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Review of Managerial Science

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06-03-2019 | Original Paper

Beyond the “good” and “evil” of stability values in organizational culture for managerial innovation: the crucial role of management controls

Authors: Marc Janka, Xaver Heinicke, Thomas W. Guenther

Published in: Review of Managerial Science | Issue 6/2020

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Abstract

Previous literature has widely regarded stability values in organizational culture as a necessary evil at best and proposed their reduction to shape innovation. Our study expands the literature by empirically exploring the relationship among stability values in organizational culture, management control (MC), and managerial innovation. Managerial innovation is important for every organization because it contributes to technical innovation in the long term and is directly or indirectly associated with firm performance in a positive way, but its success or failure depends on a process of diffusion through the organization as a social system. Based on a survey of 260 large German firms, we find that managerial innovation has a negative association with the degree of stability in organizational culture but a positive association with an emphasis on three of the four tested MCs, namely, action controls, personnel controls, and cultural controls. We reveal that a large extent of the negative effect ascribed to stability in organizational culture is caused by a lack of emphasis on cultural controls rather than by the degree of stability itself. Furthermore, we go beyond the assessment of stability as purely “good” or “evil” by generating empirical evidence of the positive fit between stability and cultural controls for managerial innovation, following contingency theory. According to our findings, organizations with a relatively high degree of stability in their culture (e.g., bureaucracy or hierarchy) may be enabled to shape managerial innovation through cultural controls.

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Organizational culture and organizational structure are context factors that seem to have an indirect influence on individual or group behavior whereas management controls represent mechanisms that directly influence behavior toward the achievement of organizational goals (Flamholtz et al. 1985).

The CVF was developed as a meta-theory to model competing tensions between flexibility and stability in a social system (Denison and Spreitzer 1991). Stereotypes must be understood as theoretical ideals.

Aaltola (2018, p. 209) distinguishes between business model innovation, which is described as organizational changes in business activities, and managerial innovation, which focuses on the work of management. He states that both “are partially parallel concepts in a sense that both are introduced to performance”.

We discarded one questionnaire because the responding firm did not answer any of the relevant questions that we pose in this study. If this case is disregarded, the overall rate of missing values in our sample amounts to 0.2% and the maximum rate of missing values per item is 0.8%. The missing value rates can be assessed as very low and thus do not raise any concerns about the comprehensibility of our questions. The net response rate is the ratio of 260 useable responses to 2186 dispatched questionnaires.

For a detailed calculation example, see the “Appendix”.

All unreported results are available from the authors upon request.

Bootstrapping is applied as an alternative to traditional resampling approaches because it retains the statistical power of hypothesis tests in comparison to holdout validation and reflects an asymmetric (i.e., non-normal) distribution of data that may be an issue with curvilinear relations (Bollen and Stine 1990; Edwards and Parry 1993). To test each model-specific coefficient, we created 5000 randomly drawn replications from the original observations by applying a bias-corrected and accelerated bootstrap (Efron 1987). For each bootstrap iteration, individual sign changes of all coefficients were corrected if necessary, to ensure that the signs in the bootstrap sample and the original sample are identical.

Effect sizes express the increase of the R² coefficient between a model in which the respective relationship is included with a path and a model in which it is excluded: ƒ² = (R_included² − R_excluded² )/(1 − R_included² ).

Interactions are marked in the following with a multiplication sign (e.g., stability values × cultural controls).

In a review over 30 years, Aguinis et al. (2005) report that the average ƒ² value for interaction effects is 0.005 and the median 0.002. Helm and Mark (2012) stress in these findings that Cohen’s values of low, medium, and high ƒ² (0.02, 0.15, and 0.35) do not hold for interaction effects in most empirical research and that it is very challenging to prove interaction effects.

Siemsen et al. (2010, p. 472) examined the extent to which CMV affects polynomial regression equation results and state that “quadratic and interaction effects cannot be artificially created through CMV.” They further claim that CMV deflates curvilinear effects, making them more difficult to detect. As each of our polynomial regression models exhibits at least one significant interaction or quadratic effect for different MC variables, we assume that the likelihood of bias in this study due to CMV is quite low.

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Title: Beyond the “good” and “evil” of stability values in organizational culture for managerial innovation: the crucial role of management controls
Authors: Marc Janka
Xaver Heinicke
Thomas W. Guenther
Publication date: 06-03-2019
Publisher: Springer Berlin Heidelberg
Published in: Review of Managerial Science / Issue 6/2020
Print ISSN: 1863-6683
Electronic ISSN: 1863-6691
DOI: https://doi.org/10.1007/s11846-019-00338-3

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