Creative Thinking Processes: Managing Innovative Efforts
Abstract and Keywords
Creative thinking is the basis for innovation in firms. And the need for strategy-relevant innovations has generated a new concern with how people go about solving the kinds of problems that call for creative thought. Although many variables influence people’s ability to provide creative problem solutions, it is assumed the ways in which people work with or process knowledge provides the basis for successful creative problem-solving efforts. Additionally, there has been evidence bearing on the processing activities that contribute to creative problem solving. It is noted that at least eight distinct processing activities are involved in most incidents of creative problem solving: (1) problem definition, (2) information gathering, (3) concept selection, (4) conceptual combination, (5) idea generation, (6) idea evaluation, (7) implementation planning, and (8) adaptive monitoring. There are strategies people employ in effective execution of each of these processes, along with contextual variables that contribute to, or inhibit, effective process execution. Subsequently, there are key variables that operate in the workplace that contribute to, or inhibit, effective execution of these processing operations. These observations, of course, lead to implications for management of innovative efforts in firms.
Few would dispute the impact of innovations in products and services on day-to-day life in our world. Many of us live on our cell phones, a technology based on innovations in solid-state circuitry (Gertner, 2013) and design (Isaacson, 2012). We travel in cars and on airplanes, both technologies arising from the development of internal combustion engines (Ganesan, 2012). Many of the products we use on a day-to-day basis arrive from factories located around the world—products that arrive in shipping containers, yet another innovation (Donovan & Bonney, 2006). Not only is our world shaped by such technical innovations, innovations in the way work is done from goal setting to standard operating procedures shape how firms seek to manage the production process (Kanigel, 2005).
Traditionally, innovation was not seen as a central goal of firms. Rather, innovations were viewed as something firms exploit (Mumford, Scott, Gaddis, & Strange, 2002). More recent work, however, indicates the long-term survival of firms, and their financial success ultimately depends on the firms’ capability for sustained innovation (Cefis & Marsili, 2005). At times, these innovations may call for the development and fielding of fundamentally new technologies or new services. At other times, however, chains of smaller innovations in firms, products, and services may prove to be the key to business success (Gordon, 2016).
Of course, many factors shape the development and success of attempts to develop and field viable new products and services—considerations that range from technological readiness (Wise, 1992) to the cost of the new product or service to customers (Rodgers & Adhikarya, 1979). However, ultimately a firm’s ability to develop and deploy viable new products and services depends on the ability of workers, all workers, to conceive of viable new products and services. The formulation of new products and services, however, is held to depend on peoples’ ability to think creatively. Put more specifically, innovation, the fielding of new products and services, is held to require that someone, an individual or a team, must be able to produce an original, high-quality, elegant solution (Besemer & O’Quin, 1998; Christiaans, 2002) to complex, novel, ill-defined, or poorly structured problems (Mumford & Gustafson, 2007). In this context, originality refers to the novelty, unexpectedness, and cleverness of a creative problem solution, whereas high quality is marked by complete, coherent, useful solutions. Elegance refers to the refinement and flow of the solution (Dailey & Mumford, 2006; Scott, Lonergan, & Mumford, 2005; Vessey, Barrett, & Mumford, 2011).
Of course, many variables influence the success of people’s creative problem-solving efforts. Creative problem solving requires a substantial investment of resources, and so, motivational variables such as need for cognition (Watts, Steele, & Song, 2017) and creative self-efficacy (Tierney & Farmer, 2002) have been found to contribute to creative problem solving. People’s willingness to engage in novel ill-defined tasks is also of some importance resulting in variables such as curiosity (Hardy, Ness, & Mecca, 2017) and openness (Furnham, 1999) influencing the success of people’s creative problem solving. People’s perception of their environment (climate perceptions) has been found to condition their willingness to undertake creative tasks (Hunter, Bedell, & Mumford, 2007). The availability of requisite resources (Howell & Boies, 2004) and induction of requisite structure (Marta, Leritz, & Mumford, 2005) also contributes to the success of peoples’ creative problem-solving efforts.
Although many variables contribute to the success of peoples’ creative problem-solving efforts, as is the case in any other form of problem solving, people must work with knowledge to produce a creative problem solution (Mumford, McIntosh, & Mulhearn, 2018). If, however, people could rely solely on extant knowledge to solve creative problems, they would not be dealing with a novel, ill-defined problem—remember that it is novel, complex, ill-defined problems that call for creative thought. As a result, the ways in which people work with knowledge (their processing activities) have long been considered critical to understanding creative problem solving (Finke, Ward, & Smith, 1992; Guilford, 1950; Wallas, 1926; Parnes & Noller, 1972). In the present effort, we will examine what we know about the key cognitive processes contributing to creative problem solving and the contextual variables that influence effective execution of these processes—including contextual variables operating in firms pursuing creative work to allow fielding of innovative and new products and services.
As noted above, creative thinking processes reflect mental operations for working with extant knowledge to provide high-quality, original, and elegant solutions to novel, complex, ill-defined problems. Accordingly, any discussion of creative thinking processes must begin with an examination of the role of knowledge in creative problem solving. Knowledge is commonly held to be reflected in people’s experience given the well-validated proposition that experts possess deeper, richer, better-organized knowledge structures (Ericsson, 2009).
In fact, Vincent, Decker, and Mumford (2002) have provided rather compelling evidence that knowledge and expertise contribute to people’s performance in creative problem solving. In this study, 1,818 army officers ranging in grade from second lieutenant to full colonel, were asked to solve a novel, complex, ill-defined military problem calling for creative thought using a modified think-aloud protocol where probe questions were presented to elicit certain creative problem-solving processes. Judges appraised the effectiveness of process execution. Expertise was assessed by presenting officers with a set of some 70 leadership tasks. They were asked to categorize these tasks, and their mapping onto a standard model of military leadership was used to appraise expertise. As might be expected, colonels (the more experienced officers) evidenced far higher scores on the measure of expertise than lieutenants. More centrally, expertise was found to be strongly and positively related (r = .51) to effective execution of those creative thinking processes. In fact, this measure of expertise produced stronger relationships with effective process execution than a measure of intelligence.
Apparently, the greater knowledge posed by experts contributes to the effective execution of creative thinking processes. Knowledge, however, comes in many forms—systematic or conceptual, case-based or experiential, associational, spatial, auditory, etc. In discussions of complex problem solving, however, the three key types of knowledges people are commonly held to employ are conceptual, case-based, and associational knowledge (Shondrick, Dinh, & Lord, 2010). Conceptual (or schematic) knowledge is based on concepts and principles pulled from past experience (Phye, 1990), where concepts are organized into categories that have exemplars based on certain features or principles (Estes & Ward, 2002). Conversely, case-based (or episodic) knowledge is a type of knowledge stemming from past experiences that a person draws upon in order to act appropriately in related situations. Lastly, associational knowledge refers to linkages between stimuli and response event nodes (Estes, 1991), where the activation of one event node activates other related nodes (Boucher & Dienes, 2003).
Hunter, Bedell-Avers, and Mumford (2007) asked 247 undergraduates to provide solutions to a creative problem requiring people to formulate a plan for leading a new, experimental, secondary school. Problem solutions were reliably appraised by judges for quality, originality, and elegance. Prior to preparing their problem solutions, an instructional intervention was used to prompt the use of conceptual, case-based, or associative knowledge (or all combinations thereof) in problem solving. It was found that priming the use of conceptual or case-based knowledge resulted in the production of the most creative problem solutions. Associational knowledge proved of value only when associations were accompanied by concepts or cases. Thus, execution of creative thinking processes apparently depends on having expertise—expertise providing people with key concepts relevant to the problem and actual hands-on experience working with other relevant problems.
It is commonly held that conceptual and case-based knowledge are organized through people’s construction of mental models—models that illustrate key cause/outcome linkages of use in solving a certain class or type of problem drawn from a given domain (Goldvarg & Johnson-Laird, 2001). Indeed, experts typically are found to possess stronger, more accurate, and more elaborate mental models for understanding problems arising in a given domain (Andersen, Barker, & Chen, 2006). Mumford et al. (2012) examined how the availability of high-quality mental models for organizing knowledge contributed to the success of people’s creative problem-solving efforts.
In the Mumford et al. (2012) study, participants, some 450 in all, were asked to formulate a marketing plan for a new type of athletic footwear for extreme sports, or formulate a plan for leading a new experimental secondary school. In both cases, the resulting plans were appraised by judges for quality, originality, and elegance. Prior to starting work on these creative problem-solving tasks, participants were provided with instruction on how to illustrate their mental models for understanding various problems using structural equations framework. Participants were asked to illustrate their mental models for understanding either marketing or educational problems before starting work on the problem presented. The resulting model illustrations were appraised for various objective features (e.g., number of cause/goal linkages, number of mediations) and subjective features (e.g., coherence, novelty). It was found that people possessing better organized mental models for understanding problems arising in either of these domains provided creative problem solutions of greater quality, originality, and elegance.
Not only does having stronger mental models for understanding problems arising in a domain contribute to people’s creative problem solving, but how people work with the knowledge appears crucial. In a series of separate, unique investigations, Barrett et al. (2013), Hester et al. (2012), Robledo et al. (2012), and Peterson et al. (2013) provided participants with instructions for applying different approaches for working with the knowledge embodied in mental models. For example, when working with causes, think about causes that have significant effects, or think about causes that have direct effects. Again, judges appraised the quality, originality, and elegance of people’s solutions to problems calling for creative thought. It was found the ways in which people worked with available knowledge influenced their ability to provide high-quality, original, and elegant solutions to problems calling for creative thought.
Model of Processes
The importance of the ways people work with knowledge in creative problem solving points to the importance of peoples’ creative thinking processes. Recognition of this point has led many scholars to propose various models describing the key processes underlying incidents of creative thought (e.g., Dewey, 1910; Silverman, 1985; Sternberg, 1988; Wallas, 1926). Mumford et al. (1991) reviewed the available models of creative problem-solving processes to formulate a general model of the key creative thinking processes. This model was based on five key assumptions. First, creative problem solving, as noted above, depends on knowledge or expertise. Second, if only extant knowledge could be used to solve a problem, creative problem solving would not occur. Third, new knowledge arises from the combination and reorganization of extant knowledge. Fourth, successful combination and reorganization efforts give rise to new, emergent features, which provide a basis for generating original ideas. Fifth, progressive, proactive, evaluation and refinement of viable ideas will, in turn, give rise to creative problem solutions.
Based on these propositions, Mumford et al. (1991) argued that eight core processes are involved in most creative problem-solving efforts. It is held that creative problem solving begins with the definition of the problem. Once a problem has been defined, people will gather information related to the problem. With information gathering, relevant concepts, or cases, can be retrieved from available mental models. The concepts/cases people select to work with are then combined and reorganized, which allows new concepts to emerge. These new concepts then provide a basis for idea generation. The generated ideas are then evaluated and refined. People then plan how to implement this idea. And, subsequently, plan implementation is adaptatively monitored to permit exploitation of opportunities and management of restrictions emerging during idea implementation. Figure 1 provides an illustration of this eight-process model.
Implicit in the model presented in Figure 1 is a number of key assumptions. First, each process serves as an input to the subsequent processing activities. Second, each process involves multiple discrete mental operations—both generative and evaluative mental operations. Third, if the products resulting from process execution are held to be inadequate, people will generally cycle back to the immediately preceding process. Fourth, as implied above, process execution is held to be a conscious activity. Fifth, all processes must be “successfully” executed to result in production of a creative problem solution. It should be noted these assumptions imply creative processing is a resource intensive effort where success is not insured due to the number of interdependent processes that must be executed successfully.
Over the years, a sizeable body of research has been conducted that provides evidence for the relevance of each of these processes to creative problem solving. This section considers a few illustrative studies examining the impact of each of these processes on peoples’ performance in creative problem solving. It is of note, however, that virtually all of these processes have been investigated in multiple studies—studies employing different methods and different types of creative problem-solving tasks.
Reiter-Palmon, Mumford, O’Connor-Boes, and Runco (1997) asked participants to solve six complex, novel, ill-defined problems drawn from the leadership, social relations, and academic domains. Judges appraised the resulting problem solutions for quality and originality. Prior to starting work on these problems, participants were presented with a set of broad problem statements (e.g., there are mice in my basement). For each problem statement, they were asked to provide as many restatements or redefinitions of the problem they could think of. Judges rated problem restatements for quality and originality. It was found that those who could redefine problems with quality and originality produced creative problem solutions of higher quality and originality (r = .30). In a more recent study, Arreola and Reiter-Palmon (2016) assessed problem definition through restatements of problems in peoples’ own words. And, again, viable problem definitions were found to be positively related to the quality and originality of creative problem solutions, with problem definition accounting for creative performance above and beyond divergent thinking ability.
In a study of conceptual combination, Mumford, Baughman, Maher, Costanza, and Supinski (1997) asked participants to develop a marketing survey, a television advertisement, and a magazine advertisement for a new product—the 3D holographic television (Redmond, Mumford, & Teach, 1993). Written solutions to these three problems were appraised by judges for quality and originality. Prior to preparing these problem solutions, participants were presented with a set of conceptual combination problems drawn from Mobley, Doares, and Mumford (1992). These problems presented three categories, or concepts, as defined by four category exemplars (e.g., owls, ostriches, robins, sparrows). Participants were asked to combine these categories to create a new category. They were to label their new category, describe key features, emergent new features of this category, and provide exemplars of this new category. Judges appraised the quality and originality of the category label, features, and exemplars. The quality and originality of the advertising campaigns produced were found to be strongly positively related (r = .35) to the quality and originality of solutions to the advertising problems. Again, conceptual combination produced stronger relationships than a measure of divergent thinking.
Mumford et al. (1991) model of the creative thinking process holds that conceptual combination provides a basis for idea generation. Finke, Ward, and Smith (1992) made a similar argument. Mumford et al. (1998) asked 1,818 army officers, ranging in grade from second lieutenant to full colonel, to provide solutions to a novel, complex, ill-defined military problem-solving task. Prior to working on this task, however, they were also asked to complete the Guilford (1950) consequences measure in which they were to generate as many potential consequences of unlikely events (e.g., what would happen if gravity was cut in half?) as they could think of. The ideas generated were appraised by judges for attributes such as realism, time frame, and use of principles (e.g., concepts and concept features). It was found not only that these attributes of idea generation were positively related to the quality and originality of solutions to this military leadership problem (r = .45) but that use of principles in idea generation produced the strongest relationships with the quality and originality of solutions to military leadership problems.
Marcy and Mumford (2007) conducted a study examining the impact of concept selection in creative problem solving. In this study, it was assumed that creative problem solutions were most likely to emerge when people employed appropriate causal concepts for understanding the problem at hand. Accordingly, participants were asked to complete a set of self-paced instructional modules where they were provided with approaches for selecting the type of causal concepts that should be employed in creative problem solving (e.g., think about causes that have significant effects, think about causes that have direct effects, think about causes you can control). Participants were asked to solve six creative problems drawn from the business and educational domains where judges appraised problem solutions for quality, originality, and elegance. It was found that use of viable causes in concept selection resulted in production of creative problem solutions with greater quality, higher originality, and higher elegance.
In this model of creative processes, it is held that implementation planning is a key component of creative thought. Some support for this proposition has been provided by Marta, Leritz, and Mumford (2005). Teams were asked to work on a business “turn-around” task calling for creative thought. Written “turn-around” plans were appraised by judges for quality and originality. After working on this task, team members nominated their leader. Prior to starting work, all team members completed a measure of planning skills based on a series of business planning scenarios. It was found teams whose leaders evidenced strong planning skills produced “turn-around” plans of greater originality and better quality. Other work by Osburn and Mumford (2006), in a study of individual level creative problem solving, provides some support for this conclusion.
Of course, implementation planning depends on selecting an idea to be pursued. Gibson and Mumford (2013) examined how idea evaluation may contribute to creative problem solving. They asked undergraduates to formulate advertising campaigns for a new product. Judges appraised these advertising campaigns for quality, originality, and elegance. Prior to preparing these campaigns, however, participants were presented with a set of candidate ideas and asked to evaluate these ideas. Judges appraised these idea critiques with respect to number, depth, usefulness, range, complexity, isolation, risk sensitivity, operational relevance, and specificity. It was found that those who produced the most creative advertising campaigns were those who also produced a limited number of deep criticisms of potential ideas.
In yet another study of creative thinking processes, Mumford, Baughman, Supinski, and Maher (1996) examined the impact of information gathering on creative problem solving. In this study participants were asked to provide television advertisements, magazine advertisements, and a marketing survey for Redmond, Mumford, and Teach (1993) and its 3D holographic television task. These products of people’s creative-solving efforts were appraised by judges for quality and originality. Prior to starting work on this task, participants were asked to read through a set of “cards” bearing on the business management and the public policy problems. These “cards” presented different types of information—information bearing on key facts, anomalies, goals, restrictions, and diverse information. The time spent reading each card was recorded as a measure of the intensity of information gathering. And it was found the people producing the highest-quality and most original problem solutions spent more time encoding information bearing on key facts and anomalies (observations inconsistent with these facts). Apparently, information gathering not only contributes to creative problem solving, but the strategies employed in gathering information make a difference in process execution.
All these studies provide some evidence for the key processes included in Mumford et al. (1991) model of creative problem solving. Indeed, evidence was provided using different creative problem-solving tasks in diverse samples. The Mumford et al. (1996a) study, however, points to another question bearing on this model. What strategies employed in process execution contribute to more effective process execution in incidents of creative problem solving?
A study by Mumford, Baughman, Threlfall, et al. (1996b) examined the strategies contributing to performance in problem definition. Participants in this study were asked to work on the 3D television advertising task with solutions to these three problems both appraised by judges for quality and originality. Prior to starting work on this task, they were asked to work on a measure examining preferences for working with different types of material in problem definition. It was found that those people who produced highly original and high-quality problem solutions tended to define problems in terms of procedures and restrictions or constraints but not goals or information. Apparently, defining problems in terms of solution attributes inhibits creative thought.
Another series of studies by Barrett et al. (2013), Hester et al. (2012), Peterson et al. (2013), and Robledo et al. (2012) examined the kinds of concepts people may employ in creative problem solving. In all these studies participants were asked to solve either a marketing problem, a high-energy root beer campaign, or an educational problem calling for creative problem solving. And judges appraised the resulting problem solutions for quality, originality, and elegance. Prior to starting work on these problems, participants were instructed as to how to illustrate their mental models and provided with training in one of four approaches for concept selection: (1) causes (e.g., think about causes that operate synergistically), (2) constraints (e.g., think about resource constraints), (3) applications (e.g., how would your solution affect multiple key stakeholders), and (4) errors (e.g., think about whether potential errors are under your control). It was found that use of all four concept selection strategies contributed to creative problem solving, especially when people had stronger mental models for understanding the problem at hand. Although it is not clear if any one strategy was better than any other, it should be recognized that certain concept selection strategies such as error and applications strategies (i.e., strategies we often do not consider in discussions of creativity) did, in fact, contribute to creative problem solving.
A study of how people go about combining concepts has been conducted by Baughman and Mumford (1995). They asked participants to solve 12 category combination problems (e.g., owls, ostriches, robins, sparrows; ball, glove, net, racket) where judges rated the quality and originality of the exemplars provided to describe their new category. An instructional manipulation was used to encourage participants to (1) identify key features of each category, (2) map shared and non-shared features of the category, and (3) elaborate on emergent new features. It was found that feature mapping and elaboration contributed to production of more creative problem solutions. In follow-up studies, Mumford et al. (1997) found that metaphors and broader images may be employed to facilitate feature mapping, while Ward, Patterson, and Sifonis (2004) found that extensive elaboration on emergent features was critical to successful combination and reorganization efforts.
The ideas flowing from conceptual combination must be evaluated. Traditionally, idea evaluation has been viewed as solely an evaluative activity as opposed to an inherently generative activity. A study conducted by Lonergan, Scott, and Mumford (2004), however, indicates that use of generative, compensatory strategies is critical in creative problem solving. In this study, highly original or high-quality ideas for marketing the 3D holographic television were drawn from earlier work (Redmond et al., 1993). Participants were asked to assume the role of a manager evaluating these campaigns and were instructed to apply either operating efficiency or innovative standards in appraising ideas before preparing a final campaign, which would be appraised by judges for quality and originality. It was found the most creative campaigns were obtained when high-quality ideas were appraised with respect to innovation standards, and highly original ideas were appraised with respect to operating efficiency standards: presumably deep, focused appraisals (Gibson & Mumford, 2013). Thus, people employ a compensatory strategy in idea evaluation—a strategy accompanied by deep processing of key deficiencies.
Of course, idea evaluation also implies the need to forecast the implications of pursuing ideas. Similarly, Mumford, Shultz, and Van Doorn (2001) argued that forecasting is a key strategy underlying effective implementation planning. Byrne, Shipman, and Mumford (2010) and Shipman, Byrne, and Mumford (2010) examined the impact of forecasting on creative problem solving. In Byrne et al. (2010) participants were asked to formulate advertising campaigns for a new product, while in Shipman et al. (2010) they were asked to formulate plans for leading a new experimental secondary school. In both studies, written plans for addressing these problems were appraised by judges for quality, originality, and elegance. As participants worked on their plans, they received “e-mails” where they were asked to forecast the outcomes of their plans. The written answers to these e-mails were appraised for 29 forecasting attributes (e.g., number of positive outcomes forecast, number of obstacles forecast). A subsequent set of factorings yielded four dimensions—(1) forecasting extensiveness, (2) forecasting time frame, (3) forecasting resources, and (4) forecasting negative outcomes. Those who produced the most creative problem solutions forecasted more extensively and over a longer time frame—often spending more effort constructing back-up plans to cope with incidents of both failure and success (Giorgini & Mumford, 2013).
Apparently, Mumford et al. (1991) allowed us to draw some key conclusions about how creative people think—they define problems with respect to restrictions rather than goals, search for key facts and anomalies, employ flexible concept selection strategies, search for shared and non-shared elements of these concepts, elaborate on emergent new concepts, criticize and attempt to improve ideas based on their criticisms, and forecast extensively to arrive at actionable plans. Although these findings point to the value of this model, the structure of the model itself also points to more global structural propositions that may be tested.
For example, Mumford et al. (1997) examined the ability of these processes to predict creative performance. They asked participants to complete measures of four creative processes—problem definition, information gathering, concept selection, and conceptual combination. When appraisals of the quality and originality of creative problem solutions were regressed on these measures, it was found that each process made a unique contribution to predicting production of high-quality and original solutions. More centrally, the average multiple correlation obtained was .50—an especially impressive prediction when considering the reliability of the quality and originality appraisals registered in the mid-70s.
Another implication of this model is that the strategies employed during process execution will depend on the type of knowledge people are working with. Scott, Lonergan, and Mumford (2005) asked participants to formulate plans for leading a new experimental secondary school with these plans being appraised by judges for quality and originality. Prior to preparing these plans, however, they were presented with either concepts (e.g., team interaction) or multiple cases reflecting the same concepts. They found that the strategies employed in conceptual combination depended on the type of knowledge people were working with. Thus, if the concepts being worked with feature search and mapping, this contributed to the production of more creative problem solutions. However, if the cases being worked with include the analysis of case strengths and weakness, this contributed to production of more creative problem solutions.
Still another implication of this model is that errors made in execution of an earlier process (e.g., problem definition) will disrupt execution of later processing activities (e.g., information gathering). Friedrich and Mumford (2009) asked undergraduates to work on a marketing problem calling for creative thought where judges appraised marketing plans for quality, originality, and elegance. As participants worked through each process, new conflicting information was introduced to induce error in process execution. And the effectiveness of process execution was appraised both for the process where conflicting information was induced and subsequent processing activities where no conflicting information was induced. It was found not only that induction of conflicting information disrupted creative problem solving, a finding suggesting creative problem solving requires focused attention but that errors made in execution of earlier processes disrupted subsequent processing activities. Thus, the process flow through model holds—a point that also indicates creative processing is a risk-prone activity, due to the potential for error in executing any given process and may require multiple cycles of processing activity.
Although creative processing is difficult, the demands made by process execution may be offset by another phenomenon. Certain processes are particularly important to creative thought in certain domains—thus an error in a non-critical process may be of less concern. Mumford et al. (2010) asked doctoral students working in the biological, health, and social sciences to complete measures examining the effectiveness with which they executed each of these eight creative thinking processes: (1) problem definition, (2) information gathering, (3) concept selection, (4) conceptual combination, (5) idea generation, (6) idea evaluation, (7) implementation planning, and (8) adaptive monitoring. Biological scientists were especially skilled at information gathering and idea evaluation. Social scientists were especially skilled at conceptual combination and idea generation. Health scientists were especially skilled at problem definition and implementation planning. Not only do these findings suggest the processes generalize across domains of creative work (albeit with varying emphasis) but that the context in which creative work occurs may also be a noteworthy variable shaping effective executions of creative thinking processes.
Creative Thinking at Work
Earlier we noted that execution of creative thinking processes is based on knowledge. Knowledge, of course, depends in part on the availability of information bearing on the problem at hand. Accordingly, a variety of studies indicate that the intensity of scanning activities and information search is positively related to creativity and innovation (Anacona & Caldwell, 1992; Ford & Gioia, 2000; Koberg, Uhlenbruck, & Sarason, 1996; Souitaris, 2001). In this regard, however, it is important to ask what type of information should be sought in information gathering. Perhaps the most clear-cut conclusion here is that the information sought should be information relevant to the type of the creative problem at hand. Thus, scanning and information gathering is likely a tightly focused activity.
In this regard, however, the further point should be kept in mind. First, given the findings of Mumford et al. (1996a), it seems reasonable to expect viable information gathering will focus on both key facts and anomalies with respect to these facts. Second, work contexts that encourage intense information search in professional contexts can be expected to encourage creative problem solving (Damanpour & Aravind, 2012). Third, because information often is obtained from social networks, one would expect creative people to establish and maintain a broader more diverse professional network with creative people evidencing loose network ties (Perry-Smith & Shalley, 2003)—perhaps because loose ties provide access to anomalies.
Access to information, access that may be created by the person or a firm (e.g., conference attendance, software) is not itself of value. Information is of value only if it can be understood in context. In other words, use of information in creative problem solving will depend on expertise—a point noted in our discussion of the Vincent, Decker, and Mumford (2002) study. Expertise however, develops rather slowly (Ericsson & Charness, 1994) with expertise being acquired over longer time frames as the complexity of work increases. Thus, it is not surprising that creative achievements in the sciences typically occur in peoples’ mid-40s (Simonton, 2006).
The impact of experience on execution of creative thinking processes has been demonstrated in Mumford et al. (2000). They contrasted more senior (e.g., colonels) and more junior (e.g., lieutenants) officers with respect to effective execution of the creative thinking processes described earlier. It was found more senior officers executed all eight creative thinking processes more effectively than junior officers when working on military problems calling for creative thought. Perhaps more critically, certain work assignments were found to contribute to the growth of these processing capabilities. More specifically, a background data measure was used to assess assignment history. Assignments that involved exposure to novel problems, complex problems, discretionary decision making, strategic planning, and boundary spanning all contributed to acquisition of stronger creative processing capabilities. Of course, these findings also suggest that firms establishing career development systems that provide people with exposure to multiple challenging creative tasks are more likely to ensure effective execution of relevant creative thinking processes.
Expertise, of course, makes work easier—less demanding. Earlier, however, we noted that execution of the various creative thinking processes is a demanding and resource intensive activity. One implication of the resource demands made by creative processing is that undue time pressure cannot be placed on those asked to do creative work, and those doing creative work must be given some autonomy to manage the stress induced by a demanding resource intensive activity (Baer & Oldham, 2006).
Another implication of this observation, however, is that motivation will prove crucial to creative work. The task engagement induced by high motivational levels helps ensure people have the resources needed to invest in creative processing activities. Indeed, a variety of studies have indicated that multiple motivational mechanisms, in fact, contribute to creative performance including achievement motivation (Feist & Gorman, 1998), creative self-efficacy (Tierney & Farmer, 2002), and perceived task significance (Oldham & Cummings, 1996). In fact, work environments where people are presented with significant professionally meaningful tasks seem to engender both creative achievements and intense execution of relevant creative thinking processes (Gertner, 2013).
Of course, many variables influence human motivation in one way or another. With respect to creative processing, however, motivational variables that encourage investment of resources in process execution per se are likely to prove especially noteworthy. Recently, Watts, Steele, and Song (2017) conducted a quasi-meta-analytic study of the impact of need for cognition on creative problem solving. Need for cognition is likely a key motivational variable influencing creative processing by encouraging people to value and invest resources in cognitively demanding activities. Drawing data from prior studies, for example, Partlow, Medeiros, and Mumford (2015) found that the need for cognition was positively related to performance on various creative problem-solving tasks and, presumably, execution of the creative thinking processes underlying task performance such as implementation planning (Osburn & Mumford, 2006).
Trait need for cognition, however, also can be framed in terms of state needs. In fact, prior research indicates work context variables inducing state need for cognition contribute to creative problem-solving performance and effective execution of the processes underlying creative problem solving. One key variable in this regard is exposure to an intellectually stimulating work environment (Sosik, Kahai, & Avolio, 1998)—intellectual stimulation that may arise from the task, exchange with colleagues, or workplace design. State need for cognition, however, may also be induced by curiosity with respect to certain features of the problem at hand. Thus, Hardy, Ness, and Mecca (2017) asked participants to solve a marketing problem calling for creative thought where judges appraised problem solutions for quality and originality. Participants’ curiosity and information-seeking behavior were assessed. Information seeking was, apparently, based in curiosity with information acquired, in turn, contributing to creative processing and creative problem solving. The impact of curiosity on creative processing, however, explains why interest and autonomy in choice of work assignments often characterizes creative workplaces (Mumford & Hunter, 2005).
Curiosity, of course, is positively related to two other variables that have consistently proven to be related to creative problem solving and creative processing—openness and extraversion—noting that here extraversion is defined with respect to the five-factor model (Gill & Hodgkinson, 2007). For example, Vessey et al. (2011) found that extraversion was positively related to performance, in solving a marketing problem calling for creative thought while Partlow, Mederios, and Mumford (2015) found that openness was positively related to performance in solving an educational problem calling for creative thought. Although openness and extraversion are commonly conceived as characteristics of the person, it should also be recognized that work environments can be structured to allow expression of traits such as openness and extraversion. For example, encouraging debate with respect to key issues may, if appropriately managed, encourage people toward openness. Similarly, challenging people to think about competitors’ accomplishments may lead to extraversion. In fact, work context manipulations of the sort described above may prove particularly beneficial because the people attracted to jobs calling for creative problem solving tend to be open and extraverted.
Earlier we noted that in executing creative thinking processes, failure can be expected. This assertation was based on the observation that eight processes are involved in incidents of creative problem solving with multiple operations being called for in executing each process. As a result, failure is likely. And to complicate matters further, creative processing activities are a conscious, voluntary set of processes requiring a substantial investment of effort. Thus, people doing creative work are presented with a quandary: Why choose to invest in an effort that may fail?
These observations point to why a critical aspect of the work environment appears crucial to creativity. More specifically, people must have feelings of psychological safety and believe that failure will be tolerated. In fact, a study of organizational climate perceptions by Amabile et al. (1996) indicated that perceptions of support for creative efforts and perceptions of the environment’s tolerance of failure contributed to creative problem solving and, presumably, peoples’ willingness to invest resources in execution of creative thinking processes. In keeping with this observation Hunter et al. (2007) found that perceived support along with intellectual stimulation were strongly (d ≥ .80) to both innovative achievement and creative problem solving in the workplace.
The Hunter et al. (2007) study, however, points to another feature of the work context that appears critical to the application of creative thinking processes: mission clarity. Mission clarity refers to the perception that the work environment provides a non-ambiguous, specified, and structured understanding of the key objectives of the work and the nature of the problems to be addressed. And, Hunter et al. (2007) found mission clarity to be positively related to both innovative achievement and creative problem solving.
The impact of mission clarity on creative thinking may strike many as disconcerting given the old notion that creative people should be free to explore. Mission clarity, however, implies that exploration will be self-limited or externally imposed. When one recognizes that creative problems are novel, complex, and ill-defined, however, it becomes apparent that mission clarity and imposition of structure may, in fact, be necessary to direct exploration along productive avenues (Mumford, Bedell-Avers, & Hunter, 2008).
The need to structure creative work is nicely illustrated in Marta, Leritz, and Mumford (2005). In this study, 55 teams were asked to formulate turn-around plans for a failing automotive firm. Team plans were appraised by judges for quality and originality. Following plan preparation, participants nominated team leaders. Leader consideration and initiating structure was assessed, and it was found that leader structuring behavior contributed to the production of problem solutions of both higher quality and originality. Of course, the need for structure does not imply overly close supervision, which generally inhibits creative thought (Barnow, 1976). Rather, a clear understanding of task objectives and problems likely to be encountered in executing this task must be available for people to structure ill-defined problems and to execute requisite creative thinking process (Keller, 2006).
Creative Thinking in Social Contexts
All work occurs in a distinctly social context involving creative problem solving. Accordingly, one may expect that social forces would also influence peoples’ willingness to invest resources in execution of the creative thinking process and their success in executing these processes. For example, information flow and information access in firms may influence the success of people’s processing and ultimately the success of their creative problem-solving efforts (Amabile & Conti, 1997). The layout and design of work spaces will, of course, influence information flow as well as the nature of social exchange among people as they work on creative problems. Access to and support from other teams—access and support, in part, conditioned by organizational structures—may influence creative processing as well as the feasibility of formulating cross-functional teams (Souitaris, 2001).
Although these, and a number of other contextual variables may influence the effectiveness of peoples’ creative processing, one variable that appears of special importance is leadership (Mumford, Scott, Gaddis, & Strange, 2002). For example, Barnow (1976) has shown that key leadership skills such as expertise and task structure contribute to the success of creative teams. More recently, Robledo, Peterson, and Mumford (2012) presented a model describing the key features that must be executed by those asked to lead creative teams. This model holds that leaders of creative efforts must (1) lead the work, defining fundamentals and themes to be pursued, planning how the work will be done, and helping team members resolve crises (e.g., Hemlin & Olsson, 2011); (2) lead the firm, championing the project to senior management, establishing requisite contacts in the firm, and educating firm members with respect to the impact of creative efforts (e.g., Howell & Boies, 2004); and (3) lead the group, recruiting team members, establishing an appropriate set of team processes and promoting a creative climate (e.g., Amabile et al., 1996).
Vessey et al. (2014) provided compelling support for this model. They content analyzed academic biographies available for 93 prominent scientific leaders who led various creative teams. Then they appraised the number of creative products produced by these teams and the impact of these products on both the field and the firm. Vessey and colleagues found not only that the leader’s skill in executing those three key functions contributed to the success of creative teams but also that the leader’s skill in executing these three functions was positively related to objective indices (e.g., H index) of their creative achievement.
Of course, leaders’ actions shape many social contextual variables that influence people’s creative processing activities. Clearly, leaders may encourage participation by team members (Zhou, Hirst, & Shipton, 2012)—participation that may encourage investment of resources in process execution. Similarly, leaders may provide team members with access to experts and technology needed for their work—access needed to provide information and encourage investment of resources in process execution. As important as these and other leader actions may be, a key function of leaders is providing team members with shared mental models.
The impact of shared mental models on team performance has been demonstrated in many studies (e.g., Day, Gronn, & Salas, 2004). And there is reason to expect that the availability of shared mental models influences creative process execution. The availability of shared mental models promotes effective communication in teams—communication that provides the exchange of expertise and knowledge needed for process execution. More centrally, Mumford, Feldman, Hein, and Nagao (2001) have shown that the availability of shared mental models also contributes to effective execution of creative problem-solving processes when people are working in team settings.
In this study, teams of three to five individuals were asked to solve either a cognitive problem (i.e., endowment allocation) or a social problem (i.e., a poorly performing team member). Prior to starting work on these problems, participants were as asked to watch a video intended to induce a shared mental model for understanding the problem at hand. Team members were asked to generate ideas for solving the problem (an idea generation task) and to select their best idea (an idea evaluation task). The number of ideas produced and the appraised creativity of ideas selected served as the outcomes of concern. It was found the number of ideas generated and the creativity of the ideas provided was higher when team members evidenced a shared mental model—regardless of whether the training video tape was, or was not, congruent with the problem presented. Thus, the availability of shared mental models does apparently influence process execution.
The availability of shared mental models, however, may influence process execution in another way. De Dreu (2006) and Gilson and Shalley (2004) indicated that feedback from other team members—critical feedback—is beneficial to creative problem solving if the feedback provided is task based, not personal, and not of excessive intensity. In fact, the availability of shared mental models helps ensure the feedback provided by other team members will be task focused and of requisite depth to encourage effective process execution.
Some support for this observation has been provided in Gibson and Mumford (2013). They asked participants to formulate solutions to a marketing problem calling for creative thought. Judges appraised solutions provided for originality, quality, and elegance. Prior to preparing their problem solutions, however, participants were presented with a set of candidate ideas where they were asked to provide a written critique of these ideas. Judges appraised these critiques with respect to the number, depth, usefulness, range, complexity, isolation, risk sensitivity, relevance, and specificity. It was found the most creative problem solutions were produced by people who produced a limited number of deep criticisms of potential ideas. What should be recognized here, however, is that those who provided others with key deep criticisms of ideas will typically be those who possess a similar shared mental model of the problem at hand. Thus, the availability of shared mental models may allow productive, task relevant conflict—conflict that presumably contributes to team members’ capacity to execute requisite creative thinking processes.
Earlier, we noted that execution of the creative thinking processes is a demanding, resource-intensive activity. Put somewhat differently, this observation implies investment of requisite resources in execution of creative thinking processes will be based on an appraisal of whether process execution is likely to prove at all possible. Indeed, economic studies indicate that in domains, for example electrical systems (Wise, 1992), innovation comes in waves as the conditions arise that permit creative thought and encourage adequate investment in creative-thinking processes. At a more “local” level, however, it seems plausible to argue people will invest scarce cognitive resources in process execution and creative problem solving only when they believe they have the resources needed to solve the problem at hand. Indeed, it appears that people in creative efforts appraise available work resources (e.g., time), available social resources (e.g., other team members’ expertise), and fiscal/physical resources (e.g., equipment availability) before deciding to invest requisite cognitive resources in execution of creative thinking processes.
Although we know relatively little about how people appraise the potential for viable creative processing, we do know three things about peoples’ willingness to invest resources in creative processing. First, adequate physical/financial resources, albeit not a surfeit of resources, should be available (Nohari & Gulati, 1996). Second, the problems presented should be relevant to their domain of professional expertise and advancement of their professional careers (Mumford & Hunter, 2005). Third, social context—for example leaders and peers—should explicitly call for creative thought (Runco, Illies, & Eisenman, 2005). Put differently, if we do not ask for creativity under conditions where creative processing is possible, we are unlikely to see people attempt to execute creative thinking processes and produce creative problem solutions.
Clearly, since the 1980s we have begun to develop a far better understanding of the key cognitive processes contributing to creative problem solving. Broadly speaking, the Mumford et al. (1991) model holds that creative problem solving requires that people be able to execute eight key processes: (1) problem definition, (2) information gathering, (3) concept selection, (4) conceptual combination, (5) idea generation, (6) idea evaluation, (7) implementation planning, and (8) adaptive monitoring. Thus, within this model, creativity is not simply a matter of idea generation. Far more goes into creative problem solving than simply generating lots of ideas.
Indeed, the evidence gathered since the 1980s indicates that this model is, in fact, plausible and likely the best available model of the processing activities required for creative problem solving. More specifically, the evidence reviewed in the present effort indicates: (1) effective execution of each process contributes to performance on a variety of creative problem-solving tasks, (2) effective execution of each process makes a unique contribution to production of creative problem-solving performance, (3) effective execution of all these processes is strongly related to the originality, quality, and elegance of people solutions to creative problems, (4) these processes are applied (albeit with different emphasis) across a variety of performance domains, and (5) errors flow through such that mistakes made in executing earlier processing operations (e.g., problem definition) result in poorer performance in executing subsequent processing activities (e.g., information gathering).
Not only has prior research provided strong support for this model of the creative thinking process, key variables shaping effective process execution have been identified. For example, we have shown that expertise and the specific type of knowledge, conceptual or case-based, people employ in problem solving influences the effectiveness of process execution. Moreover, in our prior research we have begun to isolate the specific strategies that contribute to effective execution of each of these processes. For example, creative people define problems based on procedures and restrictions not goals; they search for key facts and anomalies, employ a variety of concepts in working with these facts and anomalies, search for shared and non-shared features of these concepts, generate ideas pragmatically, work with ideas in an active fashion to compensate for deficiencies, forecast the implications of their ideas in planning, and they execute backup plans adaptively.
These findings are noteworthy partly because they point to specific educational and training interventions that could be used to improve creative thinking. In fact, Scott, Leritz, and Mumford (2004), in a meta-analytic study of the effectiveness of creativity training, found that the most effective interventions for improving peoples’ creative problem solving were those that explicitly focused on these processes and acquisition of more viable strategies for process execution. Along somewhat different lines, managers may be encouraged to appraise creative teams or creative work vis-à-vis the effectiveness with which these processes are executed (Licuanan, Dailey, & Mumford, 2007). Along somewhat different lines, managers or firms might explicitly seek to create an environment that encourages effective execution of each of these processes under conditions where creative problem solving is an integral aspect of performance.
These and a number of other potentially viable interventions point to the need for a new wave of research examining creative problem-solving processes that recognize there is far more to creative problem solving than simple idea generation. For example, one might ask how should expertise be balanced in teams? How important is it to define problems in teams of professional fundamentals? Under what conditions do team members compensate for a person’s deficiencies in executing a certain creative thinking process?
The present effort, however, and our understanding of creative thinking processes point to another noteworthy implication of work along these lines. In the present effort we have tied various aspects of work context and the social context to effective execution of these processes. For example, we have provided an explanation why motivation is so important for creative work—motivation encourages people to invest resources in process execution. We have provided an explanation for why shared mental models are important for creative work—they provide a basis for formulating deep criticisms. Although other examples of this sort may be cited, these examples serve to make a key point. As Mumford, Hunter, and Byrne (2009) have pointed out, creative thinking processes provide the fundamental foundation for understanding creative problem solving and creative performance. By developing theories based on this fundamental well-validated proposition we may begin to develop a stronger and more robust understanding of creative work in firms.
We would like to thank Tristan McIntosh, Roni Reiter-Palmon, Robert Sternberg, and Mark Runco for their contributions the present effort. Correspondence should be addressed to Dr. Michael D. Mumford, Department of Psychology, The University of Oklahoma 73019 or firstname.lastname@example.org.
Amabile, T. M., & Conti, R. (1997). Environmental determinants of work motivation, creativity, and innovation: The case of R&D downsizing. Technological innovation. In R. Garud, P. R. Nayyar, & Z. B. Shapira (Eds.), Technological innovation: Oversights and foresights (pp. 111–125). Cambridge, U.K.: Cambridge University Press.Find this resource:
Amabile, T. M., Conti, R., Coon, H., Lazenby, J., & Herron, M. (1996). Assessing the work environment for creativity. Academy of Management Journal, 39, 1154–1184.Find this resource:
Anacona, D., & Caldwell, D. (1992). Demography and design: Predictors of new product team performance. Organization Science, 3, 321–341.Find this resource:
Andersen, H., Barker, P., & Chen, X. (2006). The cognitive structure of scientific revolutions. New York, NY: Cambridge University Press.Find this resource:
Arreola, N. J., & Reiter-Palmon, R. (2016). The effect of problem construction creativity on solution creativity across multiple everyday problems. Psychology of Aesthetics, Creativity, and the Arts, 10, 287–295.Find this resource:
Baer, M., & Oldham, G. R. (2006). The curvilinear relation between experienced creative time pressure and creativity: Moderating effects of openness to experience and support for creativity. Journal of Applied Psychology, 91, 963–970.Find this resource:
Barrett, J. D., Peterson, D. R., Hester, K. S., Robledo, I. C., Day, E. A., Hougen, D. P., . . . Mumford, M. D. (2013). Thinking about applications: Effects on mental models and creative problem-solving. Creativity Research Journal, 25, 199–212.Find this resource:
Barnow, J. C. (1976). Worker performance and task complexity as causal determinants of leader behavior style and flexibility. Journal of Applied Psychology, 61, 433–440.Find this resource:
Baughman, W. A., & Mumford, M. D. (1995). Process-analytic models of creative capacities: Operations influencing the combination-and-reorganization process. Creativity Research Journal, 8, 37–62.Find this resource:
Besemer, S. P., & O’Quin, K. (1998). Creative product analysis matrix: Testing the model structure and a comparison among products—three novel chairs. Creativity Research Journal, 11, 333–346.Find this resource:
Boucher, L., & Dienes, Z. (2003). Two ways of learning associations. Cognitive Science, 27, 807–842.Find this resource:
Byrne, C. L., Shipman, A. S., & Mumford, M. D. (2010). The effects of forecasting on creative problem-solving: An experimental study. Creativity Research Journal, 22, 119–138.Find this resource:
Cefis, E., & Marsili, O. (2005). A matter of life and death: Innovation and firm survival. Industrial and Corporate Change, 14, 1167–1192.Find this resource:
Christiaans, H. H. (2002). Creativity as a design criterion. Communication Research Journal, 14, 41–54.Find this resource:
Dailey, L., & Mumford, M. D. (2006). Evaluative aspects of creative thought: Errors in appraising the implications of new ideas. Creativity Research Journal, 18, 385–390.Find this resource:
Damanpour, F., & Aravind, D. (2012). Managerial innovation: Conceptions, processes, and antecedents. Management and Organization Review, 8, 423–454.Find this resource:
Day, D. V., Gronn, P., & Salas, E. (2004). Leadership capacity in teams. The Leadership Quarterly, 15, 857–880.Find this resource:
De Dreu, C. K. (2006). When too little or too much hurts: Evidence for a curvilinear relationship between task conflict and innovation in teams. Journal of Management, 32, 83–107.Find this resource:
Dewey, J. (1910). Science as subject-matter and as method. Science, 31, 121–127.Find this resource:
Donovan, A., & Bonney, J. (2006). The box that changed the world: fifty years of container shipping-an illustrated history. Cranberry, NJ: Commonwealth Business Media Inc.Find this resource:
Ericsson, K. A. (Ed.). (2009). Development of professional expertise: Toward measurement of expert performance and design of optimal learning environments. New York, NY: Cambridge University Press.Find this resource:
Ericsson, K. A., & Charness, N. (1994). Expert performance: Its structure and acquisition. American Psychologist, 49, 725–747.Find this resource:
Ericsson, K. A., Hoffman, R. R., Kozbelt, A., & Williams, A. M. (Eds.). (2018). The Cambridge handbook of expertise and expert performance. Cambridge University Press.Find this resource:
Estes, W. K. (1991). Cognitive architectures from the standpoint of an experimental psychologist. Annual Review of Psychology, 42, 1–28.Find this resource:
Estes, Z., & Ward, T. B. (2002). The emergence of novel attributes in concept modification. Creativity Research Journal, 14, 149–156.Find this resource:
Feist, G. J., & Gorman, M. E. (1998). The psychology of science: Review and integration of a nascent discipline. Review of General Psychology, 2, 3–47.Find this resource:
Finke, R. A., Ward, T. B., & Smith, S. M. (1992). Creative cognition: Theory, research, and applications. Cambridge, MA: MIT Press.Find this resource:
Ford, C. M., & Gioia, D. A. (2000). Factors influencing creativity in the domain of managerial decision making. Journal of Management, 26, 705–732.Find this resource:
Friedrich, T. L., & Mumford, M. D. (2009). The effects of conflicting information on creative thought: A source of performance improvements or decrements? Creativity Research Journal, 21, 265–281.Find this resource:
Furnham, A. (1999). Personality and creativity. Perceptual and Motor Skills, 88, 407–408.Find this resource:
Ganesan, V. (2012). Internal combustion engines. New Delhi, India: Tata McGraw-Hill Education.Find this resource:
Gertner, J. (2013). The idea factory: Bell Labs and the great age of American innovation. New York, NY: Penguin.Find this resource:
Gibson, C., & Mumford, M. D. (2013). Evaluation, criticism, and creativity: Criticism content and effects on creative problem solving. Psychology of Aesthetics, Creativity, and the Arts, 7, 314–331.Find this resource:
Gill, C. M., & Hodgkinson, G. P. (2007). Development and validation of the five-factor model questionnaire (FFMQ): An adjectival-based personality inventory for use in occupational settings. Personnel Psychology, 60, 731–766.Find this resource:
Gilson, L. L., & Shalley, C. E. (2004). A little creativity goes a long way: An examination of teams’ engagement in creative processes. Journal of Management, 30, 453–470.Find this resource:
Giorgini, V., & Mumford, M. D. (2013). Backup plans and creative problem-solving: Effects of causal, error, and resource processing. The International Journal of Creativity and Problem Solving, 23, 121–147.Find this resource:
Goldvarg, E., & Johnson-Laird, P. N. (2001). Naive causality: A mental model theory of causal meaning and reasoning. Cognitive Science, 25, 565–610.Find this resource:
Gordon, R. J. (2016). The rise and fall of American growth: The US standard of living since the civil war. Princeton, NJ: Princeton University Press.Find this resource:
Guilford, J. P. (1950). Creativity. American Psychologist, 5, 444–454.Find this resource:
Hardy, J. H., Ness, A. M., & Mecca, J. (2017). Outside the box: Epistemic curiosity as a predictor of creative problem solving and creative performance. Personality and Individual Differences, 104, 230–237.Find this resource:
Hemlin, S., & Olsson, L. (2011). Creativity‐stimulating leadership: A critical incident study of leaders’ influence on creativity in research groups. Creativity and Innovation Management, 20, 49–58.Find this resource:
Hester, K. S., Robledo, I. C., Barrett, J. D., Peterson, D. R., Hougen, D. P., Day, E. A., . . . Mumford, M. D. (2012). Causal analysis to enhance creative problem-solving: Performance and effects on mental models. Creativity Research Journal, 24, 115–133.Find this resource:
Howell, J. M., & Boies, K. (2004). Champions of technological innovation: The influences of contextual knowledge, role orientation, idea generation, and idea promotion on champion emergence. Leadership Quarterly, 15, 130–149.Find this resource:
Hunter, S. T., Bedell, K. E., & Mumford, M. D. (2007). Climate for creativity: A quantitative review. Creativity Research Journal, 19, 69–90.Find this resource:
Hunter, S. T., Bedell-Avers, K. E., & Mumford, M. D. (2007). The typical leadership study: Assumptions, implications, and potential remedies. Leadership Quarterly, 18, 435–446.Find this resource:
Isaacson, W. (2012). The real leadership lessons of Steve Jobs. Harvard Business Review, 90, 92–102.Find this resource:
Kanigel, R. (2005). The one best way: Frederick Winslow Taylor and the enigma of efficiency. New York, NY. Penguin.Find this resource:
Keller, R. T. (2006). Transformational leadership, initiating structure, and substitutes for leadership: A longitudinal study of research and development project team performance. Journal of Applied Psychology, 91, 202–210.Find this resource:
Koberg, C. S., Uhlenbruck, N., & Sarason, Y. (1996). Facilitators of organizational innovation: The role of life-cycle stage. Journal of Business Venturing, 11, 133–149.Find this resource:
Licuanan, B. F., Dailey, L. R., & Mumford, M. D. (2007). Idea evaluation: Error in evaluating highly original ideas. The Journal of Creative Behavior, 41, 1–27.Find this resource:
Lonergan, D. C., Scott, G. M., & Mumford, M. D. (2004). Evaluative aspects of creative thought: Effects of appraisal and revision standards. Creativity Research Journal, 16, 231–246.Find this resource:
Marcy, R. T., & Mumford, M. D. (2007). Social innovation: Enhancing creative performance through causal analysis. Creativity Research Journal, 19, 123–140.Find this resource:
Marta S., Leritz, L. E., & Mumford, M. D. (2005). Leadership skills and group performance: Situational demands, behavioral requirements, and planning. The Leadership Quarterly, 16, 97–120.Find this resource:
Medeiros, K. E., Partlow, P. J., & Mumford, M. D. (2014). Not too much, not too little: The influence of constraints on creative problem solving. Psychology of Aesthetics, Creativity, and the Arts, 8, 198–210.Find this resource:
Mobley, M. I., Doares, L. M., & Mumford, M. D. (1992). Process analytic models of creative capacities: Evidence for the combination and reorganization process. Creativity Research Journal, 5, 125–155.Find this resource:
Mumford, M. D., & Gustafson, S. B. (2007). Creative thought: Cognition and problem solving in a dynamic system. Creativity Research Handbook, 2, 33–77.Find this resource:
Mumford, M. D., & Hunter, S. T. (2005). Innovation in organizations: A multi-level perspective on creativity. In F. Yammarino & F. Dansereau (Eds.), Multi-level issues in strategy and methods (pp. 9–73). Bingley, U.K.: Emerald Group.Find this resource:
Mumford, M. D., Antes, A. L., Caughron, J. J., Connelly, S., & Beeler, C. (2010). Cross-field differences in creative problem-solving skills: A comparison of health, biological, and social sciences. Creativity Research Journal, 22, 14–26.Find this resource:
Mumford, M. D., Baughman, W. A., Maher, M. A., Costanza, D. P., & Supinski, E. P. (1997). Process-based measures of creative problem-solving skills: IV. Category combination. Creativity Research Journal, 10, 59–71.Find this resource:
Mumford, M. D., Baughman, W. A., Supinski, E. P., & Maher, M. A. (1996a). Process-based measures of creative problem-solving skills: II. Information encoding. Creativity Research Journal, 9, 77–88.Find this resource:
Mumford, M. D., Baughman, W. A., Threlfall, K. V., Supinski, E. P., & Costanza, D. P. (1996b). Process-based measures of creative problem-solving skills: I. Problem construction. Creativity Research Journal, 9, 63–76.Find this resource:
Mumford, M. D., Bedell-Avers, K. E., & Hunter, S. T. (2008). Planning for innovation: A multi-level perspective. In M. D. Mumford, S. T. Hunter, & K. E. Bedell-Avers (Eds.), Multi-level issues in creativity and innovation (pp. 107–154). Bingley, U.K.: Emerald Group.Find this resource:
Mumford, M. D., Feldman, J. M., Hein, M. B., & Nagao, D. J. (2001). Tradeoffs between ideas and structure: Individual versus group performance in creative problem solving. The Journal of Creative Behavior, 35(1), 1–23.Find this resource:
Mumford, M. D., Hester, K. S., Robledo, I. C., Peterson, D. R., Day, E. A., Hougen, D. F., . . . Barrett, J. D. (2012). Mental models and creative problem-solving: The relationship of objective and subjective model attributes. Creativity Research Journal, 24, 311–330.Find this resource:
Mumford, M. D., Hunter, S. T., & Byrne, C. L. (2009). What is the fundamental? The role of cognition in creativity and innovation. Industrial and Organizational Psychology, 2, 353–356.Find this resource:
Mumford, M. D., Marks, M. A., Connelly, M. S., Zaccaro, S. J., & Reiter-Palmon, R. (2000). Development of leadership skills: Experience and timing. The Leadership Quarterly, 11, 87–114.Find this resource:
Mumford, M. D., Marks, M. A., Connelly, M. S., Zaccaro, S. J., & Johnson, J. F. (1998). Domain-based scoring in divergent-thinking tests: Validation evidence in an occupational sample. Creativity Research Journal, 11, 151–163.Find this resource:
Mumford, M. D., McIntosh, T., & Mulhearn, T. (2018). Using cases to understand expert performance: Method and methodological triangulation. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.), The Cambridge handbook of expertise and expert performance. New York, NY: Cambridge University Press.Find this resource:
Mumford, M. D., Mobley, M. I., Reiter‐Palmon, R., Uhlman, C. E., & Doares, L. M. (1991). Process analytic models of creative capacities. Creativity Research Journal, 4, 91–122.Find this resource:
Mumford, M. D., Scott, G. M., Gaddis, B., & Strange, J. M. (2002). Leading creative people: Orchestrating expertise and relationships. The Leadership Quarterly, 13, 705–750.Find this resource:
Mumford, M. D., Supinski, E. P., Baughman, W. A., Costanza, D. P., & Threlfall, K. V. (1997). Process-based measures of creative problem-solving skills: V. Overall prediction. Creativity Research Journal, 10, 73–85.Find this resource:
Mumford, M. D., Schultz, R., & Van Doorn, J. A. (2001). Performance in planning: Processes, requirements, and errors. Review of General Psychology, 5, 213–240.Find this resource:
Nohari, K., & Gulati, S. (1996). Is slack good or bad for innovation? Academy of Management Journal, 39, 799–825.Find this resource:
Oldham, G. R., & Cummings, A. (1996). Employee creativity: Personal and contextual factors at work. Academy of Management Journal, 39, 607–634.Find this resource:
Osburn, H. K., & Mumford, M. D. (2006). Creativity and planning: Training interventions to develop creative problem-solving skills. Creativity Research Journal, 18, 173–190.Find this resource:
Parnes, S. J., & Noller, R. B. (1972). Applied creativity: The creative studies project: Part results of a two year study. Journal of Creative Behavior, 6, 164–186.Find this resource:
Partlow, P. J., Medeiros, K. E., & Mumford, M. D. (2015). Leader cognition in vision formation: Simplicity and negativity. The Leadership Quarterly, 26, 448–469.Find this resource:
Paulus, P. B., & Yang, H. C. (2000). Idea generation in groups: A basis for creativity in organizations. Organizational Behavior and Human Decision Processes, 82, 76–87.Find this resource:
Perry-Smith, J. E., & Shalley, C. E. (2003). The social side of creativity: A static and dynamic social network perspective. Academy of Management Review, 28, 89–106.Find this resource:
Peterson, D. R., Barrett, J. D., Hester, K. S., Robledo, I. C., Hougen, D. F., Day, E. A., . . . Mumford, M. D. (2013). Teaching people to manage constraints: Effects on creative problem-solving. Creativity Research Journal, 25, 335–347.Find this resource:
Phye, G. D. (1990). Inductive problem solving: Schema Inducement and memory-based transfer. Journal of Educational Psychology, 82, 826–831.Find this resource:
Redmond, M. R., Mumford, M. D., & Teach, R. J. (1993). Putting creativity to work: Effects of leader behavior on subordinate creativity. Organizational Behavior and Human Decision Processes, 55, 120–151.Find this resource:
Reiter-Palmon, R., Mumford, M. D., O’Connor Boes, J., & Runco, M. A. (1997). Problem construction and creativity: The role of ability, cue consistency, and active processing. Creativity Research Journal, 10, 9–23.Find this resource:
Robledo, I. C., Hester, K. S., Peterson, D. R., Barrett, J. D., Day, E. A., Hougen, D. P., . . . Mumford, M. D. (2012). Errors and understanding: The effects of error-management training on creative problem-solving. Creativity Research Journal, 24, 220–234.Find this resource:
Robledo, I. C., Peterson, D. R., & Mumford, M. D. (2012). Leadership of scientists and engineers: A three‐vector model. Journal of Organizational Behavior, 33, 140–147.Find this resource:
Rodgers, E. M., & Adhikarya, R. (1979). Diffusion of innovations: Up to date review and commentary. In D. Nimmo (Ed.), Communications yearbook 3 (pp. 67–81). New Brunswick, NJ: Transaction.Find this resource:
Runco, M. A., Illies, J. J., & Eisenman, R. (2005). Creativity, originality, and appropriateness: What do explicit instructions tell us about their relationships? The Journal of Creative Behavior, 39, 137–148.Find this resource:
Scott, G. M., Lonergan, D. C., & Mumford, M. D. (2005). Conceptual combination: Alternative knowledge structures, alternative heuristics. Creativity Research Journal, 17, 79–98.Find this resource:
Scott, G., Leritz, L. E., & Mumford, M. D. (2004). The effectiveness of creativity training: A quantitative review. Creativity Research Journal, 16, 361–388.Find this resource:
Shipman, A. S., Byrne, C. L., & Mumford, M. D. (2010). Leader vision formation and forecasting: The effects of forecasting extent, resources, and timeframe. The Leadership Quarterly, 21, 439–456.Find this resource:
Shondrick, S. J., Dinh, J. E., & Lord, R. G. (2010). Developments in implicit leadership theory and cognitive science: Applications to improving measurement and understanding alternatives to hierarchical leadership. The Leadership Quarterly, 21, 959–978.Find this resource:
Silverman, B. G. (1985). Expert intuition and ill-structured problem solving. IEEE Transactions on Engineering Management, 1, 29–33.Find this resource:
Simonton, D. K. (2006). Creative productivity through the adult years. In M. R. Moody (Ed.), Aging: Concepts and controversies (5th ed., pp. 95–100). Los Angeles, CA: SAGE.Find this resource:
Sosik, J. J., Kahai, S. S., & Avolio, B. J. (1998). Transformational leadership and dimensions of creativity: Motivating idea generation in computer-mediated groups. Creativity Research Journal, 11, 111–121.Find this resource:
Souitaris, V. (2001). Strategic influences of technological innovation in Greece. British Journal of Management, 12, 131–147.Find this resource:
Sternberg, R. J. (1988). A three facet model of creativity. In R. J. Sternberg (Ed.), The nature of creativity (pp. 125–148). Cambridge, U.K.: Cambridge University Press.Find this resource:
Tierney, P., & Farmer, S. M. (2002). Creative self-efficacy: Its potential antecedents and relationship to creative performance. Academy of Management Journal, 45, 1137–1148.Find this resource:
Vessey, W. B., Barrett, J. D., Mumford, M. D., Johnson, G., & Litwiller, B. (2014). Leadership of highly creative people in highly creative fields: A historiometric study of scientific leaders. The Leadership Quarterly, 25, 672–691.Find this resource:
Vessey, W. B., Barrett, J., & Mumford, M. D. (2011). Leader cognition under threat: ‘Just the facts.’ The Leadership Quarterly, 22, 710–728.Find this resource:
Vincent, A. S., Decker, B. P., & Mumford, M. D. (2002). Divergent thinking, intelligence, and expertise: A test of alternative models. Creativity Research Journal, 14, 163–178.Find this resource:
Wallas, G. (1926). The art of thought. New York, NY: Harcourt Brace.Find this resource:
Ward, T. B., Patterson, M. J., & Sifonis, C. M. (2004). The role of specificity and abstraction in creative idea generation. Creativity Research Journal, 16, 1–9.Find this resource:
Watts, L. L., Steele, L. M., & Song, H. (2017). Re-examining the relationship between need for cognition and creativity: Predicting creative problem solving across multiple domains. Creativity Research Journal, 29, 21–28.Find this resource:
Wise, G. (1992). Inventions and corporations in the maturing electrical industry. In R. J. Weber & D. N. Perkins (Eds.), Inventive minds: Creativity in technology (pp. 291–310). New York, NY: Oxford University Press.Find this resource:
Zhou, Q., Hirst, G., & Shipton, H. (2012). Context matters: Combined influence of participation and intellectual stimulation on the promotion focus–employee creativity relationship. Journal of Organizational Behavior, 33, 894–909.Find this resource: