evaluation appears to improve when people seek to compensate for deficiencies in ideas (Lonergan, Scott, & Mumford, 2004). Thus, they may seek to improve the quality of highly original ideas or seek to improve the originality of high quality ideas. Finally, Osburn and Mumford (2006) have shown that penetration, getting to key issues, and forecasting the downstream effects of actions, contribute to implementation planning.
Although we have begun to develop a good understanding of the strategies contributing to effective execution of each of those processing activities, an important caveat noted by Scott, Lonergan, and Mumford (2005) should be considered. In this study, they focused on conceptual combination with students being asked to provide solutions to a social innovation problem––a problem calling for the development of a new curriculum. Curriculum proposals were appraised for quality, originality, and elegance. Notably, however, manipulations were made in both the type of knowledge to be used in creative problem-solving, either schematic or case-based, and, within the type of knowledge presented, manipulations were made in the strategies prompted for working with this knowledge. Thus, when schematic knowledge was presented, participants were asked to employ conceptual search and mapping strategies. When, however, cases were presented, participants were asked to identify critical case outcomes, reconfigure actions and actors, and forecast downstream consequences.
Notably, both sets of strategies contributed to conceptual combination and creative problem-solving. However, different types of knowledge, or expertise, required application of different strategies.
Not only has recent research begun to identify the strategies contributing to effective execution of specific creative thinking processes, efforts have begun to identify the strategies that appear of value across a number of processes. Thus,
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divergent thinking (Merrifield, Guilford, Christensen, & Frick, 1962) and convergent thinking (Cropley, 2006) seem to have value in execution of each of these processes.
The point, however, remains that the value of convergent and divergent thinking is tied to the demands made by process execution.
In addition to divergent and convergent thinking, at least three other cross-process strategies have been identified that appear to contribute to creative thought. First, causal analysis skills, an element of critical thinking skills, appear critical to creative problem-solving. Thus, Marcy and Mumford (2007, 2010) trained people in various causal analysis strategies––for example, working with cases that have large effects or working with cases that have synergistic effects. It was found that people exposed to this training produce higher quality and more original solutions to social innovation problems. Second, Byrne, Shipman, and Mumford (2010) and Shipman, Byrne, and Mumford (2010) examined the impact of forecasting on creative problem-solving. They assessed people’s forecasting strategies as they worked on a creative problem and then appraised the quality, originality, and elegance of the resulting solutions. They found that more extensive forecasting and forecasting over longer timeframes contributed to creative problem-solving. Third, Stokes (2001, 2005) has examined variability and variability with respect to identified constraints. Her findings indicate that those who vary processing activities, and presumably processing strategies, when constraints are involved in creative problems typically produce creative problem solutions of greater quality and originality.
Of course, additional cross-process strategies might be identified with further research. Moreover, there is a need for research examining how cross-process strategies interact with process-specific strategies and/or knowledge type in shaping people’s performance on creative problem-solving tasks. Nonetheless, at this juncture, it seems clear that cross-process strategies, divergent thinking, convergent thinking, causal analysis, forecasting, and constraint variability do exist. Moreover, these cross-process strategies may be as important to creative problem-solving as the
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strategies used to execute a particular process (e.g., conceptual combination, idea evaluation).