اثرات استفاده متقابل از سیستم های کنترل مدیریت بر نوآوری محصول

Simons' `levers of control' framework indicates that an interactive use of management control systems (MCS) contributes to fostering successful product innovation. However, his work is ambiguous in not specifying whether the relationship between interactive controls and innovation is a mediating or a moderating relationship. This paper examines the relationships among variables embedded in Simons' framework of levers of control, explicitly distinguishing the different types of effects involved and testing their significance. The results of the survey-based research do not support the postulate that an interactive use of MCS favours innovation. They suggest this may be the case only in low-innovating firms, while the effect is in the opposite direction in high-innovating firms. No evidence is found either in favour of an indirect effect of the interactive use of MCS on performance acting through innovation. In contrast, the proposition that the impact of innovation on performance is moderated by the style of use of MCS is supported, with results indicating that the explanatory power of a model that regresses performance on innovation is significantly enhanced by the inclusion of this moderating effect.

In recent years there has been an increased interest in examining the relationships between product innovation and the use of formal management control systems (MCS).1 Understanding how an organization can use its formal control systems to support product innovation has emerged as an important research question (Shields, 1997). A significant body of literature has explored the relationships between formal MCS and product innovation within subunits, taking R&D departments, product development teams and product development projects as the level of analysis (Abernethy & Brownell, 1997; Brown & Eisenhardt, 1995; Davila, 2000), but limited emphasis has been placed on the relationship between the use of formal MCS at top management levels and product innovation examined from an organizational perspective. Not only are there relatively few empirical studies in both the innovation and MCS literatures that address this relationship at the organizational level, but also the limited prior research appears to provide inconclusive and even contradictory findings. The innovation management literature tends to minimize or ignore the potential role of formal MCS as a factor that may influence successful product innovation (Dougherty & Hardy, 1996; Gerwin & Kolodny, 1992; Leonard-Barton, 1995; Tidd, Bessant, & Pavitt, 1997; Verona, 1999), thus suggesting that the use of formal MCS by top managers is not relevant for successful product innovation. More strikingly, a second line of research present in both the innovation and the management control literature affirms that a widespread use of formal MCS is in fact incompatible with innovation, including product innovation. Here formal MCS are seen as deterrents for creativity and for coping adequately with the uncertainty associated with product innovation (Abernethy & Stoelwinder, 1991; Amabile, 1998; Miles & Snow, 1978; Ouchi, 1977). A third stream of studies has found formal MCS to coexist with product innovation (Ezzamel, 1990; Khandwalla, 1973; Miller & Friesen, 1982). In the context of a “control package” (Otley, 1980 and Otley, 1999) that combines multiple management control elements, while informal MCS and other managerial systems and processes are expected to encourage innovation, formal MCS are expected to block innovation excesses and to help ensure that ideas are translated into effective product innovation and enhanced performance (Bart, 1991; Chenhall & Morris, 1995; Clark & Fujimoto, 1991; Dent, 1990; Kaplan & Norton, 1996; Wheelwright & Clark, 1992). Finally, a fourth group of studies affirms that inasmuch as formal MCS may provide a prioritary agenda and a stimulating forum for the generation and implementation of creative ideas including product development ideas, the most innovative firms are intensive users of formal MCS and an intensive use of MCS may lead to increased innovativeness (Simons, 1990, Simons, 1991 and Simons, 1995a). Overall, both the innovation and the MCS extant research provide inconsistent findings regarding the relationship between formal MCS and product innovation. Some authors have pointed to the different styles of use of formal MCS (Simons, 1990, Simons, 1991 and Simons, 1995a) or the different roles of MCS (Chapman, 1997 and Chapman, 1998) as explanations for these apparently inconsistent studies. This paper focuses on the interactive style of use of formal management control systems as defined by Simons, 1990, Simons, 1991 and Simons, 1995a. On emphasizing the relevance of attributes related to use rather than design and on pointing out the distinct implications of different styles of use of formal MCS, Simons' levers of control framework provides insights that help understand the mentioned apparent inconsistencies. More precisely, Simons' framework contributes to explaining the contradictory findings regarding the direction and significance of the effects of formal MCS on successful innovation as reported in prior literature. In Simons' terms, those studies that find that formal MCS (i.e. feedback and measurement systems) hinder innovation are partial to the extent that they focus exclusively on thermostat-like, diagnostic uses of formal MCS, and ignore the implications of interactive uses of formal MCS. On the contrary, those studies that have found that formal MCS act as facilitators of successful innovation are those that are more comprehensive to the extent that they capture the presence of interactive uses of MCS as well as the dynamic tension between diagnostic and interactive uses of formal MCS. However, while Simons, 1990, Simons, 1991, Simons, 1995a and Simons, 2000 suggests that an interactive control system contributes to successful innovation, this framework is ambiguous and does not clearly discriminate between whether an interactive control system makes companies more innovative or whether it makes innovative companies more successful in terms of improved performance. The purpose of this paper is to explicitly discriminate between the different effects of the interactive use of MCS on product innovation and performance, as well as to assess their significance. As defined in this study, product innovation encompasses the implementation stage (Damanpour, 1991; Wolfe, 1994) and refers to the development and launching of products which are in some respect unique or distinctive from existing products (Higgins, 1996; OECD, 1997; Sánchez & Chaminade, 1998). Consequently, this study aims to clarify the causal model and the explanatory links implied in Simons' framework as they apply to product innovation, explicitly testing whether an interactive control system makes companies more inclined to develop and launch new products or whether it contributes to successfully enhance the impact of the introduction of new products on performance. The following section develops the theoretical arguments that lead to the setting forth of several testable propositions which refer to distinct expected effects of the interactive use of MCS. The Research Methodology and Design section presents the research method, including data collection procedures, operationalization of measurement instruments and model specification. Results are then presented and discussed and interpreted in next two sections. A final section concludes, summarizing the findings, evaluating some of the limitations of the study and introducing some directions for future research.

The purpose of this study was to clarify through which specific relationships the linkbetween interactive use of MCS and successful innovation as posited by Simons framework of levers of control is enacted. In particular, it has been argued that Simons model does not provide a well-defined differentiation between distinct types of potential effects through which the interactive use of MCS may affect product inno- vation and performance. The study has set out to explicitly distinguish the different types of effects embedded in Simons model and has tested their significance. Two propositions have been devel- oped, predicting in turn that (1) the more inter- active the use of MCS by top managers, the higher the product innovation (and acting through innovation, the better the performance); and (2) the more interactive the use of MCS by top managers, the greater the effect of product innovation on performance. The results of the research have not borne out the first of these two propositions while they have provided support for the second one. Thus, results have provided evi- dence against the postulate that an interactive use of MCS favours product innovation. They have suggested this may be the case only in low-inno- vating firms, while the effect appears to be in the opposite direction in high-innovating firms. Moreover, no significant indirect effect on per- formance has been detected. In contrast, the proposition that the impact of product innovation on performance is moderated by the style of use of MCS has been supported, with results indi- cating that the explanatory power of a model that regresses performance on product innovation is significantly enhanced by the inclusion of this moderating effect. Based on the theoretical development and empirical results, it is considered plausible that an interactive use of control systems may favour innovation in low innovating firms through the provision of guidance for search, triggering and stimulus of initiatives, and provision of legitimacy to autonomous initiatives. In contrast, an inter- active use of control systems appears to reduce innovation in high-innovating firms, plausibly through the filtering out of initiatives that results from the sharing and exposure of ideas. The sig- nificant moderating effect of the interactive use of MCS on the impact of innovation on performancemay in turn result from the direction, integration and fine-tuning that interactive control systems provide. Overall, the results of this study emphasize, in support of some prior research, the considerable importance of formal MCS in the pursuit of innovation that is successfully translated into long- term performance. More specifically, the study emphasizes the relevance of the style-of-use of formal MCS, discriminating between the different types of functional effects of an interactive use of formal control systems and providing clear evi- dence of the significance of these effects on both innovation and performance. The identification of significant functional effects of the interactive control systems should encourage top managers to ponder over the convenience of paying special attention to the patterns of use of formal control systems in their firms. While the results of the study shed some light on the role of interactive MCS in fostering suc- cessful innovation, some limitations must be noted which should be addressed in further research. First, this study is confined to a limited scope of control systems. Even though this confinement was deliberate and adopted for purposes of trac- tability, it is acknowledged that this introduces a simplification that should be mitigated in further research. Furthermore, and based on different theories attachable to the different specific control systems under analysis, future studies could also address the potential different implications of dif- ferent specific control systems being selected for interactive use. Subsequent research should also aim to capture the tensions and balances among styles of use of formal MCS (e.g. diagnostic vs. interactive) as well as among types of control systems (e.g. formal vs. informal) in order to integrate the enhanced understanding of interac- tive MCS into the broader framework of overall control packages and in order to examine potential complementary and substitution effects. Second, the research domain of this study is restricted to product innovation. Extension of the inquiry to other types of innovation such as pro- cess innovation or management innovation may beaddressed in future research. Future research could also usefully focus on different degrees of radicalness or degrees of institutional novelty of innovations in order to increase the understanding of the relationships between MCS and innovation. A third type of limitation of our study is related to sample size. Small sample sizes reduce the gener- alizability of findings and the power of statistical tests. It is noticeable, however, that despite the reduction in power, an array of significant findings have emerged from statistical tests, suggesting that the presence of true relationships among the vari- ables of interest cannot be rejected. The sample of our study was selected from medium-sized, mature manufacturing firms. Generalizing the results to firms in other contexts should be done cautiously. The replication of this study with larger sample sizes in a variety of organizational settings (i.e. industries other than manufacturing, firms of dif- ferent sizes, different national cultures, ... ) could refine the findings of this study and extend them to different contexts. Several methodological improvements can also be suggested for future research. Further work is needed to test and refine the psychometric prop- erties of the proposed instruments. Despite the difficulties, multi-method strategies for gathering data should be encouraged in future research in order to avoid potential common variance biases and in order to enhance the validity and reliability of the construct measures. Future studies should use refined measurement instruments in larger samples so that the stability and generalizability of the results can be improved. Moreover, enlarged sample size would open up the possibility of using structural equation models for better estimation of the models, by simultaneously dealing with mea- surement error issues and multiple interrelated dependence relationships. Finally, some limitations of our study are inherent to the selected research methodology. As with all cross-sectional survey-based research de- signs, the nature of this study s research design does not allow for the assessment of strict causality or positive proof of the relationships among the variables of interest. The usual caution that asso-ciation is a necessary but not sufficient condition for causality does also apply here. What can be said is that the evidence obtained is consistent with positions proposed in the theoretical discussion. Longitudinal case studies provide considerable research opportunities for investigating and con- firming the proposed theoretical causal relation- ships as well as for enhancing the understanding of the dynamics and theoretical reasons underlying the relationships found in this study. Longitudinal case studies may be particularly fruitful in refining and testing the plausible interpretation of the contents and processual aspects of the detected effects of the interactive use of MCS on innovation and performance. Notwithstanding these limitations, the results of the study have provided evidence of the relevance of an interactive use of MCS in influencing prod- uct innovation and performance. The most sig- nificant contributions have been the discrimination of direct, indirect and moderating effects of the interactive use of control systems, and the identi- fication of plausible distinct consequences of the learning derived from the interactive use of control systems in low versus high innovating firms.