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).
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.
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
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.
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.
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).
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 rwg 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 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).
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.
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).
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).
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).