Personality,Nations,and Innovation

Abstract

Research has shown relationships between personality factors and innovation at the level of the individual person. Recently, data have become available that would allow testing of these relationships at the nation-state level. Based on theoretical aspects of the Big Five factors of personality, and on empirical work conducted using individuals as the unit of analysis, the authors hypothesize that mean national scores of Openness to Experience, Agreeableness, and Conscientiousness would be related to national innovation scores. Multinational data on mean national scores of the Big Five Inventory and the NEO-PI–R are compared to national-level innovation input and output scores from the International Innovation Index and the Global Innovation Index. On both indices, the results of the analyses using the NEO-PI-R show strong, positive relationships between Openness to Experience and both aspects of innovation, a strong positive relationship between Agreeableness and innovation inputs and no relationships between Conscientiousness and either innovation inputs or outputs. The analyses using the Big Five Inventory data shows no reliable relationship between national-level personality and national innovation scores. These results are discussed in terms of their implications for what one can learn from national-level studies of personality and innovation. Suggestions are offered to those governments and financial institutions interested in encouraging economic growth via innovation.

Keywords

personality Big Five innovation multinational

The image of the solitary innovator, working away by the light of a welding torch in his backyard shed to produce some astounding new widget, is an iconic one in the Western world. The true picture of the technology innovator is, of course, rather more complex. Although the individual may work alone at times, he or she does so embedded in a much wider, social context. Many times, it is this context that facilitates or hinders the innovation’s success in the marketplace (see, for example, Arndt & Sternberg, 2000; Wineman, Kabo, & Davis, 2009). It is the social context of innovation that is the primary focus of this article.

What is innovation? It is, most assuredly, a catch phrase that is widely used in the halls of business and government. Also, as suggested in the opening sentence, it is a word that has a loosely-defined meaning in the common vernacular. Unfortunately, and perhaps not surprisingly, the combination of these two conditions has served to reduce the clarity of the term and thereby create difficulties in the conduct and interpretation of innovation research. Therefore, it is important that researchers are very clear about the definition of innovation in their work. We shall take a moment to provide our terminology and definition.

The originator of the idea or widget is the inventor. Although an obvious and integral part of the process, he or she generally requires assistance from several diverse sources if the invention is to diffuse to any great extent (Adams, Bessant, & Phelps, 2006). The coordination of these sources requires interactions among people across time. This leads us to a key aspect of our definition of innovation. While an inventor is an initiator, innovation, itself, is a social process that not only includes but also extends beyond the inventor. More specifically, the authors’ view of innovation is that it is a multifactorial process that is primarily social in nature but whose outputs can be reflected by gains in one of more value categories. Most often, this latter category is economic. The actors who carry this process through from invention to the diffusion stage are innovators.

Traditionally, success in innovation meant the widespread adoption of the object or idea in the marketplace, which normally leads to significant financial gains to the innovators. Thus, success in innovation is defined by such gross economic measures as sales figures, GDP, and job creation (outputs) and also by such factors as the presence of favorable governance, higher levels of education, and support for research and development (inputs; Andrew, DeRocco, & Taylor, 2009; Atkinson & Andes, 2009; INSEAD, 2010).

Success in innovation is one of the means by which nations and businesses make their mark in the world. In a world of globalized trade, nations have an “innovation imperative” in which success in innovation is considered necessary for national growth and survival. It occurs at an individual, business, and national level and has concomitant costs and rewards at each level. An important focus for research, then, concerns the drivers of innovation. Which factors are associated with high and low innovation performance?

In this study, we empirically test the relationship between personality and innovation but do so at the national level, using four publicly available data sets: two multinational surveys of personality (McCrae & Terracciano, 2008; Schmitt, Allik, McCrae, & Benet-Martínez, 2007) and two multina-tional innovation indices (the International Innovation Index—Andrew et al., 2009; the Global innovation Index—INSEAD, 2010). First, though, we will review what is known about the connections between personality and innovation.

Personality and Innovation: The Individual Person

It will not come as a surprise to most readers that there have been a large number of studies examining the possible connections between individual innovation and an individual’s personal characteristics. There have, for example, been studies conducted on the links between innovation and emotional intelligence (Suliman & Al-Shaikh, 2007), neuroanatomy (Heilman, Nadeau, & Beversdorf, 2003), psychological flexibility (Georgsdottir & Getz, 2004), and emotions (Rank & Frese, 2008). One of the most active research areas has been in the area of personality. Before we begin a review of this area, it is appropriate to give the reader a brief overview of the dominant structural model in personality theory today, the Five-Factor Model (FFM), which will be the basis of the personality measures used in the current study.

The Five-Factor Model

Succinctly stated, the Five-Factor Model posits that all personality traits can be subsumed into five overarching factors: Neuroticism, Extraversion (or Surgency), Openness to Experience (also known as Culture or Intellectance), Agreeableness, and Conscientiousness (John & Srivastava, 1999). Neuroticism covers the family of traits that represent such characteristics as vulnerability to stress, emotional lability, and a tendency toward negative mood states. Extraversion describes the degree of external versus internal orientation. It includes aspects such as confidence, passion or exuberance, positive emotionality, and willingness to engage with the sociophysical environment. Openness to Experience captures one’s willingness to engage in, or with, novel experiences and ideas. Occasionally and incorrectly, this has been narrowly construed to mean physical sensation seeking. However, Openness actually includes any category of new experience, including the appreciation of new art, consideration of alternative value systems, and the desire to hear challenging philosophies and worldviews. Agreeableness is much as the label implies. Persons who scored high on this factor would be described using terms such as trustworthy, honest, compliant, and modest. It measures, in large part, the manner in which one conducts his or her social relationships. Finally, Conscientiousness captures such aspects as one’s sense of duty, desire to achieve, desire to complete tasks to a high standard, and self-discipline.

Openness to Experience

The primary research nexus between personality and innovation has been in the area of creativity. As Shalley, Zhou, and Oldham (2004) have pointed out, one of the principal factors in innovation is creativity. Generation of a new idea is at the very beginning of the innovation process. But, like innovation, the term “creativity” has several meanings and, in everyday parlance, is often vaguely defined. For the purposes of this article, we shall specifically adopt Henderson’s (2004) definition of creativity: “the production of novel ideas or products that solve a problem, fit a situation, or accomplish a goal with significance in broader social context” (p. 294). Like our definition of innovation, Henderson’s interpretation of creativity explicitly acknowledges the background in which the generation of new objects and ideas takes place.

The Big Five factor most closely associated with creativity is Openness to Experience (George & Zhou, 2001). All the facets of the openness factor share an “interest in varied experience for its own sake” (McCrae, 1987, p. 1259). Those scoring high on the Openness factor have been described as creative, imaginative, and original (McCrae, 1987). Such individuals are able to generate and think about novel ideas that contest the status quo (George & Zhou, 2001; McCrae & Costa, 1997). Individuals scoring low on the Openness factor tend to be more comfortable with the familiar and lack incentive to try something new (McCrae, 1987). It is likely, therefore, that Openness is positively associated with innovation, and an important aspect of this association is creativity.

A number of studies have looked at the relationship between the Openness factor and creativity. In work by McCrae (1987), Openness was correlated with creativity on the Creative Personality Scale (CPS; Gough, 1979) and the Self-Directed Search (SDS; Holland, 1985). The Openness factor was also correlated to creativity on the Rokeach Value Survey (RVS; Rokeach, 1973), the Test of Creative Thinking-Drawing Production (Urban & Jellen, 1996) and the Thematic Apperceptions Test (Murray, 1943) in work by Dollinger, Leong, and Ulicni (1996) and Dollinger, Urban, and James (2004).

The relationship between the Openness factor and creative behavior has been shown to be sensitive to contextual effects. For example, research in the workplace has been evaluated by George and Zhou. Their analysis indicates that high scores on Openness to Experience explained a significant amount of variance in creative behavior when workers had positive feedback (from supervisors or colleagues) and had unclear means and ends in their job (i.e., tasks lacking a clear and straightforward procedure for accomplishment). Regarding individuals with high Openness scores, George and Zhou (p. 514) assert that “their appreciation for things that are novel and unique in conjunction with their greater sensitivity to and range of experience may cause them to come up with novel solutions to problems and creative ideas to improve on current functioning.” The study’s results suggest that Openness encourages creative behavior if the situation allows for the manifestation of creativity (George & Zhou, 2001).

Creativity has been clearly linked to innovation (Miron, Erez, & Naveh, 2004), but it can be argued that creativity is most strongly associated with the first part of innovation, that is, invention, broadly defined. Henderson (2004), using the short-form version of the NEO-PI (the NEO-FFI), assessed the personality of inventors. He found that the mean of these participants’ Openness scores was significantly higher than the NEO-FFI’s normative sample. Kwang and Rodrigues (2002) examined the scores of 164 Singapore school teachers on the FFI and the Kirton Adaption-Innovation Scale (Kirton, 1976). Their results suggested that respondents who exhibited an innovator style generally scored higher on Extraversion and Openness and lower on Conscientiousness. A similar pattern of results was found using a sample of Singaporean polytechnic students (Ee, Seng, & Kwang, 2007). In a study examining the factors of successful product development teams, Aronson, Reilly, and Lynn (2008) found that having a leader high in Openness was a benefit to those teams working on radical innovations. Homan et al. (2008) later found similar results for team members, in that teams who scored higher on mean scores of openness tended to be more innovative.

Conscientiousness

Zaltman, Duncan, and Holbeck (1973) pointed out that innovation is a two-stage process, beginning with initiation but eventually moving to implementation. Similarly, and more recently, Fehr (2009) has suggested that innovation requires both domain breadth and persistence. Both these studies suggest that creativity is not enough; innovation also requires effort—often prolonged—to bring the invention to successful adoption. Thus, it is likely that there are other personality factors than those directly associated with creativity that come into play.

These latter attributes are more closely allied with such characteristics as persistence, responsibility, and achievement orientation, all of which have been identified by Barrick and Mount (1991) as being aspects of the Conscientiousness factor in the FFM. Barrick and his colleagues (Barrick, Mount, & Strauss, 1993) have also shown that Conscientiousness is related to goal setting and commitment to those goals. It does not require a great deal of imagination to argue that such behavior is very likely to be helpful in enabling the large-scale uptake of a new idea or product.

There is some empirical evidence of a positive relationship between Conscientiousness and innovation. Aronson et al. (2008) have found that the Conscientiousness of team leaders was associated with successful product development. Zhao and Seibert (2006) have shown that higher scores on Conscientiousness reliably distinguish entrepreneurs from managers, though only on achievement motivation and not on dependability. On the other hand, Conscientiousness has been shown to be associated with lower levels of scientific creativity (Feist, 1998) and is related to lower creativity in a work environment that is unsupportive (George & Zhou, 2001). This may be due to the adherence to orderliness that is part of being a highly conscientiousness individual. This adherence may inhibit potential inventors because the creative aspect of innovation often requires one to deconstruct—make a mess, if you will—before something new can be made.

Perhaps the best resolution here is to acknowledge that, as Fehr (2009) suggests, innovators need to be both open to experience and conscientious; a situation that she describes as a paradox. One resolution to this paradox is to posit that innovation is a complex process that involves many people with different roles: inventors, entrepreneurs, and marketers, to name a few. Thus, we suggest that Conscientiousness is most strongly associated with the output side of the innovation process (e.g., the entrepreneurial portions), where its potential dampening effect on creativity will be felt somewhat less than in the input stages of innovation (i.e., those times in which invention is most apparent).

Agreeableness

Finally, we consider Agreeableness. For an inventor to take an invention through to successful innovation requires considerable skill in managing social networks (Lambert & Fairweather, 2010). The inventor has to interact with funders, government agencies, manufacturers, business people, and intellectual property agencies—all critical factors in innovation success. In many cases, family members are also involved, often playing an important role in the innovation process, either directly, or in some case indirectly; for example, exhibiting considerable forbearance of their partner’s commitment to the invention. These interactions function more successfully if there is trust and cooperation. Furthermore, Agreeableness has been found to be positively linked to entrepreneurship (Zhao & Seibert, 2006), which may be necessary in the latter stages of the innovation process; for example, when an invention is being brought to market and sales growth is one of the main objectives. It is likely, then, that Agreeableness—especially getting along with others—is positively associated with innovation.

Whereas the traditional unit of analysis for personality has been the individual human being, there have been several arguments in the recent literature that make a case for meaningful interpretation of personality traits aggregated at the cultural and national level (Allik & McCrae, 2004; Hofstede & McCrae, 2004; McCrae, 2001; McCrae & Allik, 2002; Terracciano & 65 others, 2005). Given that our principal unit of analysis for innovation for this article will be the nation-state, we now turn to examine recent research into cross-national differences in personality.

Personality and Nations

There have been several attempts to examine personality at the national level; that is, to seek patterns of traits associated with residency in a nation (Allik, 2005). The most recent, and largest, of these studies was conducted by Schmitt et al. (2007). They investigated personality across 56 nations, which represented 10 geographic regions around the globe. Using the Big Five Inventory (BFI; John, Donahue, & Kentle, 1991) of personality traits, the authors gathered data from more than 17,000 respondents. One of the chief aims of the study was to examine the robustness of the five-factor structure across diverse cultures. The findings indicated that this structure reliably manifested itself around the world (see also, Rolland, 2002).

This is an important finding, particularly if one wishes to study relationships between personality and other variables that are aggregated at the national level. Such reliability in the measure of personality traits gives one confidence to make multinational comparisons. The degree of invariance of the five-factor structure provides evidence for the assertion that constellations of traits are viewed in much the same manner around the world and that a person in one culture is likely to interpret a Big Five factor in much the same way as a person in quite a different culture. In other words, humanity tends to hold a common view on the nature of the FFM factors of Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness.

These common definitions allow valid comparison between nations and can be used to understand national differences. They also allow us to compare similarities in nations. One of the key findings is that nations that are close to one another also show similar constellations of the Big Five factors (McCrae & Allik, 2002; Schmitt et al., 2007). Given two nations that are similar but not identical in their personality profiles, differences in innovation rates between them may suggest ways to improve the innovation rate of the lower performing nation.

We note, however, that this approach is not without its critics. In particular, Heine, Buchtel, and Norenzyan (2008) have suggested that five-factor models do not perform well as predictors of cross-national differences in behaviors purportedly related to those factors. In their study, these measures included “pace of life” (walking speed, time in a post office transaction, and bank clock accuracy), GDP (as a proxy for individual “occupational success”), and longevity of citizens. However, the rationale for all these measures relies on a series of studies that compare individual measures of personality with individual outcomes. Their study, on the other hand, falls into a methodological trap known as the ecological fallacy (Robinson, 1950) in which aggregated characteristics are misapplied as predictors of individual outcomes. This is an error that has been long recognized in the social sciences, particularly in the political sciences. We seek to avoid this mistake by comparing national-level measures of a complex and collective phenomenon (innovation levels) with scores on personality factors averaged at the same, national level.

Personality, Nations, and Innovation

In light of the fact that large-scale, multinational research into personality has only recently been undertaken, it is not surprising that links between national personality measures and national innovation measures have not been investigated. In fact, at the time of writing, the authors were not able to find a single instance of research examining the relationship between these variables.

Why investigate such links? First, there are empirical results showing a connection between personality and innovation at the level of the individual person. Thus, it becomes a theoretical point of interest to see whether these relationships can be replicated using larger units of analysis. In addition, since we cannot assume that results found at the individual level necessarily apply at the national level, several empirical questions arise: Are aggregated (national-level) scores on Openness to Experience, for example, associated with national innovation ratings? What role, if any, does a nation’s score on Conscientiousness play in its innovation rankings? Does a nation whose citizens score, on average, highly on Agreeableness also engage in greater innovation?

Second, information about any relationships between national behavioral predispositions (i.e., personality factors) and innovation performance may provide insight into societal factors that help or hinder individual innovation. One possibility along these lines is the connection between cooperation and Agreeableness. Costa and McCrae (1992) have suggested that high Agreeableness is indicative of a predisposition to be cooperative. Empirical evidence exists to support this suggestion (see, for example, Beersma et al., 2003; LePine & Van Dyne, 2001; Ross, Rausch, & Canada, 2003). Inasmuch as there have been important links drawn between innovation and such social processes as social contagion (Burt, 1987), collaboration networks (Gemünden, Ritter, & Heydebreck, 1996), and regional networking among businesses (Arndt & Sternberg, 2000; Sternberg, 2000), it would be worthwhile testing the notion that a nation that is generally agreeable is also one that is innovative.

Finally, one must keep in mind that personality is a complex constellation of traits. It is possible that different cultures, or nations, have attained high innovation performance in quite different national contexts. It is unlikely that there is one path to better performance. Factors such as national innovation policy are likely to combine with national personality to either help or hinder the innovation process. Thus, nations who rank lower on national scores of innovation may see a way forward by examining successful nations similar to them in other respects, such as personality. For example, less innovative nations could gain insights into innovation policies that are likely to be more compatible with the personality characteristics of their nations.

Because there has been no prior research into links at this unit of analysis (i.e., at the national level), the hypotheses to be forwarded are based on research conducted at the level of the individual person. The differences between individual and group behavior, though, have been long noted in various domains of the social sciences. Therefore, the hypotheses to be tested are necessarily quite speculative.

Hypotheses

The implicit hypothesis underpinning the research that follows is that national levels of personality scores are associated with national levels of innovation. Hypothesis 1 and Hypothesis 2 are derived from the empirical findings on relationships between personality traits and innovation at the individual level. Hypothesis 3 is based on our postulate that successful innovation is a social process that requires cooperation among many people. For each hypothesis, we predict that the personality score is associated with overall innovation scores (inputs plus outputs), but we also examine inputs and outputs separately.

Hypothesis 1: In light of its established links to creativity, Openness to Experience is positively associated with innovation scores.

Hypothesis 1a: Openness to Experience will be more strongly related to the stage of innovation process most closely related to inventiveness and creation rather than implementation and marketing. In the innovation measures used in the current study, this stage is captured in the innovation inputs factors.

Hypothesis 2: Agreeableness is positively associated with innovation scores, since cooperativeness and trustworthiness are critical to managing innovation networks.

Hypothesis 3: Inasmuch as Conscientiousness represents competence, achievement striving, and self-discipline (Costa & McCrae, 1992), and these characteristics would lead to greater persistence in overcoming the many challenges inherent in bringing a new idea to wide-scale adoption, Conscientiousness will be positively associated with innovation scores.

Method

Data Sources

A search of the internet led to several potential sources of data concerning national levels of innovation. Each of these were assessed for breadth of countries covered, appropriateness of variables to the planned analysis, reliability of data sources for the index, and clarity of definitions of components or variables. Two robust measures of innovation were selected, the International Innovation Index (III; Andrew et al., 2009)and the Global Innovation Index (GII; INSEAD, 2010). A similar set of criteria were applied in the search for a multinational data set covering personality factors and two measures were selected, the BFI and the NEO-PI. The data sets that were included in this study are described below.

National Innovation Data Sets

Measures of innovation used in the personality research have, for the most part, dealt with innovation as a unitary concept. It should be noted, though, that recent research and theoretical developments in the measurement of innovation in a wider context (organizational and national) take a more complex view of innovation. At the least, rankings and scores of innovation at these levels regard it as consisting of two components: inputs, which cover such things as regulatory and business contexts, and outputs, which are measured by such indicators as the quality of the social and business infrastructure expected to result from innovative activity (e.g., active R&D organizations, institutions of higher education) and, more traditionally, the number of patents issued and trademarks granted. Most current reports comparing national rates of innovation base their rankings on a large number of diverse measures from both subcomponents (see, for example, Andrew et al., 2009; Atkinson & Andes, 2009; INSEAD, 2010).

The International Innovation Index was developed primarily as an instrument to compare U.S. levels of innovation with other nations. The report that followed from this research was produced by a small consortium of U.S.-based businesses, led by the Boston Consulting Group. The report’s chief aim was to provide information about the ways in which the United States could improve its capacity for innovation. However, the III, itself, offers a sound source for comparing nations with respect to their orientation and ability to innovate, and their innovative production. It classifies these elements into two general categories: innovation inputs and innovation performance. Innovation inputs are comprised of fiscal and other (e.g., education, intellectual property) policies as well as indicators of infrastructure and workforce thought to be influential on innovation rates (e.g., workforce quality and infrastructure quality). Innovation performance represents measures of research and development, business performance, and the public impact of innovation. Further information on the particular measures for each of these can be found in the official report for the III.

Like its III score, a nation’s Global Innovation Index score is based on two main factors: innovation inputs and outputs. Input scores, in turn, are linear combinations of institutional considerations (including regulatory climate), human capacity, information technology structure and uptake, and measures of market and business sophistication. Outputs are based on scientific outputs (exports and employment; knowledge creation and application, including patents) and, unlike the III, include creative outputs and benefits to well-being. The rationale behind this latter innovation component to use the GII term, is that one of the main goals of national innovation plans, explicitly stated or otherwise, is to improve the quality of life of the nation’s citizens. Full details on the rationale, collection method, as well as complete data tables for the 132 countries included in the study, can be found in the GII report or on its website.

Taken together, these indices yield a comprehensive accounting of innovation activity around the globe. They both include information on a large number of countries and they are up to date. However, they differ somewhat in their basic philosophy, with the III being slightly more focused on traditional economic indicators, whereas the GII uses a few more indices of social well-being. Thus, we have taken the approach of conducting parallel analyses on both data sets and assessed the relationship between personality measures and innovation inputs and innovation outputs using both the III and GII.

Personality Data Sets

There are several instruments that measure the Big Five personality factors. The most widely used instrument is the NEO Personality Inventory, Revised (NEO-PI-R; Costa & McCrae, 1992). It is a well-validated and reliable instrument (Costa & McCrae, 2008) that measures the main five factors as well as the 30 subfactors (“facets”) that are hypothesized to comprise them. To say it is a popular measure would not quite reflect its use: The number of research studies in which the NEO-PI(R) and its forerunners play a significant role runs to several hundred. More recently, a new version of the NEO-PI-R has been published (NEO-PI-3; McCrae, Costa, & Martin, 2005) that offers better readability and internal consistency than the NEO-PI-R. A short-form (60-item) version of the NEO-PI(R), known as the NEO-FFI, is also available.

The Big Five Inventory is a shorter (44-item) instrument that displays acceptable levels of validity and reliability (John & Srivastava, 1999). It has been translated into 28 languages and has been shown to have good reliability and validity in multinational and multicultural studies (Benet-Martínez & John, 1998; John & Srivastava, 1999; Schmitt et al., 2007). A 10-item instrument based on the BFI has also been developed (Rammstedt & John, 2006), as has another of equal length but alternative origin (Gosling, Rentfrow, & Swann, 2003). Together, the instruments discussed in the two paragraphs above, along with Goldberg’s Trait Descriptive Adjectives (TDA; Goldberg, 1990), account for almost all the instruments used to assess the Big Five factors in a research context in the past two decades.

The NEO-PI-R and the BFI appear to be the best choices for instruments when conducting cross-cultural studies. Both have been shown to have structural stability across cultures (McCrae, Terracciano, & 78 Members of the Personality Profiles of Cultures Project, 2005; McCrae, Terracciano, & 79 Members of the Personality Profiles of Cultures Project, 2005; Schmitt et al., 2007). Of the two instruments, the NEO-PI has been used to the greatest extent in multinational research and, thus, has the greatest amount of empirical literature associated with it. On the other hand, the BFI has been used in a study that collected data from the largest number of countries (n = 56; Schmitt et al., 2007) and offers an alternative way of assessing the five factors. For these reasons, it was decided that the data used for our personality measures would be drawn from McCrae and Terracciano (2008) and Schmitt et al. (2007).

Two characteristics of the NEO-PI and the BFI need to be kept in mind. First, both employed samples of convenience; most respondents were college or university students. Second, the data were collected with the aim of making cross-cultural, rather than cross-national, comparisons. This presented a few minor challenges with the data. Although the authors of both articles state that there was significant overlap between culture and nation (e.g., German in Germany), there was an instance in the NEO-PI-R data set where a nation was split into its major cultural groups (German and French Swiss), two others in which the cultural group may not well represent the general population in the nation (Telugu Indians and Northern Irish), and another where the cultural group resided outside the nation most closely associated with the group (Hong Kong Chinese). The hypotheses we sought to test are nation based, as are the data for innovation levels. In the first four cases (German Swiss, French Swiss, Telugu Indians, and Northern Irish), the data were deleted from the final data set. In the final case, the Hong Kong Chinese were taken to represent Hong Kong rather than China. This necessarily reduced the number of data points, and perhaps rendered the data set more conservative, but we felt that it allowed us to make more reliable comparison amongst nations. This procedure left us with 56 and 42 observations for the BFI and NEO-PI-R data sets, respectively.

Results

Four multivariate multiple regressions were conducted. Each of the personality data sets were compared to each of the two innovation data sets, using the five personality factors (Neuroticism, Extraversion, Openness to Experience, Agreeableness, and Conscientiousness) as predictor variables and the two innovation components (inputs and outputs) as criteria variables.

Openness to Experience and Agreeableness were associated with innovation, but only in the analyses employing the NEO-PI-R data (all significant results at p = .05 or less). Further exploration of these results using bivariate correlations showed that Openness was significantly related to innovation input and output in both innovation indices but that Agreeableness was reliably associated only with the input variables. The entire set of results for all four multivariate analyses can be found in Table 1. The correlations associated with the significant relationships can be found in Table 2.

Table 1.

Results for Multivariate Multiple Regression Analyses: BFI and NEO-PI-R Factors and the International Innovation Index (III) and the Global Innovation Index (GII) Inputs and Outputs

Factor	Index	Wilks’ λ	F(2, 36)	p value	Partial η2	Observed Power
BFI
Neuroticism
	III	.913	1.99	n.s.	.09	.39
	GII	.972	0.66	n.s.	.03	.16
Extraversion
	III	.998	.047	n.s.	.002	.06
	GII	.927	1.80	n.s.	.07	.36
Openness to Experience
	III	.877	2.96	n.s.	.12	.55
	GII	.981	0.45	n.s.	.02	.12
Agreeableness
	III	.893	2.53	n.s.	.11	.48
	GII	.990	0.24	n.s.	.01	.09
Conscientiousness
	III	.945	1.21	n.s.	.06	.25
	GII	.935	1.59	n.s.	.07	.32
NEO-PI-R
Neuroticism
	III	.977	0.43	n.s.	.02	.11
	GII	.934	1.28	n.s.	.07	.26
Extraversion
	III	.980	0.36	n.s.	.02	.10
	GII	.946	1.03	n.s.	.05	.22
Openness to Experience
	III	.836	3.53	.04	.16	.62
	GII	.832	3.63	.04	.17	.63
Agreeableness
	III	.754	5.89	.01	.25	.85
	GII	.850	3.18	.05	.15	.57
Conscientiousness
	III	.993	0.12	n.s.	.01	.07
	GII	.981	0.36	n.s.	.02	.10

Note: III = International Innovation Index; GII = Global Innovation Index; NEO-PI-R = NEO Personality Inventory, Revised; BFI = Big Five Inventory.

Table 2.

Correlations Between the NEO-PI-R Openness to Experience and Agreeableness Scores With the International Innovation Index (III) and the Global Innovation Index (GII) Input and Output Scores

Personality factor	Innovation	Variable r	p value
Openness to Experience
	III input	.47	.002
	GII input	.54	.001
	III output	.48	.002
	GII output	.42	.006
Agreeableness
	III input	.46	.005
	GII input	.45	.005
	III output	.16	n.s.
	GII output	.30	n.s.

Note: All n = 43.

Discussion

The central hypothesis in this study was that there is a relationship between personality factors, aggregated at the national level, and national innovation ratings. The multivariate statistical test of this proposition provided qualified support, inasmuch as the NEO-PI-R Openness to Experience and Agreeableness factors showed reliable associations with both of the national-level indices of innovation. These findings indicate that at least certain aspects of country-level personality, as measured by the NEO-PI-R, may play a role in the innovative activity of nations. There was a marked discrepancy, however, between the results of the analyses using the two personality data sets, so we will first turn to an examination of why such differences might have arisen.

The Big Five Inventory, the NEO-PI-R, and National Innovation

The first, and most obvious, explanation for the difference in these results is that our central hypothesis regarding a relationship between personality and innovation was disconfirmed. However, this is unlikely to be the case, given that this hypothesis had such clear support from the NEO-PI-R data. The reason for the different findings, then, most likely exists in differences between the BFI and NEO-PI-R instruments, or in the different ways that the two personality data sets used in this study were assembled.

One immediately noticeable difference between the NEO-PI-R and the BFI is their respective lengths. The former has 240 items and the latter has 44 items. All other things being equal, statistical arguments would suggest that the NEO-PI-R has greater validity and reliability. In addition, recent evidence (Soto & John, 2009) has suggested that there is only moderate agreement between conceptually similar facets of both instruments (r = .25 to r = .78). Finally, we note that the NEO-PI-R has had a longer track record in the personality literature and has generated a substantially larger number of publications testing its psychometric properties. Although this does not necessarily establish the NEO-PI-R as the better instrument, it does lend it greater credibility with respect to measuring the five-factor model.

The original study in which the NEO-PI-R data were collected (see McCrae, Terracciano, & 78 Members of the Personality Profiles of Cultures Project, 2005) was specifically designed and executed to obtain personality data across cultures. This is not true of the study from which the BFI data are drawn. This latter study (the International Sexuality Description Project; see Schmitt et al., 2002) had the objective of examining human sexuality across many cultures. It included the BFI as an additional measure to the principal instruments in the International Sexuality Description Project. This information, when considered with the fact that the data are gathered from convenience samples, gives rise to the possibility that samples from the NEO-PI-R and BFI studies represent quite different populations.

With this information considered, we believe that the results using the NEO-PI-R study are likely to be better indicators of the relationship between innovation and personality. Therefore, we will proceed with a discussion of these results.

NEO-PI-R Factors and Innovation

Openness to experience

With respect to our more specific predictions, it would appear that both Hypothesis 1 and Hypothesis 1a were well supported. The link between Openness to Experience and innovation measures manifested itself in both the inputs and outputs measures of innovation.

Previous empirical findings had led to the expectation that Openness to Experience would be positively associated with innovation. One explanation for this is that Openness is, in part, a measure of the value one places on unconventionality (John & Srivastava, 1999). A general willingness to entertain nontraditional concepts would facilitate innovation by increasing the likelihood that an invention would, at the least, be given a fair assessment by bankers, venture capitalists, and front-line government workers in the initial stages, and by consumers or adopters in the later stages. We need to pause here to reflect on the nature of the types of analyses done for this study. The design of the research, and the statistical analyses used, do not permit causal statements. Rather, they allow us only to speculate on the direction of causality. It is conceivable that the causal arrow for Openness and innovation is reversed; innovation causes Openness. Two aspects of personality theory weigh against this explanation, though. First, personality traits do not tend to change easily, partly because valid measures of a FFM trait specifically tap behavioral dispositions that are consistent across differing situations. Second, there is substantial evidence to suggest that one’s personality has many of its roots in one’s genetic code (Plomin, Owen, & McGuffin, 1994). This code, based as it is in biology, is not easily nor directly affected by social context in the short term; we would not expect rises or falls in innovation to act upon personality via this mechanism in any time frame shorter than one measured in generations. These arguments also apply to the other factors of personality and their relationship to innovation. Finally, there are both theoretical and empirical reasons, drawn from the field of macroeconomics, that would suggest a causal arrow going from Openness to innovation (Baldwin, 2003; Harrison, 1996; but see, Rodriguez & Rodrik, 2001). As Harrison has pointed out, certain neoclassical growth models suggest that

openness to trade provides access to imported inputs, which embody new technology; increases the effective size of the market facing producers, which raises the returns to innovation; and affects a country’s specialization in research-intensive production. (p. 1)

Openness to Experience, with its facets that emphasize interest and curiosity about other values, beliefs, and aesthetics, would thus expand the pool of possible or desirable trade partners for a people strong in the Openness factor. Cultural practices of trade partners at odds with their own mores would not pose as much of a hindrance as they would for those low in Openness.

Agreeableness

We move now to a discussion of Agreeableness. It was expected that higher Agreeableness would help national innovation by providing a social context in which people were more likely to support those bringing an invention forward. Furthermore, straightforwardness and trust are both facets in Agreeableness (Costa & McCrae, 1992), and there is ample empirical evidence that level of trust is an important aspect in innovation networks (see, for example, Clegg, Unsworth, Epitropaki, & Parker, 2002; Jirotka et al., 2005; Murphy, 2002). The results of our study indicate that Agreeableness is linked to overall innovation inputs but did not figure significantly in innovation outputs.

This is a curious finding. One explanation for it may be deduced from the components that make up the outputs score. Many of these measures are those that have checks and balances built into them. In the case of patents, most countries have a well-established legal framework focusing on the process of application and enforcement of patents. These guidelines and rules require only obedience, not trustworthiness, and because they generally originate from one or more governmental institutions, likability and attractiveness may simply not play an important role. Similar safeguards, but with less legal implications, are in place with respect to scientific publications and the professional peer-review system. Thus, it may be supposed, there is less of a need to trust one’s fellow human being at the outputs stage. Any duplicity or treachery can be dealt with by the judicial system.

During the early and less formal stage of invention, however, there are far more ways in which an idea may be successfully appropriated by a competitor. Therefore, inventors may feel an increased need to be wary about whom they approach with their ideas; trust and a history of straightforwardness are particularly valuable commodities in these initial stages of a business relationship. This latter finding has been strongly evident in some preliminary findings by our research group, in a study examining innovation by “grass-roots” inventors (Lambert & Fairweather, 2010). Therefore, nations that exhibit high scores on Agreeableness are those in which inventors are more likely to reach out for assistance in developing and marketing their ideas.

We note, though, that the importance that trust has with respect to business practices varies across cultures (Fukuyama, 1995). Future research that examines specific facets of the FFM and innovation—something that was beyond the scope of the current study—may wish to take this into account.

As in the case of Openness to Experience, we realize that there is an alternative explanation that reverses the causal arrow between Agreeableness and innovation. There is a well-known relationship between the wealth of a nation and its innovation ranking: As a nation’s rate of innovation grows, so does that nation’s GDP.¹ This has been shown both theoretically (see, for example, Grossman & Helpman, 1991) and empirically (Cameron, 1996; Edwards, 1998). Although not all growth in national economies guarantees a concomitant growth in its citizens’ personal wealth and happiness, it is possible that a sufficient increase in the standard of living brought about by technological innovation could cause a population to be more agreeable. As there are currently no practicable ways of manipulating agreeableness at the national level, the causal direction must remain a point of theoretical speculation.

Conscientiousness

The last factor we need to discuss is Conscientiousness. Conscientiousness was not associated with innovation inputs or outputs. Although prior research examining this relationship at the individual level suggested that we may find this (see Feist, 1998; George & Zhou, 2001), it is still a result that appears counterintuitive: Shouldn’t good “work habit” characteristics such as diligence, sense of duty, and orderliness help the innovation process?

Attempting to provide an answer to this conundrum engendered a fair amount of discussion in our research group. To offer a possible resolution, we must return to the subcomponents of the innovation inputs and outputs. Key elements in the calculation of inputs are fiscal policy, other policy (e.g., education, business. and intellectual property policies), and aspects of the innovation environment (e.g., workforce quality and infrastructure quality). Two of these three subcomponents, fiscal policy and other policy, are primarily controlled by government agencies. A government that tends to engage in extended deliberation (a defining characteristic of high Conscientiousness, and many governments) is one that is less inclined to react quickly to opportunities. A similar argument can be made for fiscal institutions. With regards to innovation output subcomponents, we note that high levels of Conscientiousness, which incorporates orderliness, dutifulness, and deliberation facets, may militate against the quick flexibility that may be needed to most productively capitalize on novel ideas in a competitive world market. This effect would be particularly evident in those nations that exhibit elevated Conscientiousness because social norms and values about deliberative behavior would add to the pressure on individuals to act in a cautious manner.

Our results lead to some recommendations for those who can influence a nation’s population, though none of these recommendations are “quick fixes.” Rather, they involve long-term planning that span decades, and not all will sit comfortably with all ruling bodies. First, the relationship between Openness to Experience and innovation implies that public and private sector leaders should encourage people to embrace novel thinking as a way to help the economy grow; a nation’s populace should be willing to consider unconventional or unusual alternatives regardless of their place in the innovation process. Second, the findings regarding Agreeableness suggest that those who deal initially with an inventor (e.g., business managers, venture capitalists, and government agents) should be informed that their greatest asset when dealing with an inventor is their reputation as an honest person; one whom the inventor can trust.

We recognize that structure, in the form of clear governance, stable political and financial institutions, and a peaceful population, is supportive of economic growth. Perhaps more important for innovation, though, is that the solitary inventor, whom we first mentioned in our introduction, believes that he or she will be dealt with fairly and knowledgeably by people within those institutions. We also believe our results indicate that nations that do not foster openness to experience in its citizenry will, to their economic detriment, inhibit the entire innovation process.

Footnotes

Appendix

Appendix 4

Countries Used in the Analysis of Relationships Between the NEO-PI-R and the GII

Argentina	France	Philippines
Australia	Germany	Poland
Austria	Hong Kong	Portugal
Belgium Botswana	Iceland Indonesia	Russia Slovak Rep.
Brazil	Italy	Slovenia
Burkina Faso	Japan	South Korea
Canada	Kuwait	Spain
Chile	Malaysia	Thailand
China	Malta	Turkey
Croatia	Mexico	Uganda
Czech Rep.	Morocco	United Kingdom
Denmark	New Zealand	United States
Estonia	Nigeria
Ethiopia	Peru

Note: GII = Global Innovation Index; NEO-PI-R = NEO Personality Inventory, Revised.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The author(s) received the following financial support for the research, authorship, and/or publication of this article: Funding for this research was provided by the New Zealand Foundation for Research, Science and Technology (Contract number LINX0801).

Notes

References

Adams

Bessant

Phelps

(2006). Innovation management measurement: A review. International Journal of Management Reviews, 8. Retrieved on August 20, 2009, from http://ssrn.com/abstract=895598

Allik

(2005). Personality dimensions across cultures. Journal of Personality Disorders, 19, 221-232.

Allik

McCrae

(2004). Toward a geography of personality traits: Patterns of profiles across 36 cultures. Journal of Cross-Cultural Psychology, 35, 13-28.

Andrew

J. P.

DeRocco

E. S.

Taylor

(2009). The innovation imperative in manufacturing: How the United States can restore its edge. Boston: Boston Consulting Group.

Arndt

Sternberg

(2000). Do manufacturing firms profit from intraregional innovation linkages? An empirical based answer. European Planning Studies, 8, 465-485.

Aronson

Z. H.

Reilly

R. R.

Lynn

G. S.

(2008). The role of leader personality in new product development success: An examination of teams developing radical and incremental innovations. International Journal of Technology Management, 44, 5-27.

Atkinson

R. D.

Andes

S. M.

(2009). The Atlantic Century: Benchmarking EU and U.S. innovation and competiveness. Washington, DC: The Information Technology and Innovation Foundation.

Baldwin

R. E.

(2003, March). Openness and growth: What’s the empirical relationship? (NBER Working Paper Series, Vol. W9578). Available from http://ssrn.com/abstract=389449.

Barrick

M. R.

Mount

M. K.

(1991). The big five personality dimensions and job performance: A meta-analysis. Personality Psychology, 44, 1-26.

10.

Barrick

M. R.

Mount

M. K.

Strauss

J. P.

(1993). Conscientiousness and performance of sales representatives: Test of the mediating effects of goal setting. Journal of Applied Psychology, 78, 715-722.

11.

Beersma

Hollenbeck

J. R.

Humphrey

S. E.

Moon

Conlon

D. E.

Ilgen

D. R.

(2003). Competition, cooperation, and team performance: Toward a contingency approach. Academy of Management Journal, 46, 572-590.

12.

Benet-Martínez

John

O. P.

(1998). Los cinco grandes across cultures and ethnic groups: Multitrait multimethod analysis of the Big Five in Spanish and English. Journal of Personality and Social Psychology, 75, 729-750.

13.

Burt

R. S.

(1987). Social contagion and innovation: Cohesion versus structural equivalence. American Journal of Sociology, 92, 1287-1335.

14.

Cameron

(1996). Innovation and growth (Discussion Paper 277). London: London School of Economics and Political Science. Retrieved December 16, 2009, from http://eprints.lse.ac.uk/20685/1/Innovation_and_Economic_Growth.pdf.

15.

Clegg

Unsworth

Epitropaki

Parker

(2002). Implicating trust in the innovation process. Journal of Occupational and Organizational Psychology, 75, 409-422.

16.

Costa

P. T.

McCrae

R. R.

(1992). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Odessa, FL: Psychological Assessment Resources.

17.

Dollinger

S. J.

Leong

F. T. L.

Ulicni

S. K.

(1996). On traits and values: With special reference to openness to experience. Journal of Research in Personality, 30, 23-41.

18.

Dollinger

S. J.

Urban

K. K.

James

T. A.

(2004). Creativity and openness: Further validation of two creative product measures. Creativity Research Journal, 16, 35-47.

19.

Edwards

(1998). Openness, productivity, and growth: What do we really know? The Economic Journal, 108, 383-398.

20.

Seng

T. O.

Kwang

N. A.

(2007). Styles of creativity: Adaptors and innovators in a Singapore context. Asia Pacific Education Review, 8, 364-373.

21.

Fehr

(2009). Why innovation demands aren’t as conﬂicted as they seem: Stochasticism and the creative process. Industrial and Organizational Psychology, 2, 344-348.

22.

Feist

G. J.

(1998). A meta-analysis of personality in scientific and artistic creativity. Personality and Social Psychology Bulletin, 2, 290-309.

23.

Fukuyama

(1995). Trust: the social virtues and the creation of prosperity. New York, NY: Free Press.

24.

Gemünden

H. G.

Ritter

Heydebreck

(1996). Network configuration and innovation success: An empirical analysis in German high-tech industries. International Journal of Research in Marketing, 13, 449-462.

25.

George

J. M.

Zhou

(2001). When openness to experience and conscientiousness are related to creative behavior: An interactional approach. Journal of Applied Psychology, 86, 513-524.

26.

Georgsdottir

A. S.

Getz

(2004). How flexibility facilitates innovation and ways to manage it in organizations. Creativity and Innovation Management, 13, 166-175.

27.

Goldberg

L. R.

(1990). An alternative “description of personality”: The Big-Five factor structure. Journal of Personality and Social Psychology, 59, 1216-1259.

28.

Gosling

S. D.

Rentfrow

P. J.

Swann

W. B.

Jr. (2003). A very brief measure of the Big Five personality domains. Journal of Research in Personality, 37, 504-528.

29.

Gough

H. G.

(1979). A creative personality scale for the Adjective Check List. Journal of Personality and Social Psychology, 37, 1398-1405.

30.

Grossman

G. M.

Helpman

(1991). Innovation and growth in the global economy. Cambridge: MIT Press.

31.

Harrison

A. E.

(1996). Openness and growth: A time-series, cross-country analysis for developing countries (August 1995; NBER Working Paper No. W5221). Available from http://ssrn.com/abstract=225285

32.

Heilman

K. M.

Nadeau

S. E.

Beversdorf

D. O.

(2003). Creative innovation: Possible brain mechanisms. Neurocase, 9, 369-379.

33.

Heine

S. J.

Buchtel

E. E.

Norenzyan

(2008). What do cross-national comparisons of personality traits tell us? The case of conscientiousness. Psychological Science, 19, 309-313.

34.

Henderson

S. J.

(2004). Product inventors and creativity: The finer dimensions of enjoyment. Creativity Research Journal, 16, 293-312.

35.

Hofstede

McCrae

R. R.

(2004). Personality and culture revisited: Linking traits and dimensions of culture. Cross-Cultural Research, 38, 52-88.

36.

Holland

J. L.

(1985). Self-directed search–1985 edition. Odessa, FL: Psychological Assessment Resources.

37.

Homan

A. C.

Hollenbeck

J. R.

Humphrey

S. E.

Van Knippenberg

Ilgen

D. R.

Van Kleef

G. A.

(2008). Facing differences with an open mind: openness to experience, salience of intragroup differences, and performance of diverse work groups. The Academy of Management Journal, 51, 1204-1222.

38.

INSEAD. (2010). Global innovation index 2009-2010. Retrieved October 14, 2010, from http://www.globalinnovationindex.org/gii/main/home.cfm

39.

Jirotka

Procter

Hartswood

Slack

Simpson

Coopman

. . . Voss

(2005). Collaboration and trust in healthcare innovation: The eDiaMoND Case Study. Computer Supported Cooperative Work, 14, 369-398.

40.

John

O. P.

Donahue

E. M.

Kentle

R. L.

(1991). The Big Five Inventory—versions 4a and 54. Berkeley: University of California.

41.

John

O. P.

Srivastava

(1999). The Big Five taxonomy: History, measurement, and theoretical perspectives. In Pervin

L. A.

John

O. P.

(Eds.), Handbook of personality: Theory and research (2nd ed., pp. 102-139). New York, NY: Guildford.

42.

Kirton

(1976). Adaptors and innovators: A description and a measure. Journal of Applied Psychology, 61, 622-629.

43.

Kwang

N. A.

Rodrigues

(2002). A Big-Five personality profile of the adaptor and innovator. The Journal of Creative Behavior, 36, 254-268.

44.

Lambert

S. J.

Fairweather

J. R.

(2010). Innovation networks and pathways to success or failure in New Zealand: A qualitative comparative analysis of technology users who innovate (Agribusiness and Economics Research Unit [AERU] Research Report). Canterbury, New Zealand: Lincoln University.

45.

LePine

J. A.

Van Dyne

(2001). Voice and cooperative behavior and contrasting forms of contextual performance: Evidence of differential relationships with Big Five personality characteristics and cognitive ability. Journal of Applied Psychology, 86, 326-336.

46.

McCrae

R. R.

(1987). Creativity, divergent thinking, and openness to experience. Journal of Personality and Social Psychology, 52, 1258-1265.

47.

McCrae

R. R.

(2001). Trait psychology and culture: Exploring intercultural comparisons. Journal of Personality, 69, 819-846.

48.

McCrae

R. R.

Allik

(2002). The Five-Factor Model of Personality across cultures. New York, NY: Kluwer Academic.

49.

McCrae

R. R.

Costa

P. T.

(1997). Conceptions and correlates of Openness to Experience. In Hogan

Johnson

Briggs

(Eds.), Handbook of personality psychology (pp. 825-847). San Diego, CA: Academic Press.

50.

McCrae

R. R.

Costa

P. T.

Martin

T. A.

(2005). The NEO-PI-3: A more readable Revised NEO Personality Inventory. Journal of Personality Assessment, 84, 261-270.

51.

McCrae

R. R.

Terracciano

(2008). The five-factor model and its correlates in individuals and cultures. In van der Vijver

F. J. R.

van Hemert

Poortinga

Y. H.

(Eds.), Multilevel analysis of individuals and cultures (pp. 249-282). Mahwah, NJ: Lawrence Erlbaum.

52.

McCrae

R. R.

Terracciano

& 78 Members of the Personality Profiles of Cultures Project. (2005). Universal features of personality traits from the observer’s perspective: Data from 50 cultures. Journal of Personality and Social Psychology, 88, 547-561.

53.

McCrae

R. R.

Terracciano

& 79 Members of the Personality Profiles of Cultures Project. (2005). Personality profiles of cultures: Aggregate personality traits. Journal of Personality and Social Psychology, 89, 407-425.

54.

Miron

Erez

Naveh

(2004). Do personal characteristics and cultural values that promote innovation, quality, and efficiency compete or complement each other? Journal of Organizational Behavior, 25, 175-199.

55.

Murphy

J. T.

(2002). Networks, trust, and innovation in Tanzania’s manufacturing sector. World Development, 30, 591-619.

56.

Murray

H. A.

(1943). Thematic apperception test manual. Cambridge, MA: Harvard University Press.

57.

Plomin

Owen

M. J.

McGuffin

(1994). The genetic basis of complex human behaviors. Science, 261, 1733-1739.

58.

Rammstedt

John

O. P.

(2006). Measuring personality in one minute or less: A 10-item short version of the Big Five Inventory in English and German. Journal of Research in Personality, 41, 203-212.

59.

Rank

Frese

(2008). The impact of emotions, moods and other affect-related variables on creativity, innovation and initiative. In Ashkanasy

N. M.

Cooper

C. L.

(Eds.), Research companion to emotion in organizations (pp. 103-119). Northampton, MA: Edward Elgar.

60.

Robinson

W. S.

(1950). Ecological correlations and the behavior of individuals. American Sociological Review, 15, 351-357.

61.

Rodriguez

Rodrik

(2001). Trade policy and economic growth: A skeptic’s guide to the cross-national evidence. In Bernanke

Rogoff

K. S.

(Eds.), NBER Macroeconomics Annual 2000. Cambridge: MIT Press.

62.

Rokeach

(1973). The nature of human values. New York, NY: Free Press.

63.

Rolland

J.-P.

(2002). Cross-cultural generalizability of the five-factor model of personality. In McCrae

R. R.

Allik

(Eds.), The five-factor model of personality across cultures (pp. 7-28). New York,NY: Kluwer Academic.

64.

Ross

S. R.

Rausch

M. K.

Canada

K. E.

(2003). Competition and cooperation in the five-factor model: Individual differences in achievement orientation. The Journal of Psychology, 137, 323-337.

65.

Schmitt

D. P.

Alcalay

Allensworth

Allik

Ault

Austers

(2002). Patterns and universals of adult romantic attachment across 62 cultures: Are models of self and other pancultural constructs?. Journal of Cross-Cultural Psychology, 35, 367-402.

66.

Schmitt

D. P.

Allik

McCrae

R. R.

Benet-Martínez

(2007). The geographic distribution of Big Five personality traits: Patterns and profiles of human self-description across 56 nations. Journal of Cross-Cultural Psychology, 38, 173-212.

67.

Shalley

C. E.

Zhou

Oldham

G. R.

(2004). The effects of personal and contextual characteristics on creativity: Where should we go from here? Journal of Management, 30, 933-958.

68.

Soto

C.J

John

O.P.

(2009). Ten facet scales for the Big Five Inventory: Convergence with NEO PI-R facets, self-peer agreement, and discriminant validity. Journal of Research in Personality, 43, 84-90.

69.

Sternberg

(2000). Innovation networks and regional development—evidence from the European Regional Innovation Survey (ERIS): Theoretical concepts, methodological approach, empirical basis, and introduction to the theme issue. European Planning Studies, 8, 389-407.

70.

Suliman

A. M.

Al-Shaikh

F. N.

(2007). Emotional intelligence at work: Links to conflict and innovation. Employee Relations, 29, 208-220.

71.

Terracciano

& 65 others. (2005). National character does not reflect mean personality trait levels in 49 cultures. Science, 310, 96-100.

72.

Urban

K. K.

Jellen

H. G.

(1996). Test for Creative Thinking-Drawing Production (TCT-DP) manual. Frankfurt, Germany: Swets Test Services.

73.

Wineman

J. D.

Kabo

F. W.

Davis

G. F.

(2009). Spatial and social networks in organizational innovation. Environment and Behavior, 41, 427-442.

74.

Zaltman

Duncan

Holbeck

(1973). Innovations and organizations. New York, NY: John Wiley.

75.

Zhao

Seibert

S. E.

(2006). The Big Five personality dimensions and entrepreneurial status: A meta-analytical review. Journal of Applied Psychology, 91, 259-271.