Identify leader traits and attributes

Journal of Leadership & Organizational Studies 18(4) 469 –479 © Baker College 2011 Reprints and permission: http://www. sagepub.com/journalsPermissions.nav DOI: 10.1177/1548051811404891 http://jlos.sagepub.com

The first and last task of a leader is to keep hope alive.

—Gardner (1968)

A leader not only speaks to immediate wants but elevates people by vesting in them a sense of possibility, a belief that changes can be made and that they can make them. Opportunity beckons where none had appeared before, and once seized upon opens another opportunity and another.

—Burns (2003, p. 239)

Research scholars in the behavioral sciences have been requesting that more research attention be given to the role of genes in human behavior (Wilson, 1975). Answering this call, researchers have begun using behavioral genetic meth- ods to investigate the biological basis of various personality characteristics, attitudes, and behaviors within organiza- tional settings (e.g., Arvey, McCall, Bouchard, Taubman, Cavanaugh, 1994; Arvey, Rotundo, Johnson, Zhang, & McGue, 2006; Ilies, Arvey, & Bouchard, 2006). For example, research shows a genetic component to many personality variables (e.g., Heath, Cloninger, & Martin, 1994; Loehlin, 1992): emotions (e.g., Bouchard & McGue, 2003), job sat- isfaction (e.g., Arvey, Bouchard, Segal, & Abraham, 1989), work values (e.g., Keller, Bouchard, Arvey, Segal, & Dawes, 1992), and entrepreneurship behaviors (e.g., Zhang et al., 2009). Recently, research efforts have been particularly

devoted to estimating genetic influences on leadership constructs (Arvey et al., 2006; Arvey, Zhang, Avolio, & Krueger, 2007; Johnson et al., 1998; Senior & Lee, 2009).

Employing behavior genetics methodologies but with different approaches to conceptualize leadership, research- ers have consistently found that a significant proportion of variation in leadership behaviors is explained by genetic influences with the remaining variance influenced by vari- ous environmental factors. The proportion of phenotypic variation attributable to genetic variance in an observed variable is defined as heritability. Recent studies have shown significant heritability in leadership constructs using self- reported measures of transactional and transformational leadership (e.g., Johnson et al., 1998) and leadership roles attained at work (Arvey et al., 2006; Arvey et al., 2007) as measures of leadership.

In the leadership literature, although evidence suggests that genetic factors influence leadership, in terms of the mediating processes, which genes influence such leadership

404891 JLO18410.1177/1548051811404891Chaturv edi et al.Journal of Leadership & Organizational Studies © Baker College 2011

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1Imperial College London, London, UK 2National University of Singapore, Singapore 3Arizona State University, Tempe, AZ, USA

Corresponding Author: Sankalp Chaturvedi, Imperial College Business School, Imperial College London, South Kensington Campus, 289 Tanaka Building, London, SW7 2AZ, UK Email: sankalp@imperial.ac.uk

Genetic Underpinnings of Transformational Leadership: The Mediating Role of Dispositional Hope

Sankalp Chaturvedi1, Richard D. Arvey2, Zhen Zhang3, and Paraskevi T. Christoforou2

Abstract

In this study, the authors investigate the extent to which dispositional hope mediates genetic influences on transformational leadership. Based on a sample of female twins (214 identical and 178 fraternal) from the Minnesota Twin Registry, results indicated that 53% of the variance in hope and 49% of the variance in transformational leadership were accounted for by genetic factors. After controlling for positive emotionality and negative emotionality, it was found that the genetic influence on transformational leadership was mediated by dispositional hope with the overlapping genetic factors explaining 20.8% of the total variance in transformational leadership.

Keywords

transformational leadership, dispositional hope, behavioral genetics

470 Journal of Leadership & Organizational Studies 18(4)

variables is still unclear. Researchers have argued that the relationships between genetic factors and leadership behav- iors can be mediated by psychological and physiological variables (e.g., Arvey & Bouchard, 1994; Ilies, Gerhardt, & Le, 2004; Senior & Lee, 2009); however, attempts to inves- tigate such mediating processes by which genetic factors influence leadership have been limited. Arvey et al. (2006) examined the mediating role of personality variables (social potency, achievement, and social closeness) in an attempt to explain the observed genetic influence on leadership but found little support for their mediating role because of low statistical power and small sample sizes.

To fill this research gap, the present study explores the mediating role of a theoretically relevant personality variable—dispositional hope—in the relationship between genes and transformational leadership. Although many researchers have argued for the critical role that hope plays in transformational leadership (as shown in the two opening quotes), limited attention has been paid to investigating the role of hope in the leadership processes (Helland & Winston, 2005; Shorey & Snyder, 2004), and no one has examined hope in explaining genetic influences on leadership. Therefore, the purpose of this study is twofold. First, we investigate whether hope is genetically influenced and second, we examine whether hope mediates the relationship between genetic factors and transformational leadership behaviors.

Genetic Foundations of Hope Within the positive organizational behavior literature, researchers argue that positive traits, states, and feelings positively affect overall psychological well-being, and hence employee performance (e.g., Luthans & Youssef, 2007; Peterson & Seligman, 2004). One of the most important positive traits is hope (Snyder, Harris, et al., 1991), which has been defined in various ways. Some earlier research emphasized the importance of goals and postulated that hope is an “overall perception that goals can be met” (see Stotland, 1969, for a detailed description).

In more recent research, hope has been defined as the perceived cognitive capacity and motivation to find and use routes to achieve desired goals. We adopt this definition for the current study and treat hope as “an enduring disposition that is subjectively defined” (Snyder, Harris, et al., 1991, p. 571). Dispositional hope is conceptualized as encom- passing two dimensions (Snyder, 1994; Snyder, Irving, & Anderson, 1991): the first, labeled pathways (waypower), entails the formulation of a plausible strategy for reaching a goal whereas the second, agency or willpower, involves the capacity to use that strategy to reach the goal. Snyder and colleagues argue that both components (agency and path- ways) reciprocally interact but represent different parts of goal-directed thinking. These authors provide evidence that

distinguishes hope from other related constructs in positive psychology—goal setting, self-efficacy, optimism, and pos- itive affectivity (Snyder, 1994, Snyder, Irving, et al., 1991). Since then, researchers typically measure hope as a disposi- tional variable and assume that it is relatively stable across situations and time (e.g., Elliott, Witty, Herrick, & Hoffman, 1991). Notably, Snyder et al. (1996) have also shown (in four studies) that hope can also be a “state,” and a measure of state hope can explain variance above and beyond dispo- sitional hope. In the current study, we focus on dispositional hope and examine its genetic foundations. Although evi- dence suggests that dispositional hope can be stable over time, no prior studies have examined its genetic underpinnings.

Behavioral genetics literature shows that most personal- ity variables are genetically influenced, leading to the pos- sibility that dispositional hope may also be genetic. The genetic basis of personality is well-documented in the litera- ture (e.g., Loehlin, McCrae, Costa, & John, 1998; Loehlin & Nichols, 1976; Tellegen et al., 1988). For example, Loehlin and Nichols (1976) found support for genetic foundations for various personality variables using a sample of twins and their parents after controlling for a similar sample of “non-twin” students. Similarly, Tellegen et al. (1988) ana- lyzed twin data of 11 personality traits and found significant genetic influences with heritabilities ranging from .39 to .58. Personality characteristics derived from the most widely used framework of personality variables—the “Big Five” (extraversion, agreeableness, conscientiousness, neuroti- cism, and openness”)—have been shown to be heritable. Loehlin and colleagues (Loehlin, 1992; Loehlin et al., 1998; Loehlin & Nichols, 1976; Loehlin & Rowe, 1992) have reported the heritability estimates of the Big Five personal- ity dimensions ranging from .39 to .49, using multiple sam- ples and studies.

Dispositional variables such as self-esteem, optimism, and well-being have been consistently shown to be posi- tively correlated with hope (e.g., Ciarrochi, Heaven, & Davies, 2007; Gibb, 1990; Snyder, 1994; Snyder, Irving, et al., 1991) and influenced by genetic factors. Specifically, self-esteem has been shown to be affected by genetic factors with a heri- tability estimate of .52, based on the Virginia Twin Registry (Roy, Neale, & Kendler, 1995). Furthermore, individual differences in well-being are reported to be highly heritable, particularly with regard to positive and negative affect (Lykken & Tellegen, 1996; Tellegen et al., 1988). Similarly, studies have shown a substantial genetic effect on optimism (Mosing, Zietsch, Shekar, Wright, & Martin, 2009; Plomin et al., 1992; Schulman, Keith, & Seligman, 1993). Therefore, we propose that genes influence a significant proportion of variance in dispositional hope.

Hypothesis 1: Genetics significantly influences dis- positional hope.

Chaturvedi et al. 471

Genetic Foundations of Transformational Leadership

The “nature” versus “nurture” debate in the leadership lit- erature has raged since researchers developed theories in leadership, with some suggesting that leaders are born (Galton, 1869) and others arguing that environmental and developmental factors are mainly responsible for leader- ship emergence and effectiveness (e.g., Hersey & Blanchard, 1969). But only until recently have researchers begun to explicitly examine the possibility of genetic influences on leadership in a scientific manner.

Johnson et al. (1998) made one of the first attempts to investigate genetic influences on leadership; they used 183 identical and 64 fraternal same-gender male and female twin pairs. Using the Multifactor Leadership Questionnaire (MLQ; Bass & Avolio, 1990) and other leadership mea- sures (i.e., adjective checklist items), they derived two fac- tors resembling transactional and transformational leadership dimensions. Transformational leadership refers to a leader- ship style that has four behavioral attributes that are interde- pendent and mutually reinforcing (Bass, 1985), including idealized influence, inspirational motivation, intellectual stimulation, and individualized consideration. Transactional leadership refers to a leadership style based on a “quid pro quo” relationship between leader and followers, in which rewards and/or punishments given by a leader are contin- gent on the performance of subordinates (Burns, 1978).

Johnson et al.’s (1998) results indicated that 48% and 59% of the variance in the transactional and transformational leadership dimensions, respectively, are genetically influ- enced. Another study was conducted by Arvey et al. (2006), wherein a sample of male twins from the Minnesota Twin Registry with 238 identical twin pairs and 188 fraternal twin pairs were used as subjects. In their study, a biographical approach was taken when measuring leadership where lead- ership was defined and measured in terms of the various for- mal and informal work role attainments of individuals in work settings (termed leadership role occupancy). Results showed that the proportion of variance due to genetic influ- ences on the leadership role occupancy scale is 30%. Finally, the most recent research effort by Arvey et al. (2007) used a similar measurement scheme and a female twin sample from the Minnesota Twin Registry involving 89 fraternal and 107 identical female twin pairs. This study replicated the finding of genetic influences (32%) on leadership role occupancy.

In the current study, we use the same female twin sam- ple from Minnesota Twin Registry to replicate the finding of Johnson et al. (1998) that transformational leadership is heritable.

Hypothesis 2: Genetics significantly influence trans- formational leadership.

Dispositional Hope as a Mediator of Genetic Influence on Transformational Leadership

Research on antecedents of leader emergence and leadership effectiveness has been of prime importance for researchers in the field of organizational behavior. In 1994, Arvey and Bouchard (1994) presented a conceptual model of genetic influences on work-related variables, suggesting that the effects of genetics on work behaviors such as leadership are mediated by personality and other variables. Since then, researchers have included genetic factors in models predict- ing leadership variables (e.g., Arvey et al., 2006; Ilies et al., 2004). Using behavior genetics’ methodologies, Johnson, Vernon, Harris, and Jang (2004) found that genetic factors influence all 20 traits from the Personality Research Forum (PRF), and that genetic factors are associated with both personality and transformational and transactional leader- ship behaviors, highlighting the mediating role of personality variables on the genetic influence on leadership. Similarly, Ilies et al. (2004) used a mediated model to test genetic influences on leadership emergence with intelligence with the Big Five personality variables as the mediators. Their results suggested that whether an individual emerges as a leader will depend extensively on individual personality traits that, in turn, are genetically influenced.

Specifically, we expected a positive relationship between dispositional hope and transformational leadership, which in turn points to the possible mediating role of hope in the genetic influence on transformational leadership. Research on hope suggests that individuals with a high level of hope tend to be more certain of their goals, readily adapt to changes, and are less anxious in stressful situations (Snyder, 2000; Snyder et al., 1997). Thus, those high in dispositional hope have a better chance of motivating themselves by establishing attrac- tive goals (i.e., the vision) and enhancing charisma. Shorey and Snyder (2004) conceptualize hope with both components— agency and pathways—as a common process in leadership. Possessing hopefulness is considered to be an implicit and integral part of transformational leadership (Burns, 2003) by helping leaders expend requisite energy to pursue and attain goals. Hopefulness also acts as a catalyst for transformational leaders to support goal-directed thinking to achieve goals within their organization. Being hopeful also helps leaders remain inspired and motivated in pursuing goals.

To summarize, although there can be multiple mediating processes (e.g., intelligence, extraversion, openness to expe- rience, etc.) by which genes can influence transformational leadership, we hypothesize that one of those mechanisms is dispositional hope.

Hypothesis 3: The genetic influence on transforma- tional leadership is partially mediated by disposi- tional hope.

472 Journal of Leadership & Organizational Studies 18(4)

Method Sample

The sample for this study was drawn from the Minnesota Twin Registry, which located twin pairs born in the state from 1936 to 1951 (Lykken, Bouchard, McGue, & Tellegen, 1990). Surveys were sent to a random sample of 500 twin pairs out of the 1,317 female twin pairs in the registry; a total of 596 surveys were returned for a response rate of 60%. Of the returned surveys, 392 included both members of the twin pair, yielding a pair-level response rate of 39%. Of the 196 twin pairs, 107 were identical or monozygotic (MZ) twins, and 89 were fraternal or dizygotic (DZ). The zygosity (MZ or DZ) of the twins in the Minnesota Twin Registry was established by Lykken et al. (1990). The twins included in this study were reared together during their childhood. In this sample set, 98% were White and 77% were married.

Table 1 presents sample characteristics of the female database of the Minnesota Twin Registry. Most of the twins in the registry were educated and went to high school and on to further education. The chi-square difference test showed no significant difference between MZ and DZ twins on all educational and occupational items; however, the t test showed that DZ twins were older than the MZ twins (t = 3.35, p < .05, d = −.34). As age might act as a confound, it was controlled when genetic influences were estimated. Table 2 provides the means, standard deviations, reliability, and correlations of the variables used in this study and shows that hope is posi- tively correlated with the transformational leadership (r = .56, p < .001, two-tailed test). Transformational leadership is significantly correlated with age (r = −.11, p < .05), positive emotionality (r = .48, p < .001), and negative emotionality (r = −.18, p < .0015), which hints that these variables should be controlled for in the structural equation modeling (SEM) models.

Table 1. Sample Characteristics of the Female Twins (Replicated From Arvey et al., 2007)

Identical Twin (n = 214)

Fraternal Twin (n = 178) Total (N = 392)

Age in years; mean (SD) 57.1 (5.8) 59.1 (6.0) 58.0 (6.0) Occupation (%)

Managerial and administrative 11 8 10 Professional, paraprofessional, and technical 27 21 24 Sales and related 6 6 6 Clerical and administrative support 20 16 18 Service 8 13 10 Agricultural, forestry, fisheries, and related occupations 0 1 1 Production, construction, operations, maintenance, and material handling 3 3 3 Education (%) Less than high school 4 4 4 High school 43 49 46 Two-year college/vocational school 24 23 23 BA/BS 21 19 20 MA/MBA 7 5 6 PhD/JD/MD 1 0 1

Note. Sample characteristics are based on individual twin rather than twin pair.

Table 2. Means, Standard Deviations, Reliability, and Correlations

Variable Mean SD N 1 2 3 4 5

1. Age 58.00 5.99 390 — 2. Positive emotionalitya 140.55 10.63 387 −.03 (.90) 3. Negative emotionalitya 118.14 10.63 387 .01 −.22*** (.89) 4. Dispositional hope 6.03 0.99 384 −.02 .53*** −.28*** (.83) 5. Transformational leadership 3.68 .67 395 −.11* .48*** −.18*** .57*** (.92)

Note. N is sample size. Internal consistency reliability is reported diagonally in parentheses. a. Based on the composite scales. *p < .05. **p < .01. ***p < .001 (two-tailed test).

Chaturvedi et al. 473

Measures

Transformational leadership. We used the self-report MLQ developed by Bass and Avolio (1990), which is currently the most widely used measure of this construct. This instru- ment includes 20 items and captures four dimensions of transformational leadership: intellectual stimulation, indi- vidualized consideration, inspirational motivation, and ide- alized influence (attributed and behavioral). Each participant responded to his/her behavior on 5-point Likert-type scale ranging from 0 (not at all) to 4 (frequently, but not always). Given our focus on transformational leadership, we com- bined responses to all transformational leadership items to form a measure of the construct. The reliability estimate for this measure was .92. Furthermore, as it was a self-report measure, we transformed the final score of transformational leadership variable to normalize distribution.

Hope. The measure for hope was composed of eight items taken from the Dispositional Hope Scale developed by Snyder, Harris, et al. (1991). The agency and pathways sub- scale each comprised four items. Items were anchored on a 7-point scale ranging from 1 (definitely false) to 7 (definitely true). Sample items for the agency and pathways subscales included “I energetically pursue my goals” and “There are a lot of ways around any problem.” Cronbach’s alpha reli- abilities were .80 and .72 for the agency and pathway sub- scales, respectively. The overall reliability coefficient was .83. The correlation coefficient between agency and path- ways’ subscales was .59. In this study, in accordance with the past studies on dispositional hope, we used the disposi- tional hope score as the sum of these two subscales.

Control variables. We controlled for age, positive emotion- ality, and negative emotionality while testing the hypothe- sized model. The positive and negative emotionality scales were derived from the self-report Multidimensional Person- ality Questionnaire (MPQ; Tellegen, 1982), which is com- posed of 11 trait scales. Tellegen and colleagues (Tellegen, 1982; Tellegen et al., 1988; Tellegen & Waller, 2001) named the first and second higher order factors of MPQ as positive and negative emotionality, respectively. Positive emotional- ity is the higher order composite of well-being, social potency, achievement, and social closeness. There were 72 items in total, and the Cronbach alpha of the scale was .90. The nega- tive emotionality factor is the higher order composite of stress reaction, alienation, and aggression scales, which had 18 items each (i.e., 54 total). The reliability score of negative emotionality factor was .89.

Analytical Approach We used contemporary behavior genetics’ research meth- odology to test our hypotheses. The first step was to esti- mate the proportion of variance in the construct because of

genetic and environmental components. A multigroup con- firmatory SEM approach was used, which represents the standard behavioral genetics’ method of examining the degree of similarity or covariances of the individual twins on particular measures of interest (see Plomin, DeFries, McClearn, & McGuffin, 2001). This methodology assumes a greater similarity between twins in a MZ pair than the twins in a DZ pair. Mathematically, this method uses the differences in genetic relatedness between MZ and DZ twins to estimate genetic and environmental components of a specific observed variable. We used Mplus Version 4.21 to conduct these analyses with a maximum likelihood esti- mation to test several models.

The basic univariate model in this methodology uses a SEM to examine genetic influences by decomposing vari- ance into “genetic” (A), “common-shared environment” (C), and “nonshared environment” (E) components. Whereas factor A accounts for variance due to genetics, factors C and E together account for environmental components of the observed variable or phenotype. Of the two environmental factors, the C factor refers to influences shared by members of the same environment (e.g., parents’ income, parental affection, same high school friends, etc.; those environmen- tal features shared by each twin). The E factor accounts for an unshared environment. As shown in Equation 1, variance in the observed variable or phenotype (in this case hope or leadership) is expressed as the sum of variance attributable to each of the three factors, A, C, and E, each weighted by a path coefficient (a, c, and e) that determines their relative influence:

Varvariable = a 2 + c2 + e2. (1)

Heritability is defined as the proportion of total variance associated with genetic factors: H = a2/Varvariable.

Figure 1 presents the confirmatory structural equation model used to describe the relationships among the vari- ables for two individuals who are either identical or fraternal twins. This is the established SEM model used for behav- ioral genetics’ research (e.g., Heath, Neale, Hewitt, Eaves, & Fulker, 1989).

Based on the assumption that identical twins share their entire genetic component, the correlation coefficient is 1.0 between the genetic component (A) of Twin 1 and Twin 2 of the identical twin pair. On average, fraternal twins share one half of their genes so that the corresponding correlation is .5 for them. The correlation between common environ- ments between pair members of both twin types is set at 1.0, reflecting the assumption of equal common environmental influence, whereas the path between the nonshared environ- mental factors for the twins is, by definition, specified as 0. Following the practice of behavioral genetic research using this model, we also tested differences in model specification

474 Journal of Leadership & Organizational Studies 18(4)

where a full model (with A, C, and E factors present) is tested against alternative nested models—(only A, E factors), (only C, E factors), (only E)—to determine the significance of the corresponding path coefficients. If, for example, the path coefficient c is insignificant, the AE model will show little chi-square change and would probably have better fit indices than the full ACE model.

To test the mediation model, we ran multivariate genetic models to test the potential mediation effects of dispositional hope. In the multivariate genetic model, the hope and lead- ership variables were examined for their respective A, C, E factors. Figure 2 presents a simplified SEM model (with only one twin) of the multivariate models we analyzed in this study.

We concluded that there is a mediation effect if the genetic component of hope overlaps the genetic component of the transformational leadership variable. In Figure 2, a11 represents the genetic component of the hope variable and a22 represents the genetic component of the transforma- tional leadership. In particular, the path a12 represents the genetic influences that the dispositional hope and leader- ship variables share, and their significance decides whether the effects of hope mediate those of genetics on transforma- tional leadership.

Results Hypothesis 1 states that genetics significantly influences dispositional hope. Results of a univariate model indicate that the genetic component of hope is significant. Table 3 presents different models that were fitted to compare the best-fitting model. We compared models using chi-square

(χ2) test, Akaike’s Information Criterion (AIC), root mean square error of approximation (RMSEA), comparative fit index (CFI), and the Tucker–Lewis index (TLI).

The full ACE model (Table 3, Model 1) shows a good fit with significant genetic component (A) and nonshared envi- ronmental factors, χ2(df) = 7.57(6); CFI = .95; TLI = 98; RMSEA = .05. In the AE model (Table 3, Model 2), although chi-square does not change significantly, we see improve- ments in RMSEA, CFI, and TLI, which implies that the AE model, χ2(df) = 7.57(7); CFI = .98; TLI = .99; RMSEA = .03, is a better representation of the data. Also, we see that the CE model has a significant change in chi-square (Δχ2 = 6.33, p < .05) with more poor fit indices, χ2(df) = 13.90(7); CFI = .79; TLI = 94; RMSEA = .10, indicating that CE is not a supe- rior fit of the genetic model compared with the ACE model. Hence, Model 2 is the best-fitting model. Based on Model 2, the percentage of the variance in dispositional hope that can be attributed to genetic factors (i.e., the heritability estimate, h2) is 53%. Therefore, Hypothesis 1 is supported.

In Hypothesis 2, we postulated that transformational leadership is influenced by genetic factors. As Table 4 shows, the full ACE model (Model 1: χ2(df) = 3.65(6); CFI = .99; TLI = 99; RMSEA = .00) and AE model (Model 2: χ2(df) = 3.65(7); CFI = 1.00; TLI = 1.00, RMSEA = .00) both fit the data well. In the ACE and AE models, although the chi-square changes significantly, we see improvements in RMSEA, CFI, and TLI, which indicates that the AE model is a better representation of the data. Also, we see that the CE model has a significant change in chi-square with

Hope Twin1

A1 C1 E1

Hope Twin2

A2 C2 E2

1.0 or 0.5 1.0

a c a ce e

Figure 1. Univariate genetic model to estimate genetic and environmental influences on variables (hope/transformational leadership) Note. The latent factors A, C, and E are standardized latent variables representing additive genetic, shared environmental, and nonshared environmental influences, respectively. The a, c, and e are the path coefficients to be estimated and they are constrained to be equal between the monozygotic (MZ) and dizygotic (DZ) groups.

Hope

Ahope

Transformational Leadership

Alead

a11 a12 a22

Chope Ehope Clead Elead

c11 e11

c12 c22 e22

e12

Figure 2. Multivariate model for testing the mediating effects of hope on genetic influences on transformational leadership Note. This is a simplified model for one twin and for ACE factors. A refers to additive genetic effects. The latent factors A, C, and E are standardized latent variables representing additive genetic, shared environmental, and nonshared environmental influences, respectively.

Chaturvedi et al. 475

lower fit indices (Model 3: χ2(df) = 9.52(7); CFI = .92; TLI = 97; RMSEA = .06), indicating that CE is not a supe- rior fit of the genetic model compared with the ACE model. Therefore, we used the structural equation model to conclude that the AE model (Table 4, Model 2) is a better fit for the twin data. Based on these models, the heritability of transfor- mational leadership in this data is 0.49. Hence, Hypothesis 2 is supported.

Finally, in Hypothesis 3, we hypothesized that disposi- tional hope mediates the effects of genetics on transforma- tional leadership. Results of our multivariate genetic models

(with Cholesky decomposition) predicting transformational leadership are presented in Table 5. The full ACE model (Model 1: χ2(df) = 25.73(17); CFI = .90; TLI = 93; RMSEA = .08) has good fit indices and shows that hope mediates the effect of genetics on leadership. Similarly, the AE model (Table 5, Model 2) has good fit indices, χ2(df) = 25.73(20); CFI = .94; TLI = 96; RMSEA = .06, with no difference in chi-square but better fit indices than the ACE model. The AE model (Model 3: χ2(df) = 34.75(20); CFI = .83; TLI = 90; RMSEA = .09) fits the data reasonably well but the fit indices are statistically lower than the ACE and AE

Table 3. Results of Univariate Model Fitting for Dispositional Hope

Factors Model Fit Indices

a c e χ2 (df) Δχ2 Δdf RMSEA (90% CI) AIC CFI TLI

Model 1: ACE model .73*** .00 .69*** 7.57 (6) n/a n/a .05 (.00, .15) 1060.25 .95 .98 Model 2: AE Modela .73*** — .69*** 7.57 (7) 0 1 .03 (.00, .13) 1058.25 .98 .99 Model 3: CE Model — .61*** .79*** 13.90 (7) 6.33* 1 .10 (.00, .18) 1064.57 .79 .94

Note. Sample size (identical/fraternal pairs) are 107/89 pairs. A, C, and E represent additive genetic factor, shared environmental factor, and nonshared environmental factor, respectively. AIC = Akaike’s information criterion; RMSEA = root mean square error of approximation; TLI = Tucker–Lewis index; CFI = comparative fit index; CI = confidence interval. a. Indicates the best-fitting model. *p < .05.**p < .01. ***p < .001.

Table 4. Results of Univariate Model Fitting for Transformational Leadership

Factors Model Fit Indices

a c e χ2 (df) Δχ2 Δdf RMSEA (90% CI) AIC CFI TLI

Model 1: ACE model .71*** .00 .72*** 3.65 (6) n/a n/a .00 (.00, .09) 1073.32 .99 .99 Model 2: AE modela .71*** — .72*** 3.65 (7) 0 1 .00 (.00, .08) 1071.32 1.00 1.00 Model 3: CE model — .61*** .80*** 9.52 (7) 5.87* 1 .06 (.00, .14) 1077.19 .92 .97

Note. Sample size (identical/fraternal pairs) are 107/89 pairs. A, C, and E represent additive genetic factor, shared environmental factor, and nonshared environmental factor, respectively. AIC = Akaike’s information criterion; RMSEA = root mean square error of approximation; TLI = Tucker–Lewis index; CFI = comparative fit index; CI = confidence interval. a. Indicates the best-fitting model. *p < .05.**p < .01. ***p < .001.

Table 5. Model Testing the Mediating Role of Hope on the Genetic Influences on Transformational Leadership

Model Fit Indices

χ2 (df) Δχ2 Δdf RMSEA (90% CI) AIC CFI TLI

Model 1: ACE model 25.73 (17) — — .08 (.00, .13) 1578.74 .90 .93 Model 2: AE modela 25.73 (20) 0.00 3 .06 (.00, .11) 1572.75 .94 .96 Model 3: CE model 34.75 (20) 9.02* 3 .09 (.03, .14) 1581.77 .83 .90

Note. Sample size (identical/fraternal pairs) are 99/81 pairs. A, C, and E represent additive genetic factor, shared environmental factor; and nonshared environmental factor, respectively. AIC = Akaike’s information criterion; RMSEA = root mean square error of approximation; TLI = Tucker–Lewis index; CFI = comparative fit index; CI = confidence interval. a. Indicates the best-fitting model after controlling for age, positive emotionality, and negative emotionality. *p < .05.

476 Journal of Leadership & Organizational Studies 18(4)

models. Based on the fit indices, we chose the AE model to be the best-fitting for the data. Therefore, we used the coef- ficients of the AE model to calculate genetic mediation coefficient. We present the path coefficients of the best-fitting AE model in Figure 3, which shows that the path overlap- ping the genetic components of hope and transformational leadership, a12 (b =. 13, p < .05) is significant, along with paths a11 (b = .52, p < .05) and a22 (b =. 14, p < .05). The significant path, a12, in Figure 3 confirms that hope medi- ates the effects of genetics on transformational leadership. The overlapping genetic variance based on the ratio of squared path coefficient a12

2 by the sum of the squared path coefficients of all the genetic factors (a12

2 + a22 2) influencing

transformational leadership is 46.3% (i.e., .132/[.132 + .142]). The total variance, a12

2/(a12 2 + a22

2 + e12 2 + e22

2) explained in the mediating relationship is 20.8% (= .132/[.132 + .142 + .072 + .202]). Hence, we find a significant overlap between the genetic components of hope and transformational lead- ership, which supports Hypothesis 3.1

Discussion The overall purpose of this study was to investigate the extent to which hope and transformational leadership are determined by genetic factors and to examine whether genetic influences on transformational leadership are medi- ated by dispositional hope. Results suggest that hope and transformational leadership are influenced strongly by

genetic factors. In addition, we found that dispositional hope mediates the relationship between genetics and transforma- tional leadership.

Consistent with earlier research on genetic influences on personality constructs, we found that the hope variable has a heritability estimate of .53, which suggests that the remain- der of the variance, 47%, is accounted for by the nonshared environment. This result is similar to the heritability of other personality variables, such as the Big Five (Loehlin, 1992): 41% for emotional stability, 49% for extraversion, 45% for openness to experience, 35% for agreeableness, 38% for conscientiousness, 52% for self-esteem (Roy et al., 1995), and 36% for optimism (Mosing et al., 2009).

Similarly, a univariate genetic analysis of transforma- tional leadership also confirms expectations from the litera- ture that a proportion of variance in leadership is genetic (e.g., Arvey et al., 2006; Arvey et al., 2007; Johnson et al., 1998). In this study, we found the heritability estimate of transformational leadership to be 49%, which is similar to the score reported by Johnson et al. (1998), who, based on a different twins’ sample but the same leadership measure (MLQ), reported a heritability of transformational leader- ship of 59%. Overall, the research points to a fairly moder- ate to high genetic influence in general on this particular leadership variable and is consistent with trait theories of leadership. We also found that hope and leadership variables are related, strengthening the explanation of “trait” leader- ship theories.

As predicted by Arvey and Bouchard (1994), who spec- ulated that the heritability of leadership constructs should be explained by personality factors, we found that hope is a significant mediator in a relationship of genetics with trans- formational leadership. We also found that the strength of overlapping genetic influence of hope on transformational leadership is 46.3%. The similarity in overlapping heritabil- ity estimates of hope and the leadership construct suggests the importance of hope as an important mediating mecha- nism in the genetics-leadership linkage.

Finally, our results on the positive relationship between hope and transformational leadership responds to the call for research on positive organizational behavior by focus- ing attention on positive rather than negative constructs (Luthans, 2002a, 2002b; Luthans & Youssef, 2007). Although prior research on hope suggested a positive relationship of hope with performance and positive work attitudes (Luthans, Avolio, Walumbwa, & Li, 2005; Snyder, 2000; Youssef & Luthans, 2007), limited attention has been given to the role of hope in leadership emergence (Peterson & Luthans, 2003). In organizations, leaders are the source of hope and have the challenging task of keeping hope alive during turbulent times, such as during recession. Our study findings highlight the importance of hope in understanding leadership issues.

Hope

Ahope

Transformational Leadership

Alead

.20***

.13** .14**

Ehope Elead

.48***

.52***

.07*

Figure 3. The best-fitting genetic model (AE model) depicting mediating effects of hope on transformational leadership Note. This is a simplified model for one twin and for AE factors only. A refers to additive genetic effects. Results reported after controlling for age, positive emotionality, and negative emotionality. *p < .05. **p < .01. ***p < .001.

Chaturvedi et al. 477

Limitations and Future Research

Notwithstanding its contributions, this study has some limitations. First, our analysis used only a female sample from the Minnesota Twin Registry, as corresponding data with a male sample was unavailable. Perhaps, future research with a male sample could replicate our results and establish generalizability of the model. We strongly believe that hope will be a significant mediator in the genetics- leadership relationship in the male sample.

In this study, common method variance is also possible. At the same time, it is important to mention that the twin data in this registry were collected at different times (total span was 6 years) and sometimes with a different mode of survey (paper/pencil, telephonic, etc.), which minimizes the possibility of a common method variance.

In addition, the self-report nature of our data, especially with measures of hope and transformational leadership, could be problematic in that the twin subjects were consistently lenient while responding to the survey items (i.e., data might be negatively skewed). However, we tried to minimize this possibility by transforming the dependent variable (i.e., transformational leadership). In addition, we found similar results with the leadership role occupancy measure as well, which minimizes the possibility of an incorrect conclusion.

Finally, we acknowledge the possibility of upward bias in the heritability estimate because of this gene–environment correlation. We believe that hope, much like optimism, can be learned and transferred from one person to another by expo- sure to other hopeful individuals (Seligman, 1998). For exam- ple, in the case of a doctor treating a cancer patient, research has shown that hopeful thinking can be transferred and has shown to positively affect patient’s health (e.g., Scheier & Carver, 2001; Stanton, Danoff-Burg, Huggins, 2002; Taylor, 2000). Similarly, we might see the transfer of hopefulness in a familial relationship. Family ambience could affect children. In a family-owned business, we might observe “like father, like son” in terms of attitude toward a hopeful future. The goal- directed behavior of parents could affect goal-directedness of their children. During their upbringing, children could learn the willpower of meeting desired goals and the “way power” to attain them from their parents. Given the possibility of gene- environment correlation, we believe there is a unique genetic influence on hope and transformational leadership.

Conclusion Transformational leadership has been one of the most effective leadership styles in affecting employee attitudes, behaviors, and performance (Judge, Ilies, & Colbert, 2004). Our current study sheds important light on the nature ver- sus nurture debate on leadership. We found that genetic factors account for 49% of the variance in transformational

leadership and that dispositional hope mediates this genetic influence (explaining 46.3% of the genetic influences on transformational leadership, which equates to 20.8% of total variance). These findings highlight the pivotal role of dis- positional hope in transformational leadership and points out the practical importance of training for developing trans- formational leaders. After all, 51% of the variance in trans- formational leadership is not related to genes, and therefore subject to other developmental experiences.

Acknowledgement

We thank the Minnesota Twin Registry program for helping with our data collection.

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, author- ship, and/or publication of this article.

Note

1. We also tested the mediation model with the leadership role occupancy measure (as in Arvey et al., 2006; Arvey et al., 2007) with similar results.

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Bios

Sankalp Chaturvedi, PhD, is an assistant professor in the Organization and Management Group at the Imperial College London, UK. His research focuses on organizational citizenship behavior, leadership and biological basis of organizational behav- ior and multilevel modelling.

Richard D. Arvey is a Professor and Head of the Department of Management and Organization at the National University of Singapore. He conducts research in the areas of organizational behavior including leadership, motivation, and job satisfaction.

Zhen Zhang, PhD, is an assistant professor in the Department of Management at Arizona State University, USA. His research focuses on leadership process and leadership development, the interfaces between organizational behavior and entrepreneurship, the biologi- cal basis of organizational behavior, and research methods.

Paraskevi T. Christoforou, is a PhD candidate in the Department of Management and Organization at the National University of Singapore. Her research focuses on discrete emotions, emotion regu- lation, emotional labor, emotional deviance, workplace aggression, and emergence of group and organizational level phenomena.