Transformational Leadership at the CEO-TMT-Interface : The Mediating Influence of Behavioral Integration

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Transformational Leadership at the CEO-TMT-Interface –

The Mediating Influence of Behavioral Integration

Bachelorarbeit

vorgelegt von

Philip Alexander Hörlezeder

an der

Sektion Politik – Recht – Wirtschaft

Fachbereich Politik- und Verwaltungswissenschaft

1. Gutachter: Prof. Dr. Florian Kunze 2. Gutachter: Prof. Dr. Sabine Boerner

Konstanz, 2015

Konstanzer Online-Publikations-System (KOPS) URL: http://nbn-resolving.de/urn:nbn:de:bsz:352-0-296506

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3.1 Toward an Integrated Model of Individual Behavior 12 3.2 CEO Transformational Leadership and TMT Transformational Leadership 14 3.3 CEO Transformational Leadership and TMT Behavioral Integration 17 3.4 TMT Behavioral Integration and TMT Transformational Leadership 18 3.5 Transformational Leadership and Organizational Innovation 22

4. Method 24

4.1 Sample 24

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4.2.1 Transformational leadership. 28

4.2.2 TMT behavioral integration. 30

4.2.3 Organizational innovation. 31

4.2.4 Control variables. 32

4.3 Analytical Procedures 32

5. Results 33

5.1 Descriptive Statistics 33

5.2 Measurement Model 36

5.3 Structural Model 37

6. Discussion 43

6.1 Theoretical Implications 45

6.2 Practical Implications 46

6.3 Limitations and Future Research 47

7. Conclusion 50

References 51

Appendix 77

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List of Figures

1 Integrated model of individual behavior 12

2 Hypothesized model 24

3 Results of structural equation modeling for the hypothesized model 38

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1 Intercorrelations of study variables 34 2 Results of confirmatory factor analysis for the measurement model 37

3 Comparison of different structural models 38

4 Direct, indirect, and total effects of study relations in the hypothesized model 40 A1 Effects, outcomes, and moderators of transformational leadership 77 A2 Antecedents and predictors of transformational leadership and leader emergence 84 A3 Effects and outcomes of top management team composition, processes, behavior,

decision making, and behavioral integration 89

A4 English and German wording of study items 91

A5 Results of confirmatory factor analyses for study measures and items’ summary

statistics 99

A6 Study variables’ aggregation and summary statistics 104 A7 Results of structural equation modeling for the hypothesized model 105 A8 Direct, indirect, and total effects of study relations in the structural model without

controls 106

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Abstract

This paper addresses transformational leadership and its dispersion in top management teams (TMT). Drawing on different theories of individual action in social context and combining them into an integrated model of individual behavior, it is argued that transformational leadership exhibited by the chief executive officer (CEO) spurs TMT members’

transformational leadership. It is supposed that a significant part of this positive effect is mediated by TMT behavioral integration, and that both CEO and TMT transformational leadership relate positively to organizational innovation. The proposed model was tested relying on quantitative data from a large-scale study with 31,594 participants from 215 German small to medium-sized enterprises. Structural equation modeling provided support for all hypothesized relationships, to such an extent that CEO transformational leadership was positively related to TMT transformational leadership both directly and indirectly (via TMT behavioral integration). Likewise, empirical evidence for a positive impact of TMT transformational leadership on organizational innovation and for a complete mediation of the respective influence of CEO transformational leadership (via TMT transformational leadership) was found. With its theoretical argumentation and empirical findings, this study makes valuable contributions to research on antecedents and effects of transformational leadership, leadership dispersion, individual behavior in social context, and upper echelons theory. Additionally, results hold important practical implications for effective leadership in organizations.

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Transformational leadership (TFL) is the most popular and most extensively studied scientific approach to leadership in the past decades (Avolio, Walumbwa, & Weber, 2009; Judge, Woolf, Hurst, & Livingston, 2006). Emphasizing the extraordinary effect that leaders can have on their followers, it “enjoys the reputation of explaining […] the most effective form of leadership” (Van Knippenberg & Sitkin, 2013, p. 2) and is therefore of particular interest not only to researchers, but also to organizations and managers striving for success.

The primary aim of the present study is to examine how TFL disperses in TMTs, and especially the extent to which TFL behaviors are passed on from the CEO to individual TMT members. With recourse to upper echelons theory and different approaches of individual action in social context, it is argued that CEO TFL fosters TMT behavioral integration, the degree to which top managers behave as a team, which in turn fosters TMT TFL. It is supposed that besides this indirect, mediated effect, CEO TFL furthermore exerts a direct influence on TMT TFL. The proposed rationale is that individual attitudes, perceived socio- normative expectations, and perceived behavioral efficacy as determinants of behavioral intentions are greatly influenced by the attitudes and behavior of referents, to whom a transformational CEO and – in the case of a highly integrated TMT – manager’s peers belong.

Concerning the effects of TFL, it is argued that through intellectual stimulation, individualized consideration, idealized influence, and inspirational motivation transformational leaders can spur followers’ creativity and organizational innovation. In this relation, TMT TFL is supposed to partially mediate the effect of CEO TFL on organizational

1 For improved legibility, only the masculine form is used throughout the paper. However, the feminine form is always included.

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innovation, since TMT members represent channels through which the influence of the CEO cascades downward in an organization.

1.1 Scientific Relevance

While the effects of TFL received substantial scientific attention, it is considerably less clear what determines this specific leadership style (Avolio et al., 2009; Day, Fleenor, Atwater, Sturm, & McKee, 2014). Particularly, various researchers pointed to a lack of knowledge regarding the questions how TFL may be encouraged by contextual influences (Nielsen &

Cleal, 2011), how it spreads among peers and cascades in organizations (Walter & Bruch, 2009), and to what extent transformational CEOs animate TMT members to perform TFL themselves (Bommer, Rubin, & Baldwin, 2004; Ling, Simsek, Lubatkin, & Veiga, 2008b).

This work aims at enhancing scientific knowledge in this regard.

Although various approaches tried to retrace TFL dispersion in work groups, no study to date has – at least to the knowledge of the author – addressed this issue with respect to TMTs, so that mechanisms of dispersion remain largely unknown. The present paper tries to address this academic void by showing how social influences, emanated from both the CEO and TMT members, combine to spur the inclination of individual managers to exhibit TFL.

In integrating TMT behavioral integration into the analysis, insights into TMT dynamics and how they are influenced by CEO leadership are provided, thereby answering calls from upper echelons literature (Carmeli, Tishler, & Edmondson, 2012; Hambrick, 1994, 2009).

Especially, determinants of TMT behavioral integration have only sparsely received attention in scientific research to date (Carmeli, Schaubroeck, & Tishler, 2011; Simsek, Veiga, Lubatkin, & Dino, 2005), a shortcoming which this study seeks to remedy.

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Finally, advancement of both TFL and upper echelons literature is sought by separately modeling the effects of CEO and TMT leadership, rather than disregarding the CEO-TMT- interface and treating the former as a normal member of the latter – as has been done in the majority of previous research. As recommended by various researchers, this paper examines how TMT TFL is related to organizational innovation – over and above the influence of CEO TFL (Colbert, Barrick, & Bradley, 2014; Ling et al., 2008b).

1.2 Practical Relevance

Prior research established strong positive links between TFL and various outcomes of superior organizational interest (see Table A1 in the appendix for a detailed summary of TFL effects). Accordingly, appointing, promoting, and developing leaders who exhibit this specific leadership style is in the very interest of every organization. By providing insights into how TFL proliferates in TMTs and how this process is influenced by social context, this study intends to delineate some guidelines for firms on how to adjust organizational structure, culture, and the working environment in order to facilitate TFL dispersion. In assessing the relative importance of TMTs for organizational innovation, it is intended to be shown through which channels the influence of CEO TFL actually is effectuated. Based on the findings, implications for leader selection, promotion, and training are derived.

1.3 Outline

In order to delineate the focal theoretical concepts examined in this paper, a short overview of the relevant literature on TFL, leadership dispersion, upper echelons theory, and individual action in social context is given first. Subsequently, these theoretical streams are combined to derive the hypotheses proposed above. After describing the method of the present analysis,

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structural equation modeling (SEM) is conducted to test the hypothesized relations. The paper then concludes with a discussion of the results, their implications and an outlook for future research.

2. Theoretical Foundations and State of Research

2.1 Transformational Leadership

2.1.1 The theoretical concept of transformational leadership. Burns (1978) was the first scholar to outline the specific role of charismatic leaders in transforming the values of their followers. While “traditional” leadership is conceived to consist of purely transactional relationships – for example the exchange of wages for work effort – TFL “engages the full person of the follower” (Burns, 1978, p. 4) and moves followers to “performance beyond expectations” (Bass, 1985). Conceptually, TFL consists of four key components, the four Is (Bass & Avolio, 1990; Bass & Riggio, 2006).

First, idealized influence describes how a transformational leader acts as a role model to elicit followers’ admiration, identification, trust, and loyalty. Developing high levels of pride in terms of their belonging to the in-group of the leader, followers begin to emulate leader’s behavior.

Second, transformational leaders exert inspirational motivation by delineating an attractive vision of the future and making followers believe to be an indispensable part of it. By giving meaning to the work and lives of followers, the alignment of interests around the common vision, commitment, and cohesion are fostered.

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stimulating followers and fueling their creativity and innovative capacity. By communicating high expectations and at the same time encouraging new approaches and calling into question existing assumptions, transformational leaders promote followers’ personal development and problem-solving capabilities.

Finally, transformational leaders display individualized consideration by acting as mentors for their personnel and being attentive to their needs for growth and achievement. Leader- follower-interactions are personalized, so that the follower feels personally valued, and the leader tries to identify and realize the follower’s potential by providing individually tailored learning opportunities (Bass & Avolio, 1990; Bass & Riggio, 2006).

2.1.2 Scientific research on transformational leadership.² Initially, research on TFL focused on its impact, seeking to verify Bass’s (1985) postulate of “performance beyond expectations”. Over time, strong evidence for positive effects of TFL on different variables of superior organizational interest has been accumulated – both in a variety of settings and with regard to leaders, subordinates, teams, as well as the organization as a whole. Perhaps most importantly, TFL was found to be the “most important predictor of […] leadership

2 Despite minor theoretical differences, there is considerable conceptual overlap between TFL and charismatic leadership, and leaders scoring high on one type’s measure usually score high on the other one’s, too (Bass & Riggio, 2006; Conger, 1999; House, 1977; Van Knippenberg & Sitkin, 2013;

Walter & Bruch, 2009; Yukl, 1999). Summaries of TFL outcomes and antecedents therefore also include findings on charismatic leadership.

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effectiveness” (Piccolo et al., 2012, p. 567).³ A comprehensive summary of TFL effects and outcomes is given in Table A1 in the appendix.

Albeit academic interest in TFL antecedents somewhat lagged behind, numerous studies have meanwhile deepened scientific knowledge with regard to demographic variables, personality traits, contextual variables, attitudes and behavior, or life experiences influencing leader emergence and TFL. Promising from a practical perspective, the effectiveness of leader training was repeatedly confirmed. As Bass (1990) points out: “through training, managers can learn the techniques and obtain the qualities they need to become transformational leaders” (p. 19). Table A2 in the appendix provides a summary of TFL predictors and antecedents.

Importantly for this work, several antecedent-oriented approaches tried to retrace leadership dispersion in groups and organizations. They are discussed in the following section.

2.2 Leadership Dispersion⁴

Avolio and Bass (1995) introduced leadership diffusion, a mechanism which can operate in two directions. In a downward flow of influence, senior managers can create an organizational culture with strong normative expectations toward a specific leadership behavior by

3 It is noteworthy that TFL seems to be universally valid, since its phenomena were observed and its effectiveness attested across a variety of organizations and cultures (e.g., Bass, 1997; Den Hartog, House, Hanges, Ruiz-Quintanilla, & Dorfman, 1999; Jung, Yammarino, & Lee, 2009; Walumbwa &

Lawler, 2003).

4 Bass (1990) noted that “managers tend to model their own leadership style after that of their immediate supervisors” (p. 26). However, he failed to provide a thorough theoretical explanation for his observation, which is why this approach is not discussed in this paper. The same applies to social contagion, a mechanism of proliferation proposed by Meindl (1990), which is more suitable to explain the spread of TFL effects than the dispersion of actual TFL behavior.

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Conversely, in an upward direction, individual leadership behavior can emerge as a group norm and subsequently be conveyed to the entire organization through role modeling and the rotation of group members, thereby gradually becoming part of the organizational culture (Avolio & Bass, 1995).

This concept is essentially an advancement of Bass, Waldman, Avolio, and Bebb’s (1987) model of leadership cascade, which proposed that – due to role modeling processes – the more top managers’ leadership behaviors are transformational, the more TFL is exhibited concomitantly at lower levels of management (see also Waldman & Yammarino, 1999).

Finally, Bommer et al. (2004) examined the impact of peer leadership behavior on the exercise of TFL. With recourse to Fishbein and Ajzen’s (1975) theory of planned behavior – an advancement of which is used as explanatory framework in this study – they argued that the performance of TFL depends on the extent to which it is displayed by a leader’s peer group, since this group influences leader’s individual attitudes, social expectations, and perceived behavioral control regarding TFL. Although Bommer et al. (2004) made use of different explanatory models of individual action, their approach failed to integrate them into a comprehensive scheme – a shortcoming which this work tries to remedy.

Taken as a whole, albeit examining leadership dispersion in different social environments, none of these three approaches paid attention to TFL dispersion in TMTs and the CEO’s role in this process, so that both this highly specific organizational unit and the actual mechanisms of proliferation lack thorough investigation. The purpose of this paper is to address these academic voids. In doing so, the following section sheds light on TMTs’ importance and

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integrative dynamics within them by introducing upper echelons theory, before different approaches to individual action in social context – which serve as an explanatory framework in this work – are discussed.

2.3 Upper Echelons Theory

2.3.1 The role of top management teams in organizational leadership. While research on leadership and organizational success initially focused on the dominant role of the CEO, a more comprehensive approach was adopted by upper echelons theory suggesting that an organization is a “reflection of its top managers” (Hambrick & Mason, 1984). The central assumption – top executives play a decisive role in affecting the fate of companies – also resonates in the distinction between leadership in and of organizations – the latter implying leadership at the highest management level (Waldman & Yammarino, 1999).

With recourse to March and Simon’s (1958) theory of bounded rationality, Hambrick (1994) argued that in light of the complexities and informational demands of organizational steerage, the management of an organization is essentially a “shared activity, extending well beyond the chief executive” (p. 172; see also Daily & Schwenk, 1996). Accordingly, attention shifted to the TMT, the most powerful and influential group in an organization (Carmeli et al., 2011).

Organizational outcomes were supposed to be predicted by top managers’ cognitive filters, individual “givens” that determine how environmental stimuli are interpreted and – in the aggregate – how organizational decisions are made (Hambrick & Mason, 1984; Hambrick, 1994, 2007, 2009).

2.3.2 Top management team behavioral integration. Hambrick (1994) noted that the level of “teamness” in TMTs can be very limited, to such an extent that TMT members

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behavior. On the contrary, in TMTs with a high level of behavioral integration, managers frequently and substantially interact. Depicting the “degree to which the group engages in mutual and collective interaction”, behavioral integration consists of (1) the quantity and quality of information exchange, (2) collaborative behavior, and (3) joint decision making – thus the sharing of information, resources, and decisions (Hambrick, 1994, p. 188, 2007, 2009).

Various studies have substantiated the importance of TMTs (and especially TMT behavioral integration) for organizational outcomes – over and above the influence of the CEO. A summary of the relevant literature is provided in Table A3 in the appendix.

Given this well documented relevance of top managers, it seems mandatory to extend research on TFL dispersion to TMTs. Drawing on an integrated model for explaining individual behavior, this paper argues that TMT behavioral integration plays a key role in facilitating leadership proliferation. Accordingly, existing approaches to individual action in social context are outlined in the next section.

2.4 Individual Action in Social Context

A basic but essential precondition for action lies in an individual’s ability to perform the respective behavior. According to Bandura’s (1977) social learning theory, “most human behavior is learned observationally through modeling” (p. 22). Observing others allows an individual to evaluate a behavior against its observed outcome and thus to decide on his actions without having to directly experience their respective consequences beforehand.

Hence, behavioral cues of others become the most effective guideline in orientating one’s

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actions, which holds especially true for individuals to whom the observer is attracted or to whom he ascribes high levels of status or competence (Bandura, 1977).

In a similar vein, Cialdini (1985) suggested that the principle of social proof heavily influences individuals by providing cues for appropriate behavior. According to him, the perceived rightness and appropriateness of a given behavior rises with the number of people performing that behavior – and particularly if the perceived similarity between observer and observed is high (Cialdini, 1985).

Similarly, Salancik and Pfeffer’s (1978) social information processing approach underlined the importance of the social context in affecting individual attitudes and behavior. By influencing how events are interpreted and emanating pressures for conformity, a person’s immediate social environment (i.e., the attitudes and opinions of others) constrains his actions as a “function of the unanimity of shared beliefs” regarding socially acceptable behavior (Salancik & Pfeffer, 1978, p. 240). Hence, individual behavior is adapted to the social context in which it occurs and to its predominant norms and expectations.

The most comprehensive approach to individual action in social context, Fishbein and Ajzen’s (1975) theory of planned behavior, conceived actual behavior as a result of the interplay of four elements: (1) an individual’s attitude toward a given behavior, (2) subjective norms as an individual’s perception of others’ approval or disapproval of the behavior, (3) behavioral control or the perceived ability to perform the behavior, and (4) the intention to perform or not perform the behavior as a product of attitude, subjective norms, and behavioral control. As in the other three approaches, the role of referents, important others with whom the individual is motivated to comply, is emphasized. Depending on an individual’s liking for

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motivation to comply causes that subjective norms ultimately guide individual behavior by transmitting social influence and pressure (Ajzen, 1988, 1991).

In conclusion, existing approaches highlight different aspects of individual action in social context. To provide an explanatory framework for TFL diffusion in TMTs, they are combined into an integrated model in the following section.

3. Theory and Hypotheses Development

3.1 Toward an Integrated Model of Individual Behavior

Synthesizing the different schemes of individual action in social context illustrated above, an integrated model of individual behavior (Figure 1) is proposed.

Figure 1. Integrated model of individual behavior

In line with social information processing, referents’ attitudes are supposed to influence individual attitudes through their effect on the interpretation of events and their meaning. For

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example, a manager’s attitude toward an organizational decision on personnel cutbacks is likely to differ considerably depending on whether the individual is surrounded by people favoring the measure or strongly opposing it – over and above conformity pressures. It is by interacting with the social environment that individual attitudes and subjective reality are constructed (Salancik & Pfeffer, 1978).

With recourse to the approaches of social information processing, social proof and planned behavior, it is argued that both referents’ attitudes and behavior shape individual perceptions of appropriate behavior. Emanating strong conformity pressures, the intensity of perceived socio-normative expectations determines the rigidity of constrains the individual faces when deciding on his behavior.

Referents’ behavior influences an individual’s perceived behavioral efficacy, his belief regarding the ability of performing the behavior. By thoroughly observing others, an individual learns how to emulate a behavior and builds the self-confidence necessary for actually displaying it – a reasoning derived from social learning theory.

Finally, as proposed by the theory of planned behavior, individual attitudes, perceived socio- normative expectations and perceived behavioral efficacy determine behavioral intention as ultimate antecedent of actual behavior.

Importantly, the relations between referents’ attitudes and behavior on the one side and individual attitudes, perceived socio-normative expectations and perceived behavioral efficacy on the other are influenced by (1) the number of referents, (2) referents’ subjective importance to the individual, and (3) the perceived unanimity of referents’ attitudes and

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the consistency of their attitudes and behavior, the stronger are the effects on individual attitudes, perceived socio-normative expectations and perceived behavioral efficacy, ceteris paribus (see also Bandura, 1977; Cialdini, 1985; Salancik & Pfeffer, 1978).

3.2 CEO Transformational Leadership and TMT Transformational Leadership⁵

Drawing on the integrated model of individual behavior developed above, CEO TFL should provide a fertile soil for tendencies of social influence to unfold. In the following, it is accordingly illustrated how the four Is of CEO TFL fuel TMT TFL.

A transformational CEO gradually alters TMT members’ attitude toward TFL by exerting idealized influence, thereby structuring the social environment within which TMT members act and shaping their fundamental evaluation of TFL. Exposed to the role model of the leader – whom they profoundly admire – managers come to appreciate TFL as an appropriate behavior.⁶ Since they strongly identify with the CEO (Wang & Howell, 2012), his attitudes and behavior moreover become particularly salient in stimulating strong socio-normative pressures. Consistently observing the CEO exerting TFL, TMT members perceive strong expectations from his part to perform TFL themselves, which they met with a sustained eagerness to comply and put in extra effort (Wang, Oh, Courtright, & Colbert, 2011).

Accordingly, it is argued that:

5 Regarding the effects of TFL discussed in this and subsequent sections, further empirical substantiation in terms of a variety of supporting studies can be found in Table A1 in the appendix.

6 The conception of leadership as the “management of meaning” seems particularly suitable here.

According to Smircich and Morgan (1982), leadership is “realized in the process whereby one or more individuals succeed in attempting to frame and define the [social] reality of others" (p. 258; see also Ou et al., 2014).

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The idealized influence of a transformational CEO fosters TMT members’ TFL through individual attitudes and perceived socio-normative expectations.

Transformational leaders devise attractive visions and make followers feel that their contribution is decisive in achieving them, thereby boosting commitment to the “common cause” and perceived work meaningfulness (Bono & Judge, 2003).⁷ In this process, individual attitudes concerning aims and the appropriate means to achieving them are altered, and value congruence with the leader emerges (Krishnan, 2005). Because TMT members are keen to put the aspired vision into effect, they evaluate behaviors against their usefulness in doing so. As they experience the display of TFL on the part of the CEO as well as its positive outcomes (team cohesion, interpersonal helping behaviors, etc.), their fundamental beliefs about leadership are profoundly modified, making them believe that TFL is by far the most effective leadership style in realizing the delineated vision. Thus:

The inspirational motivation of a transformational CEO fosters TMT members’ TFL through individual attitudes.

A transformational leader encourages unconventional thinking by creating an atmosphere of

“trial and error” and calling into question existing assumptions, which both challenges and stimulates followers’ intellectual capacities (Wang et al., 2011). In the emerging climate of renewal and cognitive ambiguity, TMT members are particularly open to learning and thus susceptible to behavioral cues from the individual toward whom they most thoroughly orient their behavior – the CEO. Ignorant of what to do and striving to acquire the necessary means to handle their complex environment, executives look at the CEO for guidance and become

7 Perceived work importance and meaningfulness in itself was shown to be a significant predictor of TFL (Nielsen & Cleal, 2011).

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successively applying their newly developed competencies, managers come to believe that they are in charge of the course of action, which raises their self-confidence and self-efficacy beliefs regarding the performance of TFL (see also Wu, Tsui, & Kinicki, 2010).⁸ Consequently, it is proposed that:

The intellectual stimulation of a transformational CEO fosters TMT members’ TFL through perceived behavioral efficacy.

Finally, transformational leaders treat their followers as individuals with needs for achievement and growth and make them feel personally valued. Being exposed to learning opportunities that are tailored to their personal needs by the CEO, TMT members successfully handle the assigned tasks and individually develop in the process of doing so (Dvir, Eden, Avolio, & Shamir, 2002). Thereby, they successively build up self-confidence and self- efficacy, to such an extent that their general appraisal of their ability to cope with challenges or perform whatever behavior is enhanced (Nielsen & Munir, 2009). This holds especially true for TFL, since they constantly observe the CEO performing it. Furthermore, due to the personalization of the leader-follower-relationship, TMT members are eager to please the CEO and meet his perceived expectations, which they try by emulating his (leadership) behavior. Hence:

The individualized consideration of a transformational CEO fosters TMT members’ TFL through perceived socio-normative expectations and perceived behavioral efficacy.

8 In line with this argumentation, it was found that leaders’ perceived situational control and psychological empowerment are positively related to TFL (Nielsen & Cleal, 2011; Spreitzer, De Janasz, & Quinn, 1999).

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Altogether, this argumentation leads to the following hypothesis:

H1: CEO transformational leadership has a positive effect on TMT transformational leadership.

3.3 CEO Transformational Leadership and TMT Behavioral Integration⁹

As outlined above, TMT behavioral integration consists of a substantial information exchange, collaborative behavior, and joint decision making. It is shown in the following how various effects of TFL are likely to promote these aspects.

A transformational CEO’s intellectual stimulation and individualized consideration boost TMT’s collective efficacy, the group’s shared belief in its ability to jointly take on difficult problems (Wang & Howell, 2012). Creating a team atmosphere of trust and support, the CEO encourages TMT members to express and discuss their opinions openly, thereby strongly encouraging communication and the exchange of information among TMT members (Carmeli et al., 2012; Nijstad, Berger-Selman, & De Dreu, 2014).

In addition to that, the CEO leads TMT members to transcend selfish interests and adopt the objectives of the team by rallying them around a common vision. Over time, executives develop high levels of trust in the TMT and gradually base their very identity on the membership in it, which greatly fosters team cohesion (Braun, Peus, Weisweiler, & Frey,

9 This relationship has already been examined in two studies: Gu, Weng, and Xie (2012) found support for a positive effect of CEO TFL on TMT behavioral integration, but their work lacked a thorough theoretical underpinning. In contrast, Ling et al. (2008b) grounded their argumentation in TFL effects on followers. They, too, found evidence for a positive relationship.

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communicated by the CEO, a sense of common destiny evolves which strongly stimulates TMT members’ interaction, interpersonal helping, and organizational citizenship behaviors (OCB) (Piccolo & Colquitt, 2006) – and thus collaboration.

Finally, the CEO’s focus on executives’ development and empowerment results in a decision making style that is substantially decentralized and consensus-oriented (Flood et al., 2000), so that decision making powers rest with the TMT as a whole rather than with its individual members.¹¹ This and the common purpose around which TMT members converge fuel joint decision making.

Given these effects of TFL, a TMT with a transformational CEO should function as a “team”

in the proper sense of the word, in that it exhibits higher levels of information exchange, collaborative behavior, and joint decision making. Hence, it is hypothesized that:

H2: CEO transformational leadership has a positive effect on TMT behavioral integration.

3.4 TMT Behavioral Integration and TMT Transformational Leadership

If a TMT is behaviorally integrated, its members frequently and substantially interact, know what their peers are thinking, doing, and expecting, closely work together, observe each other’s behavior, and jointly decide on the firm’s course of action. Drawing on the integrated model of individual behavior and the reasoning on TFL in TMTs outlined above, it is argued

10 Collective team identification was shown to result in TMT behavioral integration, as was a CEO’s collectivistic orientation (Carmeli & Shteigman, 2010; Simsek et al., 2005).

11 Several studies showed that consensus-oriented decision making promotes TMT behavioral integration (e.g., Carmeli et al., 2011; Ling et al., 2008b; Ou et al., 2014).

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that the three components of behavioral integration significantly affect TMT members’

intention to perform TFL. In doing so, the adoption of TFL by TMT members is supposed to be a gradual process, with some executives more readily emulating CEO TFL than others, depending on their respective personal disposition (e.g., extraversion, emotional intelligence, etc.).

By frequently exchanging information and discussing new ideas, TMT members open up to cognitive mechanisms of reconsideration and reappraisal. In this state, their individual attitudes are highly susceptible to change. Since some of the TMT members have already been deeply convinced of the effectiveness of TFL, these “vanguards” share their beliefs both explicitly and implicitly, and individual managers who are exposed to their influence are led to take up a positive stance on TFL. This rationale not only applies to “procrastinators”, managers who have not yet been convinced of the virtues of TFL, but also to those who have already been persuaded to a greater extent: in a self-affirmative exchange, executives mutually fortify their inclination to perform TFL. In addition to this impact on individual attitudes, regular and dense communications between TMT members make reciprocal demands and expectations more explicit to the individual. Convinced that TFL is the most effective means in realizing the common vision, TMT members exert conformity pressures on procrastinators, creating strong socio-normative expectations toward TFL. Accordingly, it is argued that:

Information exchange in a behaviorally integrated TMT fosters TMT members’ TFL through individual attitudes and perceived socio-normative expectations.

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to the exemplary influence of their peers. Individual executives have plenty of opportunities to observe their colleagues’ behavior, and hence directly witness the positive outcomes of transformational vanguards’ leadership behavior for both the TMT and the organization as a whole. As more and more of their peers engage in this specific leadership style, executives face various occasions to observationally learn TFL behaviors. By watching a steadily growing proportion of their peers practicing TFL, managers’ confidence in their own ability of performing it is greatly enhanced. Additionally, they can be assured that their peers assist them in case of any difficulties, as TMT members help each other and engage in OCB.

Accordingly, it is proposed that:

Collaborative behavior in a behaviorally integrated TMT fosters TMT members’ TFL through perceived behavioral efficacy.

In deciding on organizational policies and procedures, TMT members profoundly shape the culture and norms of an organization in accordance with their convictions.¹² Being persuaded of the virtues of TFL, transformational executives gradually create an organizational climate which rewards this specific leadership behavior and discredits others. Since the behaviorally integrated TMT shares decision making powers, all members take part in decision making and the largest possible consensus is sought, to the extent that every executive has the opportunity to voice his opinion and approve or disapprove a decision. Because of this involvement in decision making, individual managers cannot easily deviate from the adopted policy – in contrast to situations in which they have no stake in the decision making. Since they take part in the final vote, their peers can expect them to comply with the policy agreed upon. Hence,

12 Schein’s (2010) notion of leaders as the “main architects of [organizational] culture” highlights this point (p. xi).

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socio-normative expectations for conformity are high, and this holds particularly true for the question of which leadership behavior is exhibited from the part of TMT members. Hence:

Joint decision making in a behaviorally integrated TMT fosters TMT members’ TFL trough perceived socio-normative expectations.

Altogether, TMT behavioral integration induced by a transformational CEO spurs TMT members’ TFL by affecting individual attitudes, creating strong perceived socio-normative expectations and boosting perceived behavioral efficacy. In doing so, the “team can influence each member just as the individual leader can influence his or her followers”, a phenomena labeled “team leadership” (Sivasubramaniam, Murry, Avolio, & Jung 2002, p. 67). The collective influence of the TMT on its members thus causes strong stimuli toward TFL dispersion. Accordingly, it is hypothesized that:

H3: TMT behavioral integration has a positive effect on TMT transformational leadership.

With recourse to the rationale of the integrated model of individual behavior, the mechanisms described above should emanate an impetus toward TMT TFL over and above the direct effect of CEO TFL, as (1) the number of referents approving TFL is higher, and (2) the perceived unanimity of their attitudes and behavior grows over time. Hence, a significant part of the total effect of CEO TFL on TMT TFL should be effectuated indirectly through TMT behavioral integration. Correspondingly, it is proposed that:

H4: TMT behavioral integration partially mediates the positive effect of CEO transformational leadership on TMT transformational leadership.

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3.5 Transformational Leadership and Organizational Innovation¹³

Besides examining TFL dispersion in TMTs, another major aim of the present study is to clarify the impact of TFL on organizational innovation in special consideration of the CEO- TMT-interface.

Transformational leaders fuel creativity by intellectually stimulating followers, by encouraging them to question existing assumptions and to explore new ways of thinking.

Through individualized consideration, they make followers believe that their contributions are valued, encouraging them to proactively come up with new ideas. Finally, via idealized influence and inspirational motivation, transformational leaders increase subordinates’

intrinsic motivation, commitment, and effort, which culminates in a heightened output of new approaches and solutions. Taken all these mechanisms together, TFL greatly promotes organizational innovation. Thus:

H5: CEO and TMT transformational leadership have a positive effect on organizational innovation.

The impact of TFL on organizational innovation should be particularly pronounced if it is not only performed by the CEO, but the entire TMT. In view of organizational complexities and ambiguities, top managers play a decisive role at the apex of organizations by sharing the responsibility of leading with the CEO. Due to mere numerical constraints, the direct impact of CEO TFL has a tightly limited space to unfold: only a small fraction of the total workforce

13 Prior research established the causal link between TFL and organizational innovation in more detail (e.g., Allen, Smith, & Da Silva, 2013; Jung, Wu, & Chow, 2008; Vaccaro, Jansen, Van Den Bosch, &

Volberda, 2012).

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immediately reports to him or is otherwise subjected to his influence, and this fraction is largely made up of TMT members.¹⁴ Accordingly, top executives represent the individuals most strongly affected by the CEO (Ling et al., 2008b), and the above-mentioned, innovation- enhancing influences of CEO TFL should thus first and foremost impact them. Immerged to a stimulating, valuing, and creative environment, managers come to pass their experiences on to their respective subordinates, who do the same with regard to their subalterns, and so forth, thereby exponentially multiplying the number of units and employees affected and amplifying the impact of TFL on organizational innovation. Hence, a significant portion of the positive influence of CEO TFL on organizational innovation is transferred through its effects on TMT members. Accordingly:

H6: TMT transformational leadership partially mediates the positive effect of CEO transformational leadership on organizational innovation.

Figure 2 illustrates the hypothesized model.

14 Although Waldman and Yammarino (1999) illustrated the possibility of distant leadership via attributions, visions, and storytelling, its effects on followers should be considerably smaller than in the case of direct leadership.

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Figure 2. Hypothesized model

The following section outlines the method applied for testing the proposed hypotheses.

4. Method

4.1 Sample

Data collection for the present investigation took place in two waves as part of a larger research project between February and July 2012 and June and December 2013. The self- recruitment study was conducted by a professional agency in Germany specialized in benchmarking small to medium-sized enterprises (SME). In order to be eligible, companies had to be located in Germany and employ no more than 5,000 employees.

In sum, 215 SMEs applied for voluntary participation and took part in the study. As reward for their participation, they were promised a tailored benchmarking report. Companies represented five different industries, namely service (50.3 %), production (25.1 %), finance (11.2 %), wholesale (9.5 %), and retail (3.9 %). Their number of employees ranged from 15 to

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3,897, whereat 75 % of all companies reported numbers between 24 and 484. The median was 185 and the mean 381 employees.

In order to prevent common method and single source bias (Podsakoff, MacKenzie, Lee, &

Podsakoff, 2003), data was collected from three different sources.

First, Human Resources (HR) executives were asked to provide general information on the organization, such as industry affiliation, employment statistics, and financial performance.

Second, employees were invited to participate in the study by the HR department with a standardized e-mail describing the purpose of the study, assuring participants’ full anonymity and containing a link to a web-based survey hosted by an independent IT company. In order to limit the number of questions each employee had to answer, an algorithm programmed in the survey website randomly assigned participants to one of four survey versions, thereby adopting a split-sample design (Rousseau, 1985; see also Kunze, Boehm, & Bruch, 2011, 2013, for similar approaches). Amongst others, employees were asked to provide ratings of their supervisors’ TFL behaviors.

Third, TMT members, too, were invited to participate in the study via an e-mail from the HR department. In a separate questionnaire, they supplied ratings of TMT behavioral integration, organizational innovation, and other rather broad organizational variables, where they were supposed to provide the most accurate information. Furthermore, TMT members (as well as employees) were asked to give some demographic information.

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professional translators, and a double-blind back-translation procedure was applied to guarantee semantic equivalence (Schaffer & Riordan, 2003).

In sum, 31,594 individuals took part in the survey, thereof 510 TMT members. The average number of respondents per firm was 147, with figures ranging from 1 to 965. In comparison with the entire personnel, an average within-organization response rate of 65.2 % was achieved (range = 2.1-100 %).

Due to the algorithm-based allocation of employees to one of the four survey versions, the items assessing TFL were answered by 24.5 % of all employees, or 7,622 individuals. These were predominantly male (58.9 %), on average 39 years old, had a company tenure of 10 and a position tenure of 6 years. With regard to the entire workforce in the sample, a potential non-response bias could be ruled out (59.0 %; 39; 10; 6).

The items for behavioral integration and organizational innovation were answered by all of the 510 TMT members (on average 2 per firm with a range from 1 to 14). 85.4 % of them were male, and they reported an average age of 46, a company tenure of 13, and a position tenure of 8 years.

4.2 Measures

Unless stated otherwise, items for all measures were gauged using a five-point response scheme, ranging from 1 (strongly disagree) to 5 (strongly agree). The precise English and German wording of all items is listed in Table A4 in the appendix.

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To ensure that each set of items loaded on the construct to which it was intended to do, confirmatory factor analyses (CFA) or, to be more precise, principal-component factor analyses were conducted. Following Bagozzi and Yi (1988), the cutoff value for sufficient loading was set at > 0.50. Cronbach’s α was calculated to evaluate the scales’ reliability and internal consistency, respectively. The applied cutoff value for adequate reliability was > 0.70 (Acock, 2014).

For assessing overall model fit properties, different indices were assessed. First, the χ² test statistic was calculated. Divided by the degrees of freedom (df), χ²/df < 3.0 indicates acceptable model fit (Homburg & Giering, 1996). Second and third, the Comparative Fit Index (CFI) and Tucker Lewis Index (TLI) were consulted as they were shown to avoid the underestimation of model fit in cases of relatively small samples (n < 250) to which for instance the Normed Fit Index (NFI) is prone (Bentler, 1990; Hu & Bentler, 1998; Sharma, Mukherjee, Kumar, & Dillon, 2005). The cutoff values for a reasonable fit were set at > 0.90 in agreement with common practice (Backhaus, Erichson, Plinke, & Weiber, 2003; Homburg

& Baumgartner, 1995). Finally, following the recommendations of Sharma et al. (2005) for SEM, the Root Mean Square Error of Approximation (RMSEA) was calculated. However, the RMSEA should be treated with caution as it tends to over-reject true-population models at small sample size (n < 250) (Hu & Bentler, 1998). The cutoff value for acceptable model fit was set at < 0.10 (Browne & Cudeck, 1993; Kunze et al., 2011).

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class correlations ICC(1) and ICC(2) as well as the index rwg were calculated.¹⁵ Aggregation is justifiable if the F test statistic for ICC(1) is significant, ICC(2) exceeds 0.60, and the mean r_wg across all units is > 0.70¹⁶ (Glick, 1985; Klein & Kozlowski, 2000).

4.2.1 Transformational leadership. TFL was assessed using 22 items from a scale developed by Podsakoff, MacKenzie, Moorman, and Fetter (1990).¹⁷ Answered by individual employees and assigned to either the CEO or TMT members through a variable retaining to whom employees directly reported, these items provided ratings on six dimensions of TFL behavior: Intellectual Stimulation, Articulating Vision, High Performance Expectations, Fostering Group Goals, Providing Role Model, and Individualized Support.

Because of theoretical appropriateness and practical considerations with regard to sample size requirements and the number of parameters to be estimated in the final model, parceling procedures were applied. In a hierarchical model with several first-order factors representing a broader second-order factor, homogenous parcels consisting of items that load on the same first-order factor can be constructed. In the present analysis, items were clustered in the above-mentioned six TFL dimensions (first-order factors) to serve as indicators of TFL (second-order factor). In light of the model fit’s tendency to decrease with an expanded

15 A detailed explanation of these aggregation statistics is provided in the appendix (p. 95). Since Stata, the statistics software used in this work, does not possess commands for calculating ICC(1), ICC(2), and rwg, the necessary computation formulas were programmed by the author himself. The corresponding commands are also stated in the appendix (p. 95).

16 The expected variability for calculating rwg was operationalized as rectangular distribution, assuming a purely random responding with each response having the same likelihood of being chosen.

17 Three of the items were measured in a seven-point response format. They were subjected to proportional transformation – multiplying each item with a factor of 5/7 – to adjust them to a five- point response format (see also Colman, Norris, & Preston, 1997).

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number of items – even if the model closely approximates the focal phenomenon – this reduction of items seems highly justifiable, all the more since the use of parcels was shown to result in less biased parameter estimates, a normal distribution of indicators, and higher reliability compared to the application of item-level models (Coffman & MacCallum, 2005;

Hall, Snell, & Foust, 1999; Little, Cunningham, Shahar, & Widaman, 2002).

Testing the six parcels, High Performance Expectation had a Cronbach’s α value below the specified cutoff value (0.63) and a significantly smaller loading on TFL than the other five factors (0.44). Additionally, the Cronbach’s α test statistic showed that its exclusion would improve the internal consistency of the TFL scale. Consequently, the parcel was dropped. The other dimensions all had sufficiently high α values and factor loadings, and the exclusion of any one of them would have deteriorated the properties of the scale.

Fit properties of the five-parcel-model were initially not satisfactory (χ² = 25.90; df = 5; CFI = 0.99; TLI = 0.97; RMSEA = 0.14). Based on theoretical considerations and modification indices, the parcels Articulating Vision and Individualized Support were allowed to covary.

While Articulating Vision focuses on how followers are inspired by the leader’s vision, Individualized Support comprehends leader’s considerateness to followers’ feelings. As followers’ feelings are inevitably altered by the inspirational impact of the vision, articulating a vision in itself induces an increased perception of leader’s considerateness on the part of followers, thus justifying the addition of a covariance path. The refined model showed sufficient fit properties (χ² = 10.81; df = 4; CFI = 1.00; TLI = 0.99; RMSEA = 0.09), and the internal consistency of the scale was α = 0.97.

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mixed, to such an extent that the respective ICC(1) (0.19, p < 0.001; 0.10, p < 0.001) and rwg

values (0.78; 0.73) were satisfactory, while ICC(2) values did not meet the threshold (0.50;

0.45).

4.2.2 TMT behavioral integration. TMT behavioral integration was measured using a scale developed by Simsek et al. (2005). Thereby, TMT members answered nine items that gauged the quality of information exchange, collaborative behavior, and joint decision making in the TMT.

To limit the number of parameters in the final estimation, the two items that had the weakest loading on behavioral integration (GF_35 and GF_39) were excluded and only seven were retained for further analysis.¹⁸ All remaining items had sufficiently high α values and loadings on the latent variable.

Initial model fit properties were moderate (χ² = 52.17; df = 14; CFI = 0.94; TLI = 0.91;

RMSEA = 0.13). As both GF_36 (quality of discussed solutions) and GF_37 (level of creativity emanating from TMT dialog) assessed positive outcomes of TMT member communication, they were closely related to each other, and adding a covariance path between them noticeably improved model fit (χ² = 27.92; df = 13; CFI = 0.98; TLI = 0.96;

RMSEA = 0.08). The behavioral integration scale had a reliability of α = 0.90.

Aggregation statistics were divergent. While ICC(1) was significant (0.27, p < 0.001) and rwg

clearly above the demanded value (0.83), ICC(2) did not meet the cutoff criteria (0.54).

18 Different rules of thumb suggest a minimum of three to four indicators per construct (Hall et al., 1999; Kenny, 1979; Kline, 2011).

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4.2.3 Organizational innovation. Organizational innovation was assessed in a seven- point response format with nine items from Scott and Bruce’s (1994) measure of individual innovation that were adapted to the organizational level. Answered by TMT members, these items gauged the frequency with which certain innovation-relevant behaviors such as the generation of new ideas and techniques, their intra-organizational promotion and implementation, and the evaluation of their usefulness, were performed in an organization.

Of the nine items, three were excluded: GF_78 had the weakest loading on innovation and was equivalent to GF_79, in that both items measured the generation of new techniques and solutions. GF_82 was redundant to GF_80 and GF_81, as all three items gauged active support-seeking for innovative ideas. Finally, GF_85 had the second lowest loading and assessed the frequency with which innovations are evaluated rather than the act of innovation itself. Factor loadings and α values of the remaining six items were satisfyingly high.

Initial model fit indices were moderate (χ² = 66.17; df = 9; CFI = 0.93; TLI = 0.89; RMSEA = 0.20), indicating a necessity for further refinement. GF_77 and GF_79 both measured the generation of innovative ideas and solutions, while GF_83 and GF_84 assessed their systematic implementation. The respective items were thus closely related, and adding two corresponding covariances substantially improved model fit (χ² = 18.58; df = 7; CFI = 0.99;

TLI = 0.97; RMSEA = 0.10). Cronbach’s α for the organizational innovation scale was 0.94.

In respect of aggregation statistics, ICC(1) was significant (0.26, p < 0.001) and rwg quite high (0.82), while ICC(2) was below the requested value (0.52).

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aggregation statistics of the focal study variables are summarized in Table A6. Both tables are enclosed in the appendix.

4.2.4 Control variables. To account for the potential influence of confounding variables, several controls that were previously found to relate to TFL and/or behavioral integration were added to the model. This applies for age, gender, extraversion, openness, and neuroticism (see Table A2 in the appendix), as well as for the duration of the leader-follower- relationship (Hambrick, 1994; Krishnan, 2005) and group size (Simsek et al., 2005).

Accordingly, TMT members’ mean age, the proportion of women, members’ average levels of extraversion, openness, and neuroticism, executives’ average company and position tenure and TMT size were controlled for. Moreover, the contextual variables environmental dynamism, organizational change, firm size (i.e., the number of employees) and industry affiliation (coded with a dummy variable for each of the five sectors) were added as controls (see Table A2 in the appendix; Carmeli et al., 2011; Dickson, Resick, & Hanges, 2006;

Hambrick, 1994).

4.3 Analytical Procedures

In order to test the hypothesized relations, SEM was conducted using the statistics software Stata. In comparison with multiple regression, SEM has at least two major advantages. First, it allows the simultaneous estimation of all paths in the model. Second, in integrating observed variables as indicators of latent constructs, it accounts for measurement error in the latent constructs and thus removes this potential bias from estimates of the structural relationships (Hall et al., 1999; Ling et al., 2008b).

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Data analysis was performed in two steps following the approach of Anderson and Gerbing (1988). In the first step, the measurement model was fitted to the observed data by conducting a simultaneous CFA of all study variables. Second, SEM was applied to evaluate the structural relations between the exogenous and endogenous constructs in the hypothesized model. In doing so, a sequence of nested models was compared in order to test the mediation hypotheses and gain insight into which model best explains the observed covariances.

The significance of indirect effects was tested using bootstrapping procedures. Bootstrap confidence intervals do not depend on an assumed normal distribution of residuals and are therefore more accurate in testing the significance of mediation effects than other procedures.

By drawing various random samples with replacement from the dataset, bootstrapping uses the distribution of parameter estimates across all replications to estimate standard errors and hence confidence intervals of a given statistic. It is thus particularly useful if the distribution of a statistic is unknown (Acock, 2013, 2014; Cheung & Lau, 2008). In the present analysis, the number of replications was set at 350.¹⁹

5. Results

5.1 Descriptive Statistics

Summary statistics of all study variables are listed in Table A6 in the appendix, whereas Table 1 illustrates the respective intercorrelations.

19 This number is mainly due to computation constraints of the statistical program used for this analysis. Although some researchers suggested a minimum number of 500 replications (e.g., Cheung

& Lau, 2008), others reported or recommended the use of replication numbers that were considerably smaller (50-200) (e.g., Bradley & Tibshirani, 1998; Stapleton, 2008).

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Table 1 Intercorrelations of study variables Variable 12345678910 1. CEO TFL1.00 2. TMT Behav. Integ. 0.36***1.00 3. TMT TFL0.32***0.43***1.00 4. Orga. Innovation0.31***0.65***0.35***1.00 5. Age-0.05-0.09-0.01-0.031.00 6. Prop. of Women0.180.18-0.100.22*-0.39***1.00 7. Extraversion0.060.030.090.07-0.11-0.111.00 8. Openness0.130.22*0.21*0.24**-0.040.020.30**1.00 9. Neuroticism-0.07-0.02-0.130.04-0.180.20*-0.25**-0.24*1.00 10. Tenure Company-0.020.070.090.100.45***-0.11-0.010.08-0.101.00 11. Tenure Position0.020.100.040.130.55***-0.02-0.11-0.02-0.020.69*** 12. TMT Size -0.19*-0.08-0.020.000.01-0.31***0.080.09-0.090.09 13. Env. Dynamism0.00-0.050.04-0.050.22*-0.21*0.13-0.05-0.080.06 14. Orga. Change 0.24**0.42***0.16*0.49***-0.030.23*-0.090.17-0.22*0.18 15. Firm Size -0.17*-0.27***-0.08-0.25**-0.05-0.09-0.03-0.04-0.08-0.07 16. Industry: Production-0.05-0.11-0.15-0.110.23*-0.35***0.04-0.19*0.020.16 17. Industry: Wholesale 0.010.060.040.110.04-0.180.010.00-0.070.15 18. Industry: Retail -0.05-0.08-0.05-0.06-0.030.12-0.010.000.120.01 19. Industry: Service0.060.19*0.150.09-0.27**0.30**-0.030.12-0.05-0.26** 20. Industry: Finance 0.02-0.100.02-0.010.100.020.050.14-0.050.04 Note. *p < 0.05. **p < 0.01. ***p < 0.001.

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Table 1 (continued) Intercorrelations of study variables Variable 11121314151617181920 11. Tenure Position1.00 12. TMT Size -0.061.00 13. Env. Dynamism0.05-0.041.00 14. Orga. Change 0.07-0.05-0.031.00 15. Firm Size -0.060.17*0.12-0.141.00 16. Industry: Production0.12-0.06-0.04-0.070.031.00 17. Industry: Wholesale -0.030.19*0.090.13-0.04-0.19*1.00 18. Industry: Retail 0.04-0.020.07-0.030.17*-0.12-0.071.00 19. Industry: Service-0.180.07-0.10-0.01-0.07-0.54***-0.33***-0.21**1.00 20. Industry: Finance 0.10-0.150.090.000.02-0.21**-0.12-0.07-0.33***1.00 Note. *p < 0.05. **p < 0.01. ***p < 0.001.

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integration and TMT TFL. Furthermore, TMT behavioral integration was positively related to TMT TFL and both CEO TFL and TMT TFL featured a positive correlation with organizational innovation.

Of the 15 control variables included in the analysis, five exhibited significant correlations with one or more of the focal endogenous variables and thus were retained for further analysis. These were: proportion of women, openness, organizational change, firm size, and the dummy variable for the service industry. The insignificant control variables were dropped in order to avoid biased parameter estimates due to unnecessary controls (see Becker, 2005).

5.2 Measurement Model

The final measurement model consisted of four latent constructs – CEO TFL, TMT behavioral integration, TMT TFL, and organizational innovation – with a total of 23 items (five, seven, five, and six, respectively). All relevant fit indices for the measurement model were sufficiently high (χ² = 382.89; df = 219; CFI = 0.95; TLI = 0.94; RMSEA = 0.06), as were item reliability and convergent validity. In a CFA, all indicators featured high and significant loadings on the respective latent construct (clearly above the cutoff value of >

0.50), indicating that the items were related to the construct to which they were theorized to be. The results of CFA for the measurement model are shown in Table 2.

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Table 2

Results of confirmatory factor analysis for the measurement model

Indicator Standardized factor loading

CEO Transformational Leadership

Intellectual Stimulation 0.83***

Articulating Vision 0.89***

Fostering Group Goals 0.89***

Providing Role Model 0.93***

Individualized Support 0.70***

TMT Behavioral Integration

GF_36 0.66***

GF_37 0.79***

GF_38 0.71***

GF_40 0.86***

GF_41 0.77***

GF_42 0.77***

GF_43 0.79***

TMT Transformational Leadership

Intellectual Stimulation 0.79***

Articulating Vision 0.94***

Fostering Group Goals 0.90***

Providing Role Model 0.92***

Individualized Support 0.73***

Organizational Innovation

GF_77 0.82***

GF_79 0.86***

GF_80 0.90***

GF_81 0.82***

GF_83 0.83***

GF_84 0.80***

Note. N = 212.

*p < 0.05. **p < 0.01. ***p < 0.001.

5.3 Structural Model

For each of the retained control variables, directional paths to the endogenous variables and nondirectional covariance paths to the exogenous variable were added before estimating the hypothesized structural model. SEM’s main results are illustrated in Figure 3 (for improved legibility, items and controls are not displayed). The detailed results – including effects of

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fitted the data sufficiently well (χ² = 561.56; df = 315; CFI = 0.93; TLI = 0.92; RMSEA = 0.06) (see also “hypothesized model” in Table 3).

Figure 3. Results of structural equation modeling for the hypothesized model

Table 3

Comparison of different structural models

Model χ² df χ²/df ∆χ² ∆df CFI TLI RMSEA

Hypothesized model

561.56 315 1.78 - - 0.93 0.92 0.06

Direct effect model 1

581.05 316 1.84 19.49 1 0.92 0.91 0.06

Direct effect model 2

573.41 316 1.81 11.85 1 0.93 0.91 0.06

Controls model

613.77 320 1.92 52.21 5 0.92 0.90 0.07

No controls model

431.62 220 1.96 -129.94 -95 0.94 0.93 0.07 Note. N = 212. χ2 = Chi squared test statistic. df = Degrees of freedom. ∆ refers to the baseline model (i.e., hypothesized model). CFI = Comparative Fit Index. TLI = Tucker Lewis Index. RMSEA = Root Mean Square Error of Approximation.

Transformational Leadership at the CEO-TMT-Interface : The Mediating Influence of Behavioral Integration