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Journal of Managerial Psychology Personality and learning processes underlying maverickism Elliroma Gardiner Chris J Jackson

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Personality and learning processes underlying maverickism

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Received 26 July 2012 Revised 21 March 2013 6 September 2013 11 March 2014 Accepted 11 March 2014

Elliroma Gardiner School of Applied Psychology, Griffith University, Brisbane, Australia, and

Chris J. Jackson Australian School of Business, University of New South Wales, Sydney, Australia Abstract Purpose – Maverickism is the tendency of an individual to be socially competent, creative, goal focussed, risk-taking and disruptive. Previous research with the five-factor model (FFM) shows that individuals high in maverickism exhibit both functional and dysfunctional tendencies. The purpose of this paper is to compare and contrast the descriptive FFM with the process-oriented hybrid model of learning in personality (HMLP), in the prediction of maverickism. Design/methodology/approach – Employing a cross-sectional design with 490 full-time workers the authors use the NEO-International Personality Item Pool and the Learning Styles Profiler to examine differences in the FFM and HMLP in the prediction of maverickism. Findings – Results with the FFM, identify extraversion, openness and (low) agreeableness as significant predictors of maverickism. All factors of the HMLP (except conscientious learning) significantly predict maverickism. Hierarchal regression analysis shows that the HMLP accounts for an additional 21 percent of variance in maverickism over and above that of the FFM. Research limitations/implications – The authors have tested and built theory by identifying not only what predicts maverickism, but also how the learning processes of the HMLP interrelate to predict maverickism. Practical implications – Managers interested in developing the maverick potential of their employees will find this study useful because it identifies what to look for in maverick workers. Social implications – Individuals high in maverickism have the potential for radical innovation. Understanding how to identify and develop these individuals may lead to larger societal benefits. Originality/value – The authors are the first to use the HMLP to test maverickism. The research highlights the importance of both personality and learning processes in maverickism. Keywords Learning style, FFM, HMLP, Maverickism, Sensation seeking Paper type Research paper

Journal of Managerial Psychology Vol. 30 No. 6, 2015 pp. 726-740 © Emerald Group Publishing Limited 0268-3946 DOI 10.1108/JMP-07-2012-0230

Individuals who are independent thinkers, decisive and goal focussed are often described as high in maverickism (Blohowaik, 1992). These individuals are thought to be experts in exploiting opportunities and creatively solving business challenges (e.g. Blohowaik, 1992; Cheverton et al., 2001; Taylor and LaBarre, 2006). Although very little formal research has been conducted on maverickism there seems to be some intuitive appeal that doing things differently can sometimes be advantageous. We view maverickism as a continuous variable rather than as a type or category. We see high scorers as eccentric, risk-taking and disruptive but also talented, success-oriented and engaging in goal-directed behavior (Cheverton et al., 2001; Taylor and LaBarre, 2006). Individuals high in maverickism are comfortable in making decisions and persevering in actions which go against the status quo (e.g. Clouse, 1997; Charlton, 2008). We conceptualize individuals low in maverickism as steady, team-oriented individuals who favor conventional approaches to risky ones.


Our current aim is to broaden our understanding of maverickism by comparing and contrasting the descriptive five-factor model (FFM) (Costa and McCrae, 1992; McCrae and Costa, 1997) of personality with Jackson’s (2008) process-oriented hybrid model of learning in personality (HMLP). We want to identify what predicts maverickism as well as understand how these factors interact to provide an explanation of maverickism. Our approach is interesting because models of personality are typically applied to either functional (e.g. work performance) or dysfunctional outcomes (e.g. gambling) rather than both. Here we choose an outcome variable which we conceptualize as being functional, in terms of being related to talent and success, and dysfunctional, in terms of being related to disruption and unconventionality. FFM The FFM is a descriptive taxonomy of personality that reduces the traits of personality to five orthogonal factors, extraversion, openness, agreeableness, neuroticism and conscientiousness (Costa and McCrae, 1992). Arguably the most well-known model of personality, the FFM has been found to significantly predict a wide range of organizational relevant outcomes such as job performance (Barrick and Mount, 1991), organizational commitment (Erdheim et al., 2006) and career success (Seibert and Kraimer, 2001). Work by Gardiner and Jackson (2012) report extraversion, openness to experience and low agreeableness as positively related to maverickism. Extraversion appears to provide the requisite positive affect and social skills required by individuals high in maverickism to persuade others to follow their way of thinking. The strong significant effect of openness offers some evidence of the interlinking nature of these constructs, where openness is responsible for the inquisitive, non-conformist and imaginative tendencies of maverickism. Individuals high in maverickism are disruptive and comfortable disagreeing with others. Thus it is hardly surprising that Gardiner and Jackson (2012) found that low (and not high) agreeableness predicted maverickism. Supported by research identifying mavericks as poor team players (Charlton, 2008), individuals high in maverickism are better characterized as competitive rather than altruistic. Based on this previous research we test the following hypotheses: H1a. Extraversion is positively related to maverickism. H1b. Openness is positively related to maverickism. H1c. Agreeableness is negatively related to maverickism. The work of Gardiner and Jackson (2012) is important as it provides an initial idea of what personality factors best predict maverickism, however, this work is incomplete. Gardiner and Jackson (2012) did not hypothesize or find any significant relationships between maverickism and the remaining FFM variables. In the current research we aim to test and build theory by offering and testing specific hypotheses linking maverickism to neuroticism and conscientiousness, respectively. Individuals high in neuroticism are sensitive to environmental threats and avoid situations which require taking control (Judge et al., 1997; Mathews and MacLeod, 2005) so it seems unlikely that high neuroticism would positively predict maverickism. However, although low scorers on neuroticism may react more functionally to stress (Bolger and Zuckerman, 1995) they are also unlikely to have the requisite energy to

Learning processes underlying maverickism 727

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persevere and overcome challenges, so perhaps high neuroticism underscores maverickism. Based on this rationale we test the following non-directional hypothesis: H1d. Neuroticism is related to maverickism.


728

Similarly, it is not clear whether high or low conscientiousness is likely to predict maverickism. Conscientiousness reflects a tendency to be dependable and risk aversive (Barrick and Mount, 1991). Individuals high in conscientiousness exhibit motivation for achievement (Stewart, 1996) and are often high performers (Judge et al., 1999). Although a desire for achievement is central to both conscientiousness and maverickism, individuals high in conscientiousness achieve by following rules (e.g. Watson et al., 1994) whereas individuals high in maverickism achieve despite rules (e.g. Seitz, 1983). One could therefore make an argument for both high and low conscientiousness as predicting maverickism. Therefore, we test the following non-directional hypothesis: H1e. Conscientiousness is related to maverickism. HMLP Although the HMLP was developed independently of the theory underlying rational emotive behavior therapy (REBT) (see Ellis, 2004; Jackson et al., 2012 for an evidence-based account of the overlap), the similarity between the models is instructive in understanding how initial drives can be honed into functional goal-oriented behavior or dysfunctional and irrational behavior. HMLP and REBT are likely therefore to be useful models for understanding maverickism. The central focus of REBT is that irrational beliefs, which are stable, illogical and at odds with reality, lead to psychological disturbance. The consequence of holding irrational beliefs is that people can develop dysfunctional behaviors at odds with reality (see Ellis, 2004) that are potentially associated with the maverick’s disruptive, unconventional and eccentric behaviors. Ellis (2004) argued that rationality develops from checking and replacing initial undirected impulses, habits or beliefs with a more considered choice. Ellis (2004) did not regard initial impulses as necessarily unadjusted or irrational and proposes that human beings are exceptionally complex in which there is neither a single way of becoming functional nor dysfunctional. Jackson’s (2008) HMLP provides theory-based and testable cognitive mechanisms that add depth to the general framework of REBT (Jackson et al., 2012). Like REBT, HMLP is a model of learning beginning with undirected impulses that lead to the development and maintenance of rationality or irrationality. It aims to provide a model of functional and dysfunctional behavior and suggests that there are numerous pathways from initial impulses to the holding of rational or irrational beliefs. Based on previous research conducted with the HMLP by Jackson and colleagues, Figure 1 illustrates how we hypothesize the components of the HMLP interrelate to predict maverickism. In the HMLP, the basis of personality is sensation seeking which is widely accepted as an important biological dimension of personality (Zuckerman, 1994). Although sensation seeking is typically linked to risk-taking and negative outcomes (e.g. Zuckerman, 1994), it can also have positive outcomes related to learning (e.g. being faster and more focussed learners, Ball and Zuckerman, 1990, 1992). Others also advocate that the dysfunctional consequences of sensation seeking have been over-stated (e.g. Arnett, 1994; Roth et al., 2009). Based on this emerging consensus,

Learning processes underlying maverickism

Mastery H2c H2b

H2b Rationality H2d

729

H2c

H2e Conscientious Learning (HMLP)


Sensation Seeking

Maverickism H2a H2d

H2d Deep Learning

Jackson and colleagues (Jackson, 2008, 2011; Jackson et al., 2009a, 2012) argue that sensation seeking is a neutral drive related to being curious and exploratory. Jackson therefore claims that initial impulses for action are neither necessarily good nor bad which is in agreement with Ellis (2004). Elliot and Thrash (2002), Humphreys and Revelle (1984) and Jackson and Francis (2004) argue that functional human behavior is regulated by higher order cognitive mechanisms that re-express biologically based energizing drives as complex, success-oriented and adaptive behavior. In this study, we use the word “re-express” to signify that sensation seeking indirectly influences maverickism through a range of higher order cognitive processes. We use the word “hone” to indicate how cognitive mechanisms improve the general drive of sensation seeking such that functional outcomes develop and are maintained. According to the HMLP, the failure to hone sensation seeking leads to unrestrained and dysfunctional behavior (Jackson, 2011; O’Connor and Jackson, 2008) which may also be associated with the unrestrained behavior associated with mavericks. Since this seems to be a key component of maverickism, we hypothesize that: H2a. Sensation seeking is positively related to maverickism. The HMLP provides the opportunity to study underlying learning mechanisms associated with achieving functional and dysfunctional outcomes. This model aims to unite the biological, socio-cognitive and the socio-experiential research foci in personality psychology as discussed by Jackson et al. (2009a). The closest similar model is Cloninger’s temperament and character inventory which spans biological and socio-cognitive research foci (Cloninger et al., 1993). This is the first application of the HMLP to maverickism. In the HMLP, the higher-order mechanisms that hone sensation seeking are represented by socio-cognitive variables which are well understood in the literature (Jackson et al., 2009a). Conscientious learning is derived from the FFM’s conscientiousness factor (Costa and McCrae, 1992), and is related to achieving success through social responsibility, hard work and persistence[1]. Rationality is concerned with emotional independence and objectivity and its absence leads to irrational thought processes

Figure 1. The proposed relationship between the HMLP and maverickism

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( Jackson et al., 2012). The experiential-cognitive component of the model is captured by deep learning which indicates a proactive interest in acquiring information and reflecting deeply on issues loosely representing a part of Kolb’s (1984) model of experiential learning. To date, Jackson and colleagues have provided evidence of how cognitive mechanisms re-express sensation seeking through indirect paths to predict organizational, educational and other outcomes (Jackson, 2011; O’Connor and Jackson, 2008; Jackson et al., 2009a, b). One path concerns the re-expression or honing of sensation seeking by mastery, also referred to as learning goal orientation (Dweck and Leggett, 1988; VandeWalle and Cummings, 1997), which leads individuals to invest effort and allocate cognitive resources to ensure the success of exploratory behavior. The honing of energizing drives by mastery was regarded as a basic mechanism of personality by Elliot and Thrash (2002). O’Connor and Jackson (2008), Jackson (2011) and Jackson et al. (2009a) find strong evidence of an indirect pathway from sensation seeking through mastery in the prediction of various functional outcomes, including maze completion, supervisor and self-rated work performance, self-reported school performance, university performance, and entrepreneurial behavior. Since goal-achievement is a key behavioral outcome of those high in maverickism (Clouse, 1997; Taylor and LaBarre, 2006) we hypothesize: H2b. Sensation seeking is positively related to mastery, and mastery is positively related to maverickism. The HMLP further argues that functional outcomes are associated with the re-expression of sensation seeking through mastery and rationality such that curiosity and exploration is honed successively by goal focus and cognitive effort (i.e. mastery) and then by rationality (i.e. emotional independence, autonomy and objectivity). Jackson et al. (2009a) found support for this pathway in two diverse student groups from Uganda and Australia. Jackson et al. (2012) find support for this path in the prediction of emotional well-being in a sample of depressed patients. However, rationality is not typically associated with high maverickism given maverickism is related to impatience, non-conformity and being against established procedures (Clouse, 1997; Dutton, 1973; Seitz, 1983). Therefore, in line with this evidence and the theory underlying REBT, we propose a further pathway from mastery to maverickism: H2c. Mastery is positively related to rationality and rationality is negatively related to maverickism. A further pathway from sensation seeking, through deep learning and conscientious learning to rationality has been proposed and tested by Jackson et al. (2009a, 2012). In this pathway, sensation seeking is re-expressed through a series of higher order experiences and cognitions in which curiosity is successively honed by active reflection, sustained hard work and rationality. This indirect pathway to functional learning can be summarized as exploring→reflecting→persisting→rationality→ functionally learned outcome. This pathway loosely resembles the overall content of experiential learning cycles (such as that proposed by Kolb, 1984 and later researchers) but differs in that content is developed from prominent and widely known biological, socio-cognitive and socio-experiential research foci and it represents a testable mechanism of learning as opposed to a cycle. We argue that the indirect pathway from sensation seeking through deep learning and conscientious learning to rationality will predict maverickism. Individuals high in

maverickism pursue and persevere to achieve risky goals because they are inherently invested in the outcome, as such they tend to live a “growth-oriented life” (Clouse, 1997, p. 2). Accordingly, they are likely to be deep learners. We propose that people who hone exploratory behavior through being reflective, persistent but also intuitive and low in rationality (i.e. emotionally attached as opposed to independent and rational) will be high in maverickism. We hypothesize a further pathway from sensation seeking to rationality and then to maverickism (although this latter path is already part of H2c):


H2d. Sensation seeking is positively related to deep learning, deep learning is positively related to conscientious learning and conscientious learning is positively related to rationality. The most similar personality construct in Jackson’s (2005, 2008) HMLP and the FFM is conscientious learning. In HMLP, conscientious learning concerns perseverance and social responsibility. In some research, perseverance is thought to be an important trait of individuals high in maverickism (Clouse, 1997); however we have already argued that mavericks may not be hard working in the conventional socially responsible sense so we do not hypothesize that conscientious learning will significantly predict maverickism. Finally, Jackson et al. (2009a) report that the path from sensation seeking to rationality is negative once the positive functional components are redirected through mastery and deep learning. The remainder is the dysfunctional primitive drive of sensation seeking related to impulsive action lacking functional goal direction (e.g. “I want to get rich quick and I don’t care about the long term”) which leads to low rationality. We hypothesize: H2e. Sensation seeking is negatively related to rationality. FFM and HMLP According to Jackson (2005, 2008), functional learners seek stimulation and change, have a good understanding of goals, hard work and effort, are objective and have a deep understanding of how things work. Such individuals are likely to be high in maverickism except that we think that maverickism also has dysfunctional tendencies likely associated with low rationality and low-conscientious learning. This viewpoint is supported by others who advocate that people high in maverickism are driven to succeed by being inventive and goal focussed but that they are also risk takers and rule breakers (Ray et al., 1997; Seitz, 1983). We expect both the FFM and HMLP to predict maverickism. However, we argue that the HMLP has several advantages over the FFM in mapping maverickism. First, unlike the FFM which is widely criticized as being atheoretical (see Block, 1995), the HMLP is strongly grounded in theory and has overlap with the well-established clinical model of REBT. Second, although the FFM is thought to tap into most attitudes and behaviors, there is compelling evidence to suggest that there are a number of constructs (such as being cunning, which one might associate with maverickism) that are beyond the FFM (see Saucier and Goldberg, 1998 for a review). Third, the FFM is a descriptive rather than process-oriented model like the HMLP. If our reasoning that maverickism consists of both functional and dysfunctional components holds, than the HMLP will predict more variance in mavericksim than the FFM. This leads to our final hypotheses: H3a. The HMLP will incrementally explain more variance in maverickism than the FFM.


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Methods Participants and procedure Participants were 268 male and 222 female (n ¼ 490) full-time workers (mean tenure ¼ 7.78 years, SD ¼ 8.41) who were recruited via a research web site. Participants were primarily Australian (76.3 percent) aged between 18 and 69 years (mean ¼ 40.0, SD ¼ 13.22) who had some managerial responsibilities (32 percent senior managers, 27 percent junior managers). Reported education levels ranged from high-school (32 percent) to attainment of a university degree (49 percent). Participants were from various industries (42 percent service, 18 percent education, 12 percent retail), with just over half working in large-sized businesses (33 percent small, 16 percent medium). Presentation of the online measures was randomized. Measures NEO-International Personality Item Pool (2001) measured the FFM factors of neuroticism, extraversion, openness, agreeableness and conscientiousness on a five-point Likert scale. This 50-item measure is a widely used test with high reliability and validity (Goldberg, 1999). α’s for each of the scales were good (α ⩾ 0.77, see Table I). Learning Styles Profiler measured the HMLP (Jackson, 2005) consisting of sensation seeking (e.g. “I am excited by what is new in my field”), mastery (e.g. “I achieve specific goals that I set myself”), conscientious learning (e.g. “I usually think carefully before doing things”), rationality (e.g. “I often feel a lack of control over the direction my life is taking”) and deep learning (e.g. “I enjoy working on a project that involves a great deal of library research”). Each scale had 15 items that were answered on a three-point Likert scale (2 ¼ True, 0 ¼ False, 1 ¼ Cannot decide). α’s of all scales have been reported as satisfactory (e.g. α ⩾ 0.69, Jackson, 2005; O’Connor and Jackson, 2008). Maverickism scale (Gardiner and Jackson, 2012) consisted of seven items. The scale measures the tendency of individuals to be successful performers as a result of taking risks, being eccentric and unorthodox. Items were answered using a three-point scale (2 ¼ True, 1 ¼ Cannot decide, 0 ¼ False). An example item is “I have a way of solving problems which is different from other people.” Low scorers tend to adopt a conventional approach when pursuing and achieving goals and are less open than high scorers to making risky decisions. Both high and low scorers have the potential to be successful, however unlike high scorers; success for low scorers is achieved by following rules rather than breaking them. The scale has good internal consistency (α ¼ 0.72). Results Overview We conducted three separate regressions: one to predict maverickism with the FFM, one using the HMLP and another where both the FFM and HMLP were entered in separate steps. We then conducted a path analysis to determine if processes underlying functional learning predicted maverickism. Sex was controlled. Model fit was determined by the goodness of fit index (GFI), adjusted goodness of fit index (AGFI), comparative fit index (CFI) and root mean square of approximation (RMSEA). Paths are based on those previously proposed and tested by Jackson et al. (2009a). Descriptives Table I presents the descriptive statistics of all the measures. The FFM is designed to be relatively orthogonal whereas the HMLP is a process model in which scales are necessarily more intercorrelated (as shown in Table I). There was also some overlap between the FFM and HMLP.

1. Sex – 2. Neuroticism 28.67 3. Extraversion 31.26 4. Openness 36.24 5. Agreeableness 37.98 6. Conscientiousness (FFM) 35.26 7. Sensation seeking 21.65 8. Mastery 22.29 9. Deep learning 22.75 10. Conscientious learning (HMLP) 19.61 11. Rationality 17.04 12. Maverickism 8.34 Notes: n ¼ 490. *p ⩽ 0.05; **p ⩽ 0.01

– 7.16 7.19 5.62 5.88 5.76 5.81 5.41 5.24 6.78 5.82 3.61

Mean SD – 0.24** 0.04 0.02 0.25** −0.01 −0.03 0.05 0.09* 0.07 −0.03 −0.12**

1

3

(0.86) −0.15** (0.87) −0.05 0.25** −0.05 0.35** −0.22** 0.03 −0.19** 0.36** −0.25** 0.33** 0.09* 0.13** −0.12** −0.03 −0.42** 0.17** −0.04 0.13**

2

5

6

(0.78) 0.28** (0.83) 0.27** 0.13** (0.77) 0.36** 0.23** 0.03 0.36** 0.18** 0.30** 0.35** 0.14** −0.05 0.09 0.19** 0.46** 0.31** 0.23** 0.30** 0.27** −0.02 0.10*

4

(0.77) 0.61** 0.32** 0.20** 0.09* 0.41**

7

8

9

10

11

12

(0.76) 0.17** (0.70) 0.41** 0.17** (0.72) 0.32** −0.09* 0.25** (0.80) 0.40** 0.31** 0.15** −0.08 (0.72)



Table I. Descriptive statistics

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FFM Table II presents our first regression. Sex negatively predicted maverickism, indicating that high levels of maverickism were more likely to be associated with males than females. Extraversion, openness and (low) agreeableness all significantly predicted maverickism, accounting for 11.0 percent of variance. Thus H1a, H1b and H1c were supported. Despite a significant correlation between conscientiousness and maverickism, conscientiousness did not significantly predict maverickism in regression. Maverickism was not significantly predicted by neuroticism. Thus the other hypotheses associated with the FFM and maverickism was rejected. HMLP Sex and all the HMLP factors (except conscientious learning) significantly predicted maverickism (see Table III). These results provide evidence in favor of sensation seeking as a direct predictor of maverickism (H2a) as well as evidence of positive indirect effects of mastery (H2b) and deep learning (H2d) on maverickism. Rationality negatively predicted maverickism (H2c), supporting our reasoning that individuals high in maverickism are unlikely to form logical, emotionally independent cognitions when engaging in risky behavior. Although found to correlate significantly with maverickism, conscientiousness failed to predict maverickism in our model. We suspect this may be due to the conflicting way in which high scorers in conscientiousness and maverickism achieve goals. In line with previous studies we found a link between sensation seeking and rationality (H2e).

Step

Variable

1 2 Table II. Regression analysis with FFM in the prediction of maverickism

Sex Sex Neuroticism Extraversion Openness Agreeableness Conscientiousness (FFM) Notes: n ¼ 490. *p ⩽ 0.05; **p ⩽ 0.01

Step 1 2 Table III. Regression analysis with HMLP in the prediction of maverickism

Variable

Sex Sex Sensation seeking Mastery Deep learning Conscientious learning (HMLP) Rationality Notes: n ¼ 490. *p ⩽ 0.05; **p ⩽ 0.01

B

SE

β

R2

Fch (df)

−0.86 −0.74 0.05 0.39 0.96 −0.42 0.17

0.34 0.33 0.17 0.17 0.17 0.18 0.17

−0.12** −0.10* 0.01 0.11* 0.27** −0.12* 0.05

0.01 0.11

6.97 (1, 488)** 9.91 (5, 483)**

B

SE

β

R2

Fch (df)

−0.86 −1.11 0.55 1.22 0.71 0.04 −0.71

0.43 0.28 0.18 0.20 0.15 0.16 0.15

−0.12** −0.15** 0.15** 0.34** 0.20** 0.01 −0.20**

0.01 0.29

6.97 (1, 488)** 38.32 (5, 483)**

Table IV presents our final regression. Results show that an additional 21 percent of variance in maverickism could be attributed to the HMLP after accounting for the effects of participant sex and the FFM. H3 was supported. Figure 2 shows the hypothesized paths using sex and the HMLP scales as predictors of maverickism. Standardized parameter estimates are reported. H2a-H2e was supported. Goodness of fit was acceptable (GFI ¼ 0.984; AGFI ¼ 0.950; CFI ¼ 0.970; RMSEA ¼ 0.065).


Step

Variable


1 2

Sex Sex Neuroticism Extraversion Openness Agreeableness Conscientiousness (FFM) 3 Sex Neuroticism Extraversion Openness Agreeableness Conscientiousness (FFM) Sensation seeking Mastery Deep learning Conscientious learning (HMLP) Rationality Notes: n ¼ 490. *p ⩽ 0.05; **p ⩽ 0.01

B

SE

β

R2

Fch (df)

−0.86 −0.74 0.05 0.39 0.96 −0.42 0.17 −0.91 −0.04 −0.10 0.48 −0.37 0.19 0.60 1.05 0.60 0.07 −0.79

0.33 0.33 0.17 0.17 0.17 0.17 0.17 0.30 0.16 0.16 0.18 0.16 0.17 0.19 0.20 0.16 0.18 0.17

−0.12** −0.10* 0.01 0.11* 0.27** −0.12* 0.05 −0.13** −0.00 −0.00 0.13** −0.10* 0.05 0.17** 0.29** 0.17** 0.02 −0.22**

0.01 0.11

6.97 (1, 488)** 9.91 (5, 483)**

0.32

29.38 (5, 478)**

Table IV. Regression analysis with FFM and HMLP in the prediction of maverickism

Mastery 0.37** 0.34** 0.39**

Rationality Sex

0.61** 0.13**

–0.19**

–0.31**

–0.17** Conscientious Learning (HMLP) Sensation Seeking

Maverickism 0.15** 0.10** 0.20**

0.32** Deep Learning

Notes: n=490. Standardized estimates shown. Covariances have been omitted for ease of presentation but can be provided on request. **p-0.01

Figure 2. Path model of HMLP to maverickism

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Discussion Our aim was to compare and contrast the descriptive FFM with the process-oriented HMLP in the prediction of maverickism. Our study produced three major findings. First, we found that factors of the FFM significantly predicted maverickism. In line with previous research (Gardiner and Jackson, 2012), extraversion, openness and low agreeableness significantly predicted maverickism, supporting H1a, H1b and H1c, respectively. Our results echo common descriptions of maverickism as having strong linkages to reward sensitivity, creativity, open-mindedness and competitiveness. Our results also demonstrate the multifaceted nature of maverickism as consisting of both functional (e.g. extraversion, openness) and dysfunctional tendencies (e.g. low agreeableness). Given the dearth of empirical literature regarding maverickism, these results add to our empirical knowledge by providing new descriptive evidence of how the FFM relates to maverickism. Second, our results support the use of the HMLP in the prediction of maverickism. Results of three indirect pathways from sensation seeking through mastery (H2b), rationality (H2c) and deep learning and conscientious learning (H2d) to maverickism reaffirms Jackson’s (2005) assertion that functional behavior results from a re-expression of sensation seeking through higher order socio-cognitive mechanisms. We also find evidence of a direct pathway from sensation seeking to maverickism (H2a) suggesting that maverickism is at least partially driven by an unrestrained desire to approach and explore. The significant result of the structural model is in line with previous empirical studies investigating the application of the HMLP to various outcomes (e.g. Jackson et al., 2009a) and builds on this previous work by uncovering new evidence of dysfunctional links between sensation seeking and irrationality (H2e) as well as irrationality and maverickism (H2c). Congruent with the broad framework of REBT (Ellis, 2004), our findings make an important theoretical contribution by providing converging evidence that both functional and dysfunctional mechanisms of learning underlie maverickism. Third, our results provide full support for H3, demonstrating that in comparison to the descriptive FFM, the process-oriented HMLP accounts an additional 21 percent of variance in maverickism after controlling for the effects of participant sex and the FFM factors. This result indicates that although the FFM provides some description of how personality relates to maverickism, the HMLP provides a more comprehensive picture of the functional and dysfunctional learning processes underlying maverickism. Managerial implications Organizations have much to gain from research on maverickism. Our empirical findings have two important implications for practicing managers and HR professionals. First, our research demonstrates that learning and personality go hand in hand, such that our results indicate that while personality is a key predictor of maverickism, considering an individual’s learning processes is also important. Our results are in line with other research (e.g. Saucier and Goldberg, 1998) that the FFM alone may not be adequate in measuring the full range of attitudes and behavior of interest to managers. For those who would like to encourage their workers to be more innovative and risk-taking, understanding the learning processes involved should be useful when designing and delivering training packages or workshops aimed at tapping into workers “maverick potential.” Our research


with full-time workers, demonstrates that individuals high in maverickism learn through the setting and achievement of goals rather than being conscientious learners and rule-following. Second, this research corroborates our previous work with the maverickism scale (Gardiner and Jackson, 2012). Although still in the early stages, this scale appears to be a valid and useful predictor of creative and successful risk-taking behavior while also predicting the positive and functional components of maverickism. Given the known popularity and utility of the FFM in personnel selection (e.g. Hermelin and Robertson, 2001), managers who have FFM data for themselves and/or their employees may be able to use our findings to estimate where they and/or their employees sit along the maverickism continuum. Our finding of maverickism as consisting of both functional and dysfunctional aspects highlights the need for managers to take a balanced approach in providing a work environment that allows mavericks to be individualist while also recognizing that mavericks might need to be closely monitored. Although we think that maverickism may not be equally appropriate or beneficial across all work settings, we also think that the merits of the maverick should not be understated. Limitations and future research Despite our large sample size, our use of cross-sectional self-report design to our study is a limitation and also raises the issue of common method bias. We have tried to correct for threats to validity by enlisting only published scales with good psychometric properties, keeping participants blind to the aims of the study, randomizing the presentation of the questionnaires and testing a large and varied sample of full-time workers (Podsakoff et al., 2003). We see future research aimed at both replicating and extending our findings to determine whether or not contextual conditions are likely to influence maverickism. For instance, it would be interesting to determine whether manifestations of maverickism are more common in certain settings such as start-ups and new ventures as opposed to more established businesses. Research on maverickism has future potential to inform organizational policies around recruitment and selection as well as training and development. Conclusion Our current aim was to broaden our understanding of maverickism by comparing and contrasting the descriptive but psychometrically well-validated FFM of personality with Jackson’s (2005) process-oriented HMLP. Our research not only corroborates previous literature identifying the FFM factors of extraversion, openness and (low) agreeableness as significant predictors of maverickism (Gardiner and Jackson, 2012) but it also makes a new and important contribution by showing how the HMLP both predicts and explains processes associated with maverickism. This study makes an important theoretical contribution to the emerging literature on maverickism by implicating both functional and dysfunctional aspects of learning in maverickism. Note 1. Conscientious learning from the HMLP should not be confused with the similar yet distinct conscientiousness factor from the FFM.


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Corresponding author Dr Elliroma Gardiner can be contacted at: [email protected]

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