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DOI: 10.1177/0149206312441835 2012 38: 1362 originally published online 17 April 2012Journal of Management

Tamara Montag, Carl P. Maertz, Jr. and Markus BaerA Critical Analysis of the Workplace Creativity Criterion Space

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A Critical Analysis of the Workplace Creativity Criterion Space

Tamara MontagCarl P. Maertz, Jr.

Saint Louis University

Markus BaerWashington University in St. Louis

This article outlines a criterion-oriented framework for understanding workplace creativity. Drawing from research on job performance, the authors make three important conceptual distinc-tions. First, they add theoretical and methodological precision to the workplace creativity litera-ture by separating creative performance behaviors from the creative outcomes they produce. Second, they explain inconsistent findings in the extant creativity literature by distinguishing expected versus unexpected creative performance behaviors. Third, they provide an alternative approach to conceptualizing and measuring novelty and usefulness by considering them as for-mative dimensions of a creative outcome. These distinctions form the basis for their framework, which provides researchers with a theoretically grounded approach to measuring creativity in a more nuanced way. Finally, the authors highlight several key avenues for future research.

Keywords: creativity; workplace creativity; performance; criterion, measurement

Workplace creativity, typically defined as the production of novel and useful ideas for organizational products, services, or processes (Amabile, 1983a; Oldham & Cummings, 1996), has become one of the key drivers of growth, performance, and valuation in organizations today. According to a recent McKinsey Global Survey, 70% of corporate

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Acknowledgments: This article was accepted under the editorship of Talya N. Bauer. We would like to thank Brandon Smit, Wendi Maertz, and two anonymous reviewers for their insightful comments during the writing and revision of this article.

Corresponding author: Tamara Montag, Department of Psychology, Saint Louis University, Saint Louis, MO 63103, USA

E-mail: [email protected]

Journal of ManagementVol. 38 No. 4, July 2012 1362-1386

DOI: 10.1177/0149206312441835© The Author(s) 2012

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Montag et al. / Workplace Creativity Criterion Space 1363

leaders indicated that tapping new ideas is a top priority for driving firm growth. Reflecting this increasing number of executives who make creativity a top priority, interest in the topic among organizational scholars has exploded over the past few decades. In fact, research on workplace creativity is growing at such a pace that efforts to review and synthesize the burgeoning literature now appear regularly (e.g., Baas, De Dreu, & Nijstad, 2008; George, 2007; Hennessey & Amabile, 2010; Mumford & Gustafson, 1988; Runco, 2004; Shalley, Zhou, & Oldham, 2004).

Although earlier reviews have brought much-needed organization to the rapidly growing field of workplace creativity, many if not all previous reviews have at least one thing in common—a focus on organizing the literature according to the predictors of creativity. For example, Zhou and Shalley (2003) reviewed both the personal (personality, self-efficacy) and contextual (goals, performance evaluation and feedback, social influence, supervisor behaviors, leadership, job design) antecedents of creativity. Similarly, George (2007) reviewed the literature proceeding from a focus on within-individual processes (conscious and unconscious thinking, positive and negative affect) to a molar perspective, capturing both the contextual (signals of safety, creativity prompts, supervisors and leaders, networks) and the group-level antecedents. Organizing the extant literature exclusively around the various antecedents of creativity, however, may have caused some reviews of the literature to conclude that results across studies are often inconsistent and that few firm conclusions can be drawn regarding creativity (e.g., Oldham & Baer, 2011; Shalley et al., 2004). That is, not explicitly examining the different ways in which workplace creativity itself has been conceptualized and measured may have obscured findings and interpretations across studies, which could stifle theoretical and empirical progress. It seems, thus, that there is now a growing need for a paper that examines key differences in the creativity criterion space.

The goal of this article is to add theoretical and methodological precision to the workplace creativity literature by proposing a framework for organizing and understanding extant workplace creativity criterion constructs, drawing on lessons from the well-established job performance literature (e.g., Campbell, 1990; Dunnette, 1963). This article is not meant to be a traditional, comprehensive review, covering all workplace creativity studies during a certain time frame. Instead, we sought and reviewed (1) theoretical papers on workplace criteria and creativity, (2) studies that address the definition and/or measurement of workplace creativity, (3) studies that specifically examine the creativity criterion space, (4) studies that exemplify current criterion ambiguity in the area, (5) studies employing multiple creativity criterion measures, and (6) studies addressing or exemplifying definitive distinctions in the criterion space (novelty vs. usefulness of outcomes; expected vs. unexpected creative behaviors).

This criterion-oriented analysis contributes to the extant creativity literature in a number of important ways. First, our central thesis is that the term “creativity” has been used to refer to a number of different constructs, suggesting that rather than thinking of creativity as a unitary construct, it may be beneficial to think of creativity as a research domain within which multiple constructs exist. Specifically, we postulate two separate sets of constructs, behaviors and outcomes, and we clarify the key definitional differences and causal direction between them. Second, we provide conceptual clarity to each of these constructs by



1364 Journal of Management / July 2012

distinguishing between creative behaviors that are expected versus those that are unexpected and between the novelty and usefulness dimensions of creative outcomes. We highlight support for and the significant implications of these distinctions. Third, we show how this framework can be used to explain some discrepant findings in the area. Fourth, we draw attention to related problematic issues in the measurement of creative behaviors and outcomes such as contamination, deficiency in some studies, and low fidelity in others, and we make recommendations to help overcome these problems. Finally, we discuss some important new research directions and practical lessons implied by the framework.

Adapting a Criterion-Centered Framework to Workplace Creativity Research

Research on workplace creativity has traditionally faced a criterion problem (Amabile, 1983a)—the failure to recognize, both theoretically and empirically, the multidimensional (e.g., quantity and quality) and multifunctional (e.g., behavior and outcome) nature of workplace performance criteria (Austin & Villanova, 1992). Evidence of a creativity criterion problem emerges in three existing practices. First, although considered a unitary construct in many studies, creativity has been conceptualized and measured in terms of both performance (i.e., behaviors) and effectiveness (i.e., outcomes of these behaviors). The widely accepted definition of creativity as referring to the production of novel and useful ideas conceptually confounds behaviors (i.e., producing or generating ideas) with the outcome of these activities (i.e., the ideas themselves judged as both novel and useful; Amabile, 1996). Likewise, frequently used measures of creativity (e.g., George & Zhou, 2001) empirically confound creative performance behaviors (e.g., generating ideas) with judgments of the novelty and usefulness of the outcomes produced by these behaviors. Second, despite references to creative performance that is expected (e.g., Dewett, 2007; Tierney & Farmer, 2004) and creative performance that occurs without expectation (e.g., Alge, Ballinger, Tangirala, & Oakley, 2006; Janssen, 2000), both forms of creative performance are generally treated as the same construct (cf. Unsworth, 2001). Finally, there is often a mismatch between construct definitions and the measurement of creativity (see Sullivan & Ford, 2010). For example, even though most researchers define creativity with two unique dimensions (novelty and usefulness), overall indicators are often used.

This seeming confusion regarding the creativity criterion space is similar to the criterion problem once faced in job performance research. Historically, researchers focused their efforts on isolating the key antecedents of job performance (Cascio & Aguinis, 2008) without giving equivalent attention to the criterion itself (Campbell, 1990). After decades of using different operationalizations interchangeably, which created conceptual and empirical confusion, theoretical frameworks have now been applied to clarify the job performance criterion space (e.g., Bartram, 2005; Beal, Weiss, Barros, & MacDermid, 2005; Borman & Motowidlo, 1997; Borman, White, Pulakos, & Oppler, 1991; Campbell, McCloy, Oppler, & Sager, 1993; Motowidlo & Van Scotter, 1994; Organ, 1997; Van Dyne & LePine, 1998). Applying such conceptual frameworks redirected attention toward collecting validity evidence for the criterion itself (e.g., Binning & Barrett, 1989) and increasing the validity of




nonobjective criterion measures (e.g., Borman, 1991). Furthermore, applying these frameworks has led to a number of key insights on important workplace relationships, such as the link between job performance and job satisfaction (Organ, 1977). Based on this success, it seems that lessons learned from the job performance literature may be meaningfully applied to resolve current criterion issues in the creativity research domain.

Among the various criterion frameworks that have been established in the broader performance research, the one developed by Campbell and colleagues (e.g., Campbell, 1990; Campbell et al., 1993; Campbell, McHenry, & Wise, 1990; McCloy, Campbell, & Cudeck, 1994) is particularly prominent. At the heart of this framework is the notion that the workplace criterion space includes both performance behaviors (i.e., behaviors directed toward achieving organizational goals) and outcome effectiveness (i.e., the evaluation of the outcomes of these behaviors along various dimensions such as quantity or quality), two distinct constructs. Making this distinction is essential for at least three reasons: (1) to properly evaluate the critical causal influence of behaviors on outcomes, (2) to recognize that performance behaviors of multiple individuals can jointly cause the effectiveness of one outcome, and (3) to highlight that there are a number of environmental factors outside of employees’ control (e.g., trends, market shifts) that may help drive outcome effectiveness (Campbell, 1990; Motowidlo, Borman, & Schmit, 1997). For example, sales representatives typically are evaluated based upon gross sales volume. Yet, this outcome may be partially dependent on the behaviors of other staff employees, and sales might slump due to economic conditions, changes in customer preferences, or production bottlenecks. Sales behaviors may be good, yet sales outcomes can still be poor. Given this reasoning, it is theoretically and empirically essential to distinguish between performance behaviors and outcome effectiveness no matter which part of the overall workplace criterion space one studies, including the creativity criterion space.

We contend that creativity research will benefit from more explicitly distinguishing between performance behaviors and outcome effectiveness as criteria. Recognizing this distinction is basic to studies of causal paths and sequences but often can be forgotten in definitions and measures when the research focus tends toward empirical prediction. Thus, building on Campbell and colleagues, we define creative performance behaviors (CPBs) as the set of interdependent observable and unobservable activities that occur in response to a nonalgorithmic task or project and that purportedly constitute the creative process (Eindhoven & Vinacke, 1952; Guilford, 1950; Lubart, 2000-2001; Wallas, 1926). Creative outcome effectiveness (COE) is defined as the extent to which the outcomes (idea, prototype, product, etc.) of nonalgorithmic task or project completion are judged by relevant stakeholders to be both novel and useful (Amabile, 1983b, 1996). Weak to moderate correlations between CPBs and COE also offer some support for their distinctiveness as constructs (e.g., Amabile, Barsade, Mueller, & Staw, 2005; Binnewies, Ohly, & Sonnentag, 2007; Frese, Teng, & Wijnen, 1999; Scott & Bruce, 1994; Tierney, Farmer, & Graen, 1999). For example, Tierney et al. (1999) reported modest correlations (rs = .28–.29) between CPBs (measured with supervisor ratings of employee behaviors) and COE (measured via invention disclosure forms and research reports) in a sample of 191 R&D employees. Thus, both theoretical and empirical evidence seem to substantiate this main distinction.

In addition to distinguishing behaviors from outcomes, previous research on job performance highlights two other distinctions that have the potential to clarify weak or




discrepant findings in this literature—the distinctions (1) between behaviors that are an expected part of an employee’s formal role description versus those that are more voluntary and exceed an employee’s role description (e.g., Katz, 1964; Katz & Kahn, 1978) and (2) between the multiple dimensions of outcome effectiveness (e.g., quantity vs. quality). Extant work in the creativity research area often forgets these distinctions in practice, despite the theoretical argument that expected and unexpected creative behaviors differ (Unsworth, 2001) and the empirical evidence that novelty and usefulness capture unique aspects of effectiveness (Beersma & De Dreu, 2005; Rietzschel, Nijstad, & Stroebe, 2007). We discuss the evidence for these additional distinctions and their importance in the next two sections.

Creative Performance Behaviors

Research on creativity from the process perspective, which defines creativity as the “sequence of thoughts and actions that leads to novel, adaptive productions” (Lubart, 2000-2001: 295), primarily informs our understanding of CPBs. Historically, process or stage models have posited that the creative process unfolds in a sequential order. However, evidence for a standard sequence is mixed (e.g., Eindhoven & Vinacke, 1952; Patrick, 1935, 1937), which has led many to suggest that CPBs may not unfold according to a standard sequence and that sometimes multiple CPBs may occur simultaneously (Csikszentmihalyi & Getzels, 1971; Eindhoven & Vinacke, 1952). Thus, rather than focus on a sequence of activities, it appears more beneficial to focus on the nature and types of CPBs that predict variation in COE (Lubart, 2000-2001).

CPB Categories

When determining the behavioral categories to include in our discussion, we sought to differentiate CPBs from innovative performance behaviors and problem-solving behaviors. With regard to innovation, extant research typically defines innovation as the development and implementation or application of a novel, useful idea (Janssen, 2000; West & Anderson, 1996). Thus, implementation behaviors (e.g., championing ideas), which are unique to innovation, were excluded to maintain a conceptually clear distinction between innovation and workplace creativity. With regard to problem-solving behaviors, the extant literature does not seem to make clear delineations regarding the types of behaviors necessary for each process. Although theoretical arguments have been made regarding the distinctiveness of creativity from problem solving based on the nature of the task or the outcome (Lubart, 2000-2001; Mumford, Mobley, Uhlman, Reiter-Palmon, & Doares, 1991), models that outline behaviors relevant for problem solving tend to overlap with models of creative behaviors. Thus, we do not make any definitive distinctions in this respect.

To inform our analysis of which types of behaviors to incorporate in our framework as CPBs, we reviewed the top-five most cited (according to Social Sciences Citation Index) creativity and innovation process models (see Table 1). Although some models include as few as two types of behaviors (Basadur, 1994) and others as many as eight (Mumford et al., 1991; Mumford, Supinski, Baughman, Costanza, & Threlfall, 1997), a classification of four




types is the most strongly supported in the literature to date (e.g., Amabile, 1988; Guilford, 1950; Lubart, 2000-2001; Reiter-Palmon & Ilies, 2004; Wallas, 1926). Thus, in our framework, the CPB categories include problem definition behaviors (e.g., problem identification), preparation/information-gathering behaviors (e.g., category search, information search), idea generation behaviors (e.g., divergent thinking, conceptual combination), and idea evaluation behaviors (e.g., idea selection, idea refinement).

Problem definition/formulation includes the behaviors of identifying or formulating a problem or opportunity that requires an unknown solution (Guilford, 1950). The preparation/information-gathering behaviors have often been thought of as a precursor to the development of a novel connection or as the period of time during which information incubates in a person’s mind (e.g., Guilford, 1950; Wallas, 1926). However, this CPB type also involves active and intentional efforts to search for and acquire new information. Intentional information-gathering behaviors can occur internally in a person’s memory or externally by seeking new sources of input information (e.g., ideas from group members; Nijstad & Stroebe, 2006). Idea generation behaviors develop ideas through making new mental connections relevant to the creative task or problem. This activity is associated with divergent thinking and is emphasized in techniques such as brainstorming (Osborn, 1953), where the goal is usually to generate as many novel ideas as possible. Idea evaluation/validation behaviors include making conscious judgments or subconscious screenings based on the utility of an idea by forecasting the implementation of the idea or outcome (Mumford, Lonergan, & Scott, 2002). This type of behavior includes accurately choosing the most creative idea from among multiple options (Dailey & Mumford, 2006; Mumford et al., 2002; Silvia, 2008). When discussing the higher order construct of CPBs, it is these four categories that are the formative dimensions. We do not claim that this list of CPB types is necessarily comprehensive; however, it appears that these four categories enjoy the greatest consensus among scholars.

Table 1Four Creative Performance Behaviors Types as Identified in

the Most-Cited Creative Process Models

Reference

Problem Formulation/

Definition

Preparation/Information Gathering Idea Generation

Idea Evaluation/Validation

Amabile, 1983b, 1988, 1996

Task presentation Preparation Response generation

Response validation

Baughman & Mumford, 1995

Problem construction

Information encoding, category search and selection

Conceptual combination

Idea evaluation

Drazin, Glynn, & Kazanjian, 1999

– – Idea generation Idea testing

Patrick, 1935, 1937 – Preparation Illumination VerificationWallas, 1926 Preparation Incubation Illumination Verification




Expected Versus Unexpected CPBs

Unsworth (2001) suggested that people would perform CPBs as the result of either internal (e.g., self-determined choice) or external (e.g., situational requirement) drivers, where expected CPBs are externally driven and unexpected CPBs are internally driven. Empirical studies also suggest that these may be opposite ends of a continuum (e.g., Shalley, Gilson, & Blum, 2000; Yuan & Woodman, 2010). When there is a clear, valid job or role description, determining expected versus unexpected CPBs is more straightforward. If this is not the case, making this distinction becomes more difficult and another cue is needed. In this vein, we propose that CPBs are externally driven, or “expected,” when punishment could reasonably ensue if CPBs are not performed. CPBs are internally driven, or “unexpected,” when an employee chooses to perform CPBs without any fear of punishment for nonperformance of these behaviors. Punishment is a more reliable indicator of expectation than rewards are, because rewards are regularly administered for expected (e.g., contractual bonuses) or unexpected (e.g., spot bonuses) behaviors. Also, rewards may not be forthcoming for expected behaviors. Thus, besides CPBs being specified or unspecified in a role description, we reason that, from the employee’s perspective at least, the prospect of punishment is the best available indicator of whether a work behavior is considered expected or unexpected.

Despite the face validity of this distinction, in most studies there has been a lack of attention to whether or not creative performance is expected. Researchers collect data from employees with a variety of job roles that may require creative performance to a varying extent (e.g., George & Zhou, 2007), but little distinction is made between contexts in which CPB performance is explicitly mandated (e.g., R&D; Dewett, 2007; Tierney & Farmer, 2004) and contexts in which this performance is clearly unexpected or explicitly labeled as discretionary (e.g., Alge et al., 2006; Janssen, 2000). We see value in this distinction, and we suggest that the relative lack of attention to this distinction, long recognized in the larger job performance literature (i.e., in-role vs. extra-role behaviors), may have hidden differential predictors of expected versus unexpected CPBs and may have inhibited the resolution of seemingly inconsistent findings on extrinsic motivators.

First, some empirical evidence suggests that expected versus unexpected CPBs may be predicted by different antecedents. Studies tend to show that experience and perceived ability may predict expected CPBs (e.g., Gong, Huang, & Farh, 2009; Shin & Zhou, 2007; Tierney & Farmer, 2002, 2004, 2011). Gino, Argote, Miron-Spector, and Todorova (2010) conducted an experiment to examine the effects of experience on team creativity in a product development task, which explicitly required the performance of CPBs. The authors found that task experience positively related to COE when CPBs were expected. In examining the antecedent of perceived ability, Tierney and Farmer (2002) reported the results of two different samples—a manufacturing sample with no requirement to perform CPBs and an operations sample with some CPB performance expected. Findings suggest that perceived ability may better predict creative performance in occupations where it is expected (β = .26) as compared with environments in which such performance is unexpected (β = .09). Other research shows that feeling empowered and having control may motivate employees to engage in unexpected CPBs (e.g., Alge et al., 2006; Axtell et al., 2000; Gong, Cheung,




Wang, & Huang, 2010; Pieterse, van Knippenberg, Schippers, & Stam, 2010). Research on suggestion programs indicates that shop floor employees make suggestions more when they have autonomy, feel ownership over the results of production, and can participate in decision-making processes (Axtell et al., 2000). This evidence, along with previous theoretical work from creativity research and job performance research (Smith, Organ, & Near, 1983; Unsworth, 2001), seems to suggest that experience, knowledge, and skills or abilities may be particularly strong predictors of CPBs when such engagement is expected, whereas motivation constructs may be stronger predictors of CPBs when such engagement is not expected. This is a key hypothesis for creativity research to test in clarifying the relevant nomological net.

Second, the expected–unexpected distinction may shed light on the mixed pattern of findings surrounding research on the effects of extrinsic motivators on creativity. In some studies, empirical evidence points to a positive or null effect of extrinsic motivators on CPBs and subsequent COE (e.g., Amabile, Hennessey, & Grossman, 1986; Eisenberger, Haskins, & Gambleton, 1999; Shalley, 1991, 1995; Shipton, West, Dawson, Birdi, & Patterson, 2006). Shalley (1991,1995) presented evidence from two experiments that showed that the presence of external evaluation had either no effect or a positive effect on COE. Yet other studies suggest that such extrinsic motivators can undermine CPB performance and result in lower COE (e.g., Amabile et al., 1986; Hennessey & Amabile, 1998). Amabile et al. (1986), for example, presented evidence for a decrement in COE when contingent rewards were presented and participants had a choice to perform the task.

We contend that considering the nature of expected versus unexpected CPBs in combination with motivation theory can help explain these seemingly inconsistent findings. According to cognitive evaluation theory (Deci, 1975), extrinsic motivators (e.g., rewards) encourage task-oriented thinking (Amabile et al., 1986), which will deter a person from thinking creatively. This logic, however, only holds when task-oriented thinking and creativity-oriented thinking diverge, which is most likely when CPB performance is unexpected. In situations where performing CPBs is expected and necessary (e.g., directing a movie), then extrinsic motivators should not unduly deter creative thinking because task-related thinking and creative thinking converge. In fact, when a task and the relevant CPBs associated with it are expected, rewards and other extrinsic motivators (e.g., appraisals, bonuses, raises, promotions, or recognition) are often contractually specified or expected by tradition, and their absence could even contribute to lower work satisfaction and engagement over time (e.g., not paying a movie director when she or he expects it is unlikely to aid creativity). Thus, for expected creative tasks, extrinsic motivators would generally have a positive or null effect on CPB performance, but for unexpected CPBs, extrinsic motivators would generally have a negative effect on COE. This working hypothesis merits a future program of research in order to establish more clearly if and when managers can use extrinsic motivators effectively to improve workplace COE.

Creative Outcome Effectiveness

COE is defined as the joint novelty and usefulness (i.e., quality) of a product or service or of an outcome as judged by relevant stakeholders (e.g., experts, customers, managers;




e.g., Amabile, 1983b, 1988; Madjar & Oldham, 2006; Oldham & Cummings, 1996; Shalley, 1995; Shalley et al., 2004; Simonton, 2003; Tierney et al., 1999). COE judgments are essentially subjective and context dependent (Amabile, 1983a; Hennessey & Amabile, 2010). Judgments may change based on both the type of judge and/or the features of the context such as time or location of the judgment. Different stakeholder groups may judge COE differently and/or differ on the level of COE for a given outcome. Customers of a new product, who generally do not know or understand the many new intricacies of the product, may judge COE quite differently than a design expert in the field would. Furthermore, the judgment of novelty may change based on the context, even at the same point in time. Starbucks’ idea to sell lattes or espressos in addition to traditional coffee was novel for the United States at the time, but not for Italy, where this had already been a common practice in most cafés. These same contextual issues exist when determining the usefulness of an outcome. For example, a fuel-efficient car in the 1960s was seen as less useful because such cars were often criticized for their lack of power, but today due to fuel price increases, such cars are considered highly useful.

Distinguishing Idea Generation From COE

Historically, many researchers seem to have considered the generation of ideas as synonymous with COE (Reiter-Palmon & Ilies, 2004). According to our conceptualization, a count of the number of ideas produced by an individual or team—an often-employed measure of creative productivity—generally indicates the behavior of idea generation (i.e., CPB). However, we acknowledge that each of the ideas produced could potentially be judged in terms of its novelty and usefulness, with these judgments becoming indicators of the effectiveness of the creative outcome (COE). Given this, the conceptual distinction between the measurement of an idea as reflecting a CPB or as indicating COE may become blurred. We now offer an option for clarifying this. We recommend that measures such as the number of ideas produced during a brainstorming session, or the average originality of these ideas, should be considered indicators of the CPB of idea generation—not of COE. We consider the judgment of ideas generated as appropriate indicators of COE only if (1) there has been an explicit judgment by relevant stakeholders of the ideas (e.g., end users) and (2) each idea is perceived to be at least an interim problem solution (i.e., it could potentially be implemented as a solution to the creative task or project in question). Therefore, ideas generated during a brainstorming session would generally not qualify as creative outcomes because the ideas generated are typically not final solutions in themselves but, rather, building blocks toward another creative outcome. Although somewhat complex, to fully distinguish CPBs from COE in the creativity literature we recommend that this definitive line on the status of “ideas” be drawn and followed, at least until a theoretical argument or empirical evidence to the contrary surfaces.

Novelty and Usefulness as Formative Dimensions of COE

Nearly all conceptualizations of COE define it as two dimensional: novel (also termed “new” or “original”) and useful (also termed “practical,” “appropriate,” or “valued”; see




Sullivan & Ford, 2010). Novelty is necessarily balanced by usefulness to ensure that the product or service creates value. Even an outcome that appears to derive its creativity predominantly from the novelty component (e.g., artwork) cannot be considered creative unless it generates some value (e.g., aesthetic pleasure) from its stakeholders (e.g., art collectors, gallery owners, etc.). Conversely, even an extremely useful outcome must contain some novelty to be distinguishable from outcomes of more algorithmic types of work activities. Thus, a zero on either dimension would theoretically indicate a zero level of COE.

Novelty and usefulness are conceptually and empirically distinct formative dimensions of COE (Sullivan & Ford, 2010). Novelty and usefulness combine to form the higher order construct labeled COE as opposed to these dimensions working as potential overlapping indicators (i.e., as in a reflective model). Many constructs are assumed to be reflective in nature, but this type of measurement model is not always appropriate (MacKenzie, Podsakoff, & Jarvis, 2005). Extant creativity research appears to make this assumption by averaging across a number of items (see Sullivan & Ford, 2010). With a reflective model, the indicators must co-vary with one another such that they are interchangeable (Jarvis, MacKenzie, & Podsakoff, 2003). For COE though, substantial empirical evidence shows that novelty and usefulness load on separate measurement factors and often do not correlate significantly (Amabile, 1983a, 1985, 1996; Amabile et al., 1986). Clearly then, novelty and usefulness are not interchangeable dimensions, and COE is not reflective but is a formative construct, which Sullivan and Ford (2010) empirically support in their structural equation model.

The first implication of classifying COE as a formative construct is that each dimension must be considered as theoretically and empirically distinct (e.g., Litchfield, 2008). If not, valuable information may be lost in terms of their unique relations with antecedents and outcomes. Grant and Berry (2011) suggested that, while intrinsic motivation likely drives the novelty component of COE, “perspective taking,” as generated by prosocial motivation, likely drives the usefulness component. Consistent with this, Beersma and De Dreu (2005) reported experimental evidence that prosocial groups—manipulated by having all group members work toward a collective goal on a negotiation task—produced more useful slogans than “proself” groups did. This study also found that proself groups—manipulated by directing group members toward a self-serving goal—tended to produce more novel slogans than prosocial groups did. In addition, there is evidence to suggest that novelty and usefulness may differentially predict unique variance in certain stakeholder behaviors and reactions. For example, recent research on consumer behavior found that ad novelty predicted an individual’s level of attention to an ad, and ad usefulness predicted motivation and depth of processing, brand attitude, and purchase intentions (Smith, MacKenzie, Yang, Buchholz, & Darley, 2007; Smith & Yang, 2004). These findings suggest that novelty and usefulness may be caused by different mechanisms and may even predict different outcomes, underscoring the need to treat these dimensions separately in models and empirical studies.

A second implication of COE as a formative construct is that variations in the weighting of the dimensions of novelty and usefulness change the meaning of the construct. Although judges clearly do combine novelty and usefulness judgments to form COE, how they do this and the relative weightings that they use for each dimension are completely unclear and unstudied in the creativity area. This is an important omission because each judge may use unique schemes for weighting the dimensions when making his or her judgments. Even when COE level may be




quantitatively the same across individuals, the relative weight of novelty and usefulness may vary according to each judge, thereby altering the nature of the construct. Further, if judges rate only overall COE rather than the independent dimensions, this results in idiosyncratically combining the dimensions of novelty and usefulness (e.g., Eisenberger & Aselage, 2009; George & Zhou, 2001, 2002; Gilson, Mathieu, Shalley, & Ruddy, 2005; Madjar, Oldham, & Pratt, 2002; Perry-Smith, 2006; Zhou & George, 2001), and the process and weighting of the combinations remain completely unknown. Thus, it is vital for future research to examine novelty and usefulness separately (instead of using overall unitary measures of COE) order to explicitly study the weighting applied to each dimension, and thereby know the meaning of COE more precisely, instead of accepting that this weighting and combination of novelty and usefulness reside within a “black box” not to be studied.

Questions of what weighting schemes judges or raters actually use can be studied through traditional policy-capturing methods where overall COE must be measured as the regression criterion along with separate measures of novelty and usefulness dimensions. Additionally, to empirically derive a weighting scheme that will maximize prediction of key outcomes, it is necessary to examine how the dimensions of novelty and usefulness predict both overall COE and more distal criterion variables (e.g., sales) in the same analysis (Franke, Preacher, & Rigdon, 2008). Using this approach, the relative weight for each dimension is derived from its statistical loading (i.e., beta weight) on the latent COE construct when it is predicting distal criterion variables (see Franke et al., 2008; Ruiz, Gremler, Washburn, & Carrion, 2008). Future research could use these approaches and others to study weighting/combination schemes of COE dimensions across various workplace contexts. In any case, this will help researchers better understand the meaning of COE (e.g., there may be different combinations of novelty and usefulness used for different work contexts) and how these dimensions of COE best combine to predict important organizational outcomes (e.g., sales).

Clarifying the Causal Relationship Between CPBs and COE

In describing the causal relationship between CPBs and COE, scholars must ultimately embrace one of two arguments: (1) CPBs depend on COE for their existence and definition (i.e., COE → CPBs), or (2) CPBs exist prior to and independently of COE and thereby can cause COE (i.e., CPBs → COE).

The first causal argument, COE causes CPBs, essentially means that we cannot observe CPBs without first identifying an outcome as creative. Amabile stated, “The identification of a thought process or subprocess as creative must finally depend upon the fruit of that process—a product or response” (1983a: p.33). By this logic, determining and examining process-related CPBs can only occur retrospectively after COE has been established, and consequently creative performance is simply attributed to relevant past behaviors. This implies that many behaviors (e.g., conceiving an idea or stealing an idea) can be labeled as a CPB so long as it can be associated with COE through some causal chain. Further, two identical behaviors could be CPBs one day because they are attributed to COE, and not the next day when no COE can be linked to them. This would suggest that CPBs are quite subjective and transient and are not conducive to systematic study across situations.




According to the second argument, CPBs can exist independently of COE and cause COE. This means that CPBs can be observed and identified in the moment of occurrence rather than having to be identified retrospectively. In other words, it is not necessary for a creative outcome to exist in order for a person to perform CPBs. Instead, individuals would recognize the CPB performance based on the nature of the task and the observed behavior. This argument also implies that many CPBs may not “succeed” in producing high COE. We believe this second argument is theoretically more valid and more conducive to systematic study.

Some existing empirical work supports the CPBs → COE causal direction (e.g., Amabile et al., 2005; Binnewies & Wörnlein, 2011; Zhang & Bartol, 2010a). Amabile et al. (2005), for example, collected data from 222 employees working on projects that required creativity. They measured daily self-reported creative thought during these projects—thoughts about “(1) a discovery, insight, or idea; (2) the act of searching for a discovery, insight, or idea; (3) solving a problem in a non-rote way; or (4) the act of searching for a problem solution in a non-rote way” (Amabile et al., 2005: 380)—as a higher order indicator of CPBs over one month. At the end of one month, they used coworker ratings of the outcome produced by each employee to measure COE. Averaged daily CPBs correlated weakly with COE (r = .16), supporting the conclusion that these two concepts are distinct but related and that CPBs temporally precede COE—two necessary conditions for causality. Similarly, Binnewies et al. (2007), using self-report over the course of a month, examined the extent to which nurses performed a set of CPBs in developing creative solutions for a problem. At the end of the month, these authors linked these behaviors to the effectiveness of the creative outcome by measuring COE with expert ratings of the solution descriptions. They found a positive association between the information-gathering CPB and the COE of the proposed solutions. Such longitudinal findings also support the second causal direction.

Although laboratory research examining the causal relation between CPBs and COE is typically limited to the manipulation of one specific type of behavior at a time, a number of laboratory studies by Mumford and colleagues (Baughman & Mumford, 1995; Dailey & Mumford, 2006; Friedrich & Mumford, 2009; Lonergan, Scott, & Mumford, 2004; Mumford, Baughman, Maher, Costanza, & Supinski, 1997; Mumford, Baughman, Supinski, & Maher, 1996; Mumford, Baughman, Threlfall, Supinski, & Costanza, 1996; Mumford, Supinski, Threlfall, & Baughman, 1996) tend to support the second argument as well. For example, Mumford, Baughman, et al. (1997) found that using metaphors (an idea generation CPB) increases later COE. Additionally, Mumford et al. (2002) examined the effects of the criteria used when evaluating ideas (a CPB) and found that selecting ideas based on their financial viability predicted lower COE novelty ratings. Given this evidence and the insights gained from field research, it appears likely that the second causal argument, which is in alignment with the predominant model of job performance (Campbell et al., 1993), is more valid and useful for studying workplace creativity. Thus, we suggest that future research in this area should formally adopt this causal logic.

One important implication of the CPBs → COE causal relationship is that a number of commonly examined predictors of COE (e.g., workplace creative climate) are likely to only impact it indirectly—to the extent that they directly influence CPBs. For example, intrinsic motivation, a key ingredient to creativity (Amabile, 1983b, 1988), is likely to predict the




engagement of certain CPBs without having a direct effect on COE. Given the direction of the causal effect of CPBs on COE, it may be safe to assume that the effects on COE of these previously studied antecedents are mediated by changes in the various CPBs. Despite the strong basis for this causal assumption, little research has examined these mediated paths, and thus this area seems ripe for future research.

To be theoretically comprehensive, research should also recognize that factors other than CPBs might cause COE. Workplace outcomes are not solely the result of engagement in a certain set of performance behaviors; rather, they result from the joint effects of these behaviors (which are under the control of the employee) and contextual factors largely outside of the control of the employee (e.g., trends, market shifts; Campbell, 1990; Motowidlo et al., 1997). Thus, some antecedents may predict COE without directly affecting CPBs. For example, Tierney et al. (1999) found that the division in which an employee worked did not correlate with CPB ratings but was associated with COE as measured via ratings of research reports. This reinforces that COE is an essentially context-based construct that can also be influenced by factors unrelated to CPBs (e.g., environmental constraints, various rater errors).

Finally, researchers must remember in their modeling efforts that multiple people or multiple sets of people often perform different CPBs over time, resulting in one outcome judged for COE. Given recent research highlighting the social and collective nature of creativity (Baer, 2010; Burt, 2004; Hargadon & Bechky, 2006; Perry-Smith, 2006) and the prevalence of teams within contexts where creativity is in high demand (e.g., design, product development, etc.), it is conceivable that the various CPBs are exhibited not necessarily by one individual but, rather, by a collection of individuals. For example, teammates involved in identifying or formulating an initial problem may not be tasked with idea evaluation. We propose here that, regardless of the constellation of individuals working on a creative task, it is the extent to which and the quality by which the various CPBs are performed that cause COE variation. Thus, the task is the most useful unit of analysis when studying the causal process, rather than the individual or the team.

Measurement of CPBs and COE

The main thesis of this review is that CPBs and COE are unique, causally related constructs in the creativity criterion space. Clarity and refinement in the measurement of CPBs and in COE are necessary components of clarifying this criterion space. In this vein, we next review common measurement practices (see also Zhou & Shalley, 2003, 2011), present evidence that the use of some practices may limit inferences drawn, and offer recommendations for overcoming these shortcomings.

Common Creativity Measurement Practices

Efforts to examine CPBs in the laboratory typically involve participants writing down lists of ideas, using several prompts, including generating possible titles to a short story (Eisenberger & Aselage, 2009) or generating ideas on how to preserve the environment (Nijstad, Stroebe, & Lodewijkx, 2003). In this case, researchers can rely upon the observable




behavioral manifestations (e.g., writing down or articulating an idea) of a generally unobservable CPB (e.g., thinking of a new idea) for measurement (Campbell, 1990). The written ideas are then counted (i.e., fluency) and coded for the average within-sample response infrequency (i.e., originality), the number of categories from which ideas were generated (i.e., flexibility), or the average amount of description used for each idea (i.e., elaboration)—each an indicator of CPB idea generation (Beersma & De Dreu, 2005; Choi & Thompson, 2005; Connolly, Jessup, & Valacich, 1990; Eisenberger & Rhoades, 2001; Madjar & Oldham, 2006; Nijstad et al., 2003; Pearsall, Ellis, & Evans, 2008). In field studies, researchers most often use self-report, coworker, or supervisor ratings of CPBs that occur in the course of work performance (e.g., George & Zhou, 2001; Janssen, 2000; Scott & Bruce, 1994; Tierney et al., 1999).

Though some studies have measured COE via objective indicators, such as number of research reports or patent applications (Oldham & Cummings, 1996; Tierney et al., 1999), COE is often measured via judgments from subject matter experts using the consensual assessment technique (CAT; Amabile, 1983a). Judgments using CAT have been applied in laboratory studies, with outcomes such as collages (Amabile et al., 1986) and marketing campaigns (Mumford, Baughman, et al., 1997), and in field studies, with outcomes such as organizational improvements (Binnewies et al., 2007). This method, developed and validated by Amabile and colleagues (e.g., Amabile, 1996), specifies the use of judges who are familiar with the domain of the creative task or product, because they can rely on their implicit definitions of novelty and usefulness. In essence, this technique functions on the assumption that those familiar with the domain have similar implicit prototypes or theories of creativity and that they will therefore agree in their ratings. Empirical evidence has supported agreement between expert raters in samples including artists judging paintings, writers judging poems, and graduate students rating problem-solving solutions (Amabile, 1996).

Validity Concerns With Common Creativity Measurement Practices

Based on our review of the common measurement practices in contemporary creativity research, we identify three concerns that may undermine the ability of existing measures to produce internally and externally valid conclusions (Borman, 1991). First, some common measures used in applied studies suffer from potential contamination (e.g., George & Zhou, 2001; Tierney et al., 1999). For example, a commonly used creative performance scale is George and Zhou’s (2001) 13-item measure (Alge et al., 2006; George & Zhou, 2001, 2002, 2007; Gong et al., 2009; Shin & Zhou, 2003; Zhang & Bartol, 2010a, 2010b; Zhou, 2003; Zhou & George, 2001). Although this measure tends to exhibit acceptable reliability, it includes some potentially contaminating items, jeopardizing the construct validity of the scale. For example, items such as, “[My employee] is not afraid to take risks” taps the individual difference trait of risk taking, which is not part of the definition of the construct of CPB nor part of the traditional definition of creativity as referring to ideas that are both novel and useful. Moreover, other researchers (e.g., Dewett, 2007) have measured risk taking separately from CPBs, substantiating that this construct is both conceptually and empirically distinct from CPBs. Based on the application of our




framework, we believe that the continued use of items that capture concepts outside the formal definition and specified categories of CPBs are likely to cause ambiguity regarding the inferences drawn in such studies.

Second, both laboratory and field studies tend to use limited measures of CPBs without a compelling reason. According to our review, existing models tend to reference four categories of behaviors relevant in the production of creative outcomes (i.e., idea generation, problem definition, information gathering, idea evaluation). Because the higher order construct of CPBs consists of these four types of behaviors, studies examining this higher order construct should include indicators for each behavior category. However, most laboratory studies only simulate and measure idea generation, and field studies rarely measure all relevant CPBs simultaneously. Zhou and Shalley (2011) identified a number of commonly used scales in creativity research, three of which, at least partially, capture creative behaviors (George & Zhou, 2001; Scott & Bruce, 1994; Tierney et al., 1999). All of these scales fail to include items that measure information gathering and/or idea evaluation, making them incomplete when the purpose is to examine CPBs generally or to examine the entire creative process for a given task or project.

Regarding COE, when quantitative indicators such as the number of patent applications or the number of suggestions are used to measure COE, the comparisons one can make with quality indicators such as expert judgments may be limited. Previous research has long suggested that quantity and quality are two separate, and sometimes conflicting, dimensions of outcome effectiveness (Woodworth, 1899). Oldham and Cummings (1996) reported a weak correlation between a quantitative indicator of the number of patent applications and a quality indicator from supervisor ratings of COE (r = .23). Given that COE is better represented by quality, purely quantitative measures may miss important information about the construct (i.e., three highly creative patents are not the same as three minimally creative patents).

Third, many studies, particularly those conducted in the laboratory and employing rather simple tasks suffer from low fidelity, a problem when determining the external validity of the conclusions drawn from these studies. External validity limitations can stem from the nature of the participants (e.g., Would age impact the expected effect?), the setting (e.g., Would the artificial and short-term setting impact the expected effect?), the manipulation (e.g., How realistic is the manipulation?), and the measures of behavior (e.g., Does the experiment allow for observation of the behavior as defined?) for explaining the real-world effects (Colquitt, 2008). For creativity research, experimental designs are primarily limited by the behavior measures utilized in the research design. In organizations, employees perform CPBs in response to complex tasks that generally require a high level of domain expertise. In order to design a new soft drink, for instance, a person must have general chemistry knowledge as well as deep knowledge of the current soft drink market. In laboratory settings, the prompt for which a participant must generate ideas usually requires, by design, little familiarity and no expertise within a certain work domain. Generating alternative uses for a paperclip, for example, does not require domain-relevant expertise. As a result, the processes and behaviors elicited could be very different from those that naturally emerge for tasks or projects in settings such as R&D.




Recommendations

To address these validity concerns in the measurement of CPBs and COE, first and foremost, it is necessary for research to conceptually and empirically separate CPBs and COE—a key omission in most existing creativity research studies (for exceptions, see Amabile et al., 2005, and Binnewies et al., 2007). Moreover, scholars should ensure that both CPBs and COE are measured exclusively (no contamination) and comprehensively (no deficiency for the purpose of the study).

In the case of treating CPBs as a higher order construct, it would be necessary to measure indicators of all categories of CPBs. To demonstrate construct validity, each indicator of each behavior would map onto its specific latent construct, and these latent constructs would then form the higher order latent construct of CPBs (e.g., Binnewies et al., 2007; Zhang & Bartol, 2010a, 2010b). It is understandable that in some circumstances a researcher is interested only in examining a subset of CPBs, but it is important to make the appeal for comprehensive measurement in any empirical research examining the overall creative causal process. This is because the CPBs likely operate interactively within a given task or project and because the area currently has a limited understanding of the relative impact of different CPBs on COE (cf. Binnewies et al., 2007).

Similarly, we recommend that measures of COE include separate indicators for novelty and usefulness. If the scope of a study is constrained to examining one identifiable creative outcome, researchers may collect subjective judgments of novelty and usefulness for the outcome as measures of COE at the end of the task or project. If multiple outcomes are possible or expected, however, both quantity and quality indicators could be used. Generally though, COE is an inherently subjective and qualitative construct, and using quantitative measures or counts of outcomes would be limited as measures and in need of quality indicators as supplements.

To further counter the problem of contamination, future applied research may benefit from collecting episodic data on CPBs. Within a creative task or project, CPBs are likely to occur in discrete episodes over time. A performance episode begins when individuals initiate their thoughts and behaviors around a specific nonalgorithmic task and ends when that process is interrupted (Beal et al., 2005). Precisely tracking these CPB episodes and their causal linkages over time can capture both meaningful within- and between-person variations (Beal & Weiss, 2003). Researchers could also use momentary assessments of CPB episodes in the workplace as they occur (e.g., Amabile et al., 2005), reducing the errors associated with retrospective reports of CPBs (e.g., Beal & Weiss, 2003; Tennen, Affleck, Armeli, & Carney, 2000) often used in creativity studies (e.g., Madjar et al., 2002; Shin & Zhou, 2003).

To address the concern of lack of fidelity, an issue primarily facing laboratory research, future research could ameliorate this problem by having participants perform multiple CPBs within one specific and complex creative task (e.g., in-basket task; Yuan & Zhou, 2008). While the time frames of such tasks are still likely to be shorter than those that employees would encounter in organizational settings, researchers could simulate the workplace experience by instructing participants to engage in all relevant CPBs in order to complete the task. Measures for each CPB could include, for example, the amount of




information gathered, the number of ideas generated, and the accuracy or comprehensiveness of the idea evaluation, or measures could simply include the length of time spent performing each behavior. After completing these CPBs, participants could write and submit a solution proposal for hypothetical implementation. Experts could then judge this proposal for COE.

Methodologically, we generally recommend that any attempt to examine the casual linkages between CPBs and COE should adopt the creative task, assignment, or project as the key unit of analysis. Most existing studies use the individual or the team as the level of analysis, but when working on a creative task, work may move back and forth between the individual and group levels of activity and analysis. By focusing on the individual or team as the unit of analysis, measurement error may be introduced by variability in performance for either individual or team across time (Borman, 1991). To avoid these problems, future research could focus efforts on studying CPBs and COE relationships over time with respect to one identifiable and well-circumscribed task or project. Although it may be difficult to draw clear boundaries for tasks, such a unit of study and its boundaries would make it more possible to clearly establish the causal relations between the various CPBs and COE, both within and across levels.

Implications and Future Research Directions

The main goal of this article was to provide conceptual and empirical clarity regarding the workplace creativity criterion space and research related to it. Throughout, we offered conceptual and empirical evidence for the validity and usefulness of our framework, suggesting fruitful research topics and making specific methodological recommendations. The main value of this article is not simply supporting and formally drawing attention to these distinctions, though there is certainly a need for this. The main contribution is pointing out the implications of these distinctions both conceptually and empirically. Some implications involve a slightly different way of thinking about creativity; others mean new research practices could be embraced. We now summarize some future research directions stemming from our analysis of the creativity criterion space.

Future Research Directions

With respect to studying CPBs, future research could focus on examining multiple categories of behaviors. One needed area of research is to determine all the specific behaviors that are necessary and sufficient for creative performance. Research in this area could aim to distinguish these behaviors more precisely from problem-solving behaviors. Further, researchers could begin to pursue Unsworth’s (2001) expected versus unexpected creative performance distinction as theoretically and empirically important. In fact, we suggest that possible research hypotheses test this distinction. For example, skill-related antecedents predict expected CPBs and motivation-related antecedents predict unexpected CPBs, and extrinsic motivators have a positive or null effect on expected CPBs while having a negative effect on unexpected CPBs.




Future research could also examine whether additional dimensions of COE exist (e.g., implementation success, sales, etc.) in some situations. Buel (1960), for example, examined the dimension of “lasting contributions” in addition to novelty and usefulness, which may be a helpful dimension for determining the potential profitability of a product. Similarly, ad creativity added the dimensions of emotion or connectedness (Ang, Lee, & Leong, 2007). With the prevalence of emotional resistance often faced during implementation (West, 2002), consideration of the emotional reactions or resistance of stakeholders may be beneficial to consider as a dimension of COE. Future research could also examine how antecedent effects on COE are mediated by CPBs, as well as moderators of different CPBs → COE linkages.

Related to the CPB → COE causal relationship, future research could examine the relative impact CPBs have on COE. In this article, we conclude that a combination of four CPB types contribute to COE. As a result, one may question whether some CPB types are more important to COE than others. The magnitude of research on brainstorming and creative ability measures, which predominately emphasize behaviors broadly identified as idea generation (e.g., associating, divergent thinking), seems to suggest that idea generation behaviors may be the most vital for producing COE. However, at least one recent study suggests that idea generation behaviors may have relatively less impact in predicting COE compared with information-gathering behaviors (Binnewies et al., 2007). Future research could use our framework to classify behaviors into the four types and then examine the relative impact of each behavior type on COE in several contexts (e.g., R&D, artistic) and with both expected and unexpected CPBs.

Finally, future research could examine behaviors other than the four identified types of CPBs that may enhance COE. This includes examination of those behaviors that increase the probability of producing COE in some circumstances but that are not necessary for all creative tasks. Such behaviors may include risk taking, nonconformity, and so on. These should not be formally classified as CPBs if they are not necessary for producing COE, but research is now needed to establish the contextual conditions under which such supplementary behaviors may impact COE in addition to those the CPBs identified in this framework.

Management Implications

One key implication of the CPB–COE distinction emphasized here is that CPBs, in comparison with COE, offer a more objective cross-situational measurement platform for examining employee creativity, whereas COE consists of evaluations that are subjective and context dependent (e.g., time, location, task or problem). Thus, managers should probably rely on established CPBs to create selection criteria for creative projects or teams rather than relying on employees’ associations with past high COE outcomes alone.

However, with respect to creativity, COE is likely the most valued criterion for organizations. Employees who can consistently produce outcomes judged high on COE (e.g., project leader for the outcome in question) are likely to be rewarded. This practice should be tempered somewhat with the fact that there may be other causes of COE besides those employees’ CPBs. To the extent that our linkage between certain CPBs and COE are




supported and expanded, managers can confidently reward performers of those CPBs without negative effect on motivation, particularly when such CPBs are expected in the normal course of their jobs.

Conclusion

In this focused conceptual review and analysis, we help clarify the criterion space of workplace creativity. Our framework models workplace creativity partially in the tradition of job performance and thereby helps to clarify conceptual issues also closely related to current measurement concerns. By presenting our framework of criterion distinctions, we point the way toward clearer construct definitions, more valid measures, and as a result, greater understanding of findings and their implications for the creativity area. Although we have not addressed all the questions related to creativity as a workplace criterion, we hope that the framework proposed here helps inspire and direct future creativity research in novel and useful directions.

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