The role of managerial control in innovation

Int. J. Business Performance Management, Vol. 19, No. 3, 2018

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The role of managerial control in innovation processes: an empirical analysis among Italian firms Anna Maria Arcari and Anna Pistoni* Department of Economics, Insubria University, Italy Email: [email protected] Email: [email protected] *Corresponding author

Stefano Peluso Department of Statistic Science, Università Cattolica del Sacro Cuore, Italy Email: [email protected] Abstract: This paper investigates the link between the managerial control system (MCS) and product/service innovation. The aim is to provide an empirical analysis of MCS role in innovative firms, highlighting MCS characteristics that better support the innovation processes. The sample consists of 104 Italian manufacturing firms belonging to those sectors featuring the largest number of registered patents. The results show that an MCS can enhance innovation but it can also inhibit it depending on the role it plays. Indeed, an MCS may hamper innovation if it is limited to pursuing diagnostic functions. Conversely, innovation is positively associated to an interactive use of the MCS. The results of this study may have major implications for practitioners. Organisations hoping to enhance their innovation performance should develop an MCS able to stimulate free thinking and search for opportunities, while avoiding a strict compliance with rules and rigid performance evaluation. Keywords: innovation; management control systems; diagnostic control; interactive control; formal control mechanisms; informal control; Italy. Reference to this paper should be made as follows: Arcari, A.M., Pistoni, A. and Peluso, S. (2018) ‘The role of managerial control in innovation processes: an empirical analysis among Italian firms’, Int. J. Business Performance Management, Vol. 19, No. 3, pp.349–370. Biographical notes: Anna Maria Arcari is a Full Professor in Management and Control System and Cost Accounting at Insubria University. Her main research areas include the study of the performance measurement and control system in different organisational contexts: manufacturing and service companies, private and public, profit and non-profit, large and small firms, in particular, the impact that the use of the PMCS can have on management processes of companies and their performance. Anna Pistoni is an Associate Professor in Management Accounting and Control at Insubria University, Varese, Italy. She is also the Director of Master in General Management at Insubria University, teacher for different undergraduate, graduate and MBA courses in Bocconi University and co-Chair Copyright © 2018 Inderscience Enterprises Ltd.

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A.M. Arcari et al. of the track Accounting and Control for Sustainability at the EURAM. Her main current research interest are managerial control in innovative firms management accounting in servitisation strategy, corporate social responsibility and performance measurement, and integrated reporting. She is the author of many publications on managerial control, performance measurement, corporate social responsibility and sustainability. Stefano Peluso received his PhD in Economics with specialisation in Finance from Universitàdella Svizzera Italiana, Lugano, Switzerland. He is currently an Assistant Professor in Statistics at Università Cattolica del SacroCuore, Milan, Italy. His research interests are in Bayesian statistics, Bayesian non-parametrics, computational statistics, high-frequency finance, credit risk, graphical and relational models. This paper is a revised and expanded version of a paper entitled ‘How italian companies are monitoring innovation’ presented at Misure di performance e sistemi di informazione e controllo nei nuovi ambienti socio-economici, Catania, 22–23 October 2015.

1

Introduction

Nowadays, innovation has become a key factor for achieving a competitive advantage. Several variables have been studied as driversof innovation performance. For example, Naranjo Valencia et al. (2010) highlighted the role of the organisational culture in supporting product innovation. The organisational culture is one of the key elements in both enhancing and inhabiting innovation. While adhocracy cultures may foster the development of new products or services, hierarchical cultures inhibit product innovation. Similarly, Nonaka (1991) maintained that redundancy is important because it encourages frequent dialogue and communication. This helps to create a common cognitive ground among employees and thus facilitates the transfer of tacit knowledge. Camelo-Ordaz et al. (2006) analysed the impact of the top management team (TMT) vision and of the work team characteristics on innovation. They hypothesised that the TMT development of an innovative strategic vision positively affects a company’s innovation performance, as do some team characteristics, such as diversity of skills and social cohesion, autonomy, and informal communication. According to the authors, creativity blossoms when individuals perceive a relatively high level of autonomy as well as control over their work and ideas, and when they feel free to decide how their tasks should be carried out. Promoting informal communication by means of debates and discussions has been indicated as another element that positively correlates with innovation. On the one hand, informal communication supports knowledge sharing, and consequently across-fertilisation among different fields or areas is the best way to develop creative proposals. On the other hand, it leads to a deeper interaction among individuals, thereby contributing to building trust among them. Cabello-Medina et al. (2005) studied the relationship among organisational structures, managerial processes, and innovation activities. Starting from the relevance of new organisational forms on innovation processes, they specifically analysed the impact of the following variables: strategic flexibility factors, communication processes, and the use of IT, strategic alliances and collaboration agreements.

The role of managerial control in innovation processes

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Damanpour (1991) carried out research on the influence of the organisational size on innovation. On the one hand, large organisations can rely on several enabling factors favouring innovation, such as large resources, skilled workers, advanced technical knowledge, leeway to tolerate potential loss of unsuccessful innovations. On the other hand, small business can be more innovative due to their higher flexibility, better ability to adapt and improve, and less resistance to accepting and implementing change. Damanpour also highlighted that the size of a company is more strongly related to the innovation implementation phase than to the creative phase. More specifically, a bigger size facilitates the implementation of innovation more than its development. Size also seems to have a positive impact on complexity, and complexity positively affects innovation. Complex organisations are more innovative because they can rely on a greater variety of specialists, which provides broader knowledge and more differentiated units that are more likely to produce a cross-fertilisation of ideas. Our study delves into those managerial mechanisms that are most likely to support the development of innovation processes. Within this framework, our paper focuses on the link between the managerial control system (MCS) and product/service innovation. The underlying idea of our work is that, if properly designed, an MCS is a powerful mechanism supporting the implementation of the business strategy (Kaplan, 1983; Kaplan and Norton, 2001; Dixon et al., 1990; Simons, 1999). The MCS has a direct impact on the individuals’ behaviour and consequently may bring the overall performance to higher levels. Moreover, as some authors (Davila, 2000; Bisbe and Otley, 2004; Hitt et al., 1996; Pearson et al., 2000; Werner and Souder, 1997) have emphasised, the presence of forms of coordination, such as the MCS, is necessary in contexts with a high degree of creativity, which is typical of innovative firms. A lot has been written on the major role played by the MCS in favouring or inhibiting innovation. A first body of literature (Ouchi, 1977; Davila, 2000) focuses on the concept of informal control as a form of coordination that promotes creativity at individual, group and organisational levels. Another body of literature (Bremser and Barsky, 2004; Calantone et al., 1995; Chiesa et al., 2009; Davila et al., 2009; Donnelly, 2000; Dunk, 2011), recognising that innovation processes, similarly to any business activity, must pursue both financial and market success, advocates that innovation development has to be managed through formalised control. It is also postulated that such formal mechanisms should be used with an interactive role rather than a diagnostic one. Notwithstanding the importance of the topic, there is little empirical evidence supporting the different approaches. The earlier studies provide only little robust empirical evidence, while current research projects have been carried out based on the experience of a limited number of firms and mainly provide a descriptive analysis. Against this background, this research has two main goals. The first is to empirically test whether or not an MCS has an impact on innovation performance. The second is to highlight the characteristics of the MCS’s that are best suited to supporting innovation development. More specifically, we refer to the use of formal or informal mechanisms, or, in case of formal mechanisms, to the pursued role by distinguishing between a diagnostic or interactive role. To do this, an empirical study on a sample of 104 innovative Italian firms was carried out. The Italian context was selected mainly because only few studies on this topic have been carried out on Italian companies. This paper is divided into four sections. The first outlines the mainstream theories, while the second describes the research scope, design and methods. The third section

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presents our findings, and the final section discusses the theoretical and managerial implications, limitations and recommendations for future research.

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Theoretical background

In line with the aims of this study, we focused our analysis of the literature on three main areas: some preliminary issues about innovation processes, the characteristics of MCS’s specifically designed to support innovation, and the features of informal control mechanisms. This analysis resulted into the definition of the assumptions underlying our research, which will be described in detail in the following sections.

2.1 Innovation in managerial literature Organisational innovations can be classified according to different criteria. For example, Damanpour (1991) suggests to consider on the one hand, the impact of innovation on the company’s activity and, on the other hand, the dual-core model. According to the former criteria, innovations can be classified as radical when they produce significant changes in the company’s activities and imply a relevant deviation from consolidated practices, or as incremental when they generate shorter-term changes (Damanpour, 1991; Ettlie et al., 1984). The dual-core model, instead, leads to distinguish between technical and administrative innovations. Technical innovations refer to new technologies, products and services, while administrative innovations refer to new organisational forms, procedures and rules (Damanpour and Evan, 1984; Hollen et al., 2013). In our research, we specifically focus on product innovation, i.e., the development of new or improved products and/or services and their successful launch on the market. This is consistent with the concept of technological product innovation outlined in the OECD (1992) which refers to goods and services that are completely new or feature significant improvements as compared to those previously offered by the firm. These innovations may derive from radically new technologies, new applications of existing technologies, or from the use of new knowledge. As to the measurement of a company’s innovation level, different approaches and metrics are recommended in the literature (Green et al., 1995). For instance, Chen and Muller (2010) propose a holistic approach based on two perspectives for measuring the level of innovation, namely the performance perspective and the competence perspective. According to the former, the authors make a distinction between input measures (i.e., investment in innovation projects, number of innovators working in an organisation, percentage of the workforce devoted to innovation projects, etc.) and output measures (i.e., number of new products/services launched in a certain period of time, sales and margins from new products/services, ROI increase in the businesses where innovations were introduced, etc.). Therefore, the competence perspective leads the Authors to suggest more complex and less conventional measures, such as the following: the level of innovative competences among workers, the number of workers positioned in the different competence levels of the firm, the percentage of workers trained on innovation topics, the number of available innovation facilities, such as information systems, the amount of time devoted by the management to support innovation. Similarly, Manu (1992) maintains that innovation is related not only to output (e.g., number of new


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products), but also to input (e.g., R&D expenditure) and timing (e.g., pioneers, followers).

2.2 Managerial control and innovation in the literature The role of MCS’s in supporting innovation processes is a rather debatable and particularly complex research area (Kerssens-van Drongelen and Bilderbeek, 1999; Colarelli O’Connor and Ayers, 2005; Leifer et al., 2001; Simon et al., 2003; Shields, 1997; Brühl et al., 2010; Balkin et al., 2000; Bedford, 2015). The existing literature does not provide sufficiently reliable results that could demonstrate which role formal MCS’s and performance measurement systems (PMS) may play in supporting innovation processes. On the one hand, several authors (Zhao, 2003; Macintosh and Daft, 1987; Davila, 2000; Bassani et al., 2010) pointed out that using MCS’s and PMS’s is not welcome in environments where creativity and cooperation are fundamental in order to guarantee the success of innovation. In fact, it often arises from creative and unstructured processes, which can hardly, if ever, be controlled (Kerssens-van Drongelen and Bilderbeek, 1999). It follows that the traditional MCS shows some limits in the context of highly innovative firms and, hence, should be avoided. Moreover, some empirical studies point out that formal MCS’s are not so widespread among innovative firms (Donnelly, 2000; Kerssens-van Drongelen and Bilderbeek, 1999). Other authors reach the same conclusions after evaluating the consequences, unwanted from a behavioural standpoint, of using formal MCS’s in highly innovative environments (Davila and Wouters, 2005; Fisher et al., 2006). They underline how the emphasis on financial and short-term indicators could stifle inventiveness and innovation impulses and conclude that the creativity of engineers and scientists involved in innovation processes should not be constrained by financial concerns (Shields and Young, 1994; Marginson and Ogden, 2005; Moulang, 2013). On the other hand, some authors deem it crucial to monitor innovation costs in order to protect the shareholder value (Lin and Chen, 2005). Moreover, MCS’s should reduce innovation redundancies (focus costs) and, at the same time, should measure the real benefit of innovation processes (Bisbe and Otley 2004). Other studies highlight a direct relationship between a company’s innovation performance and its financial performance (Balkin et al., 2000; Calantone et al., 1995; Nijssen et al., 1995) and, in a wider perspective, how innovation can contribute to increasing the company’s value (Aggeri and Segrestin, 2007; Hitt et al., 1996). The need hence arises to have some measures to provide for the financial monitoring of innovation activities (Pike et al., 2005; Poh et al., 2001; Rotman, 1994). In an attempt to solve this ambiguity, other research studies, which consider the MCS as an essential element to curb the excess of innovation and establish its benefits,have focused on the way MCS’s are used to support innovation processes (Bisbe and Otley, 2004). According to this body of literature, while an MCS certainly plays a positive role in supporting innovation, its use should be limited in scope. More specifically, Bisbe and Otley point out how studies that consider the MCS as an obstacle to innovation have only focused on its diagnostic use while ignoring the effects of its interactive use, according to the definition provided by Simons (1990, 1991, 1994). In fact, Simons claims that the difference between a diagnostic and an interactive control system is not the kind of instruments implemented in each of them, but the wayin which

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such instruments are used by the management (Sakka et al., 2013, 2016; Chong and Mahama, 2014; Su et al., 2015). The diagnostic use of an MCS mainly consists in the traditional feedback cycle where the MCS is used to monitor and reward the achievement of defined objectives. A diagnostic use leads to achieve goals by focusing on and correcting deviations from standards of performance. If, however, it is used with an interactive function, the MCS is mainly devoted to expanding opportunity seeking and learning throughout the organisation, by reflecting signals sent by the top management. It fosters the development of new ideas and initiatives and a focus on strategic uncertainties. The concept of interactive system has been used as the conceptual framework in a number of survey-based studies. Abernethy and Brownell (1999) highlighted how the interactive use of budgets improves performance in situations of strategic change. Through a survey research design, Henry (2006) examined the impact of interactive systems on various organisational capabilities including innovativeness and entrepreneurship. He found that, in line with Simons’ framework, interactive systems are associated with enhanced innovativeness and entrepreneurial capabilities. Other studies highlighted how interactive systems are crucial when dealing with uncertainty and strategy definition. Finally, Lin and Chen (2005) used the concept of strategic control to examine the change process associated with innovation. According to this perspective, the same mechanism, similarly to the budget or the strategic plan, can be used with a diagnostic purpose or in order to facilitate a better interaction among workers and managers. Simons clarifies that the interactive function of an MCS emerges when the top management “appeals to planning and control procedures in order to involve personally itself in the decision-making activities of the subordinates in order to foster the dialogue among managers and subordinates and to assure the best performance”. The study carried out by Bisbe and Otley (2004) focuses on the interactive use of the MCS and correlates it to product innovation and organisational performance. They argue that the interactive use of an MCS allows the top management to signal and effectively communicate the critical aspects of the organisational activities without inhibiting the development of new ideas. In fact, the interactive dimension of an MCS stimulates the collection of information, enables dialogue and debate between managers and subordinates, facilitates a prompt response to external opportunities and threats, thus feeding a process of organisational learning, which leads to a performance improvement. Bisbe and Otley (2004) argue that the interactive use of an MCS indirectly acts on performance because the link between innovation and performance strengthens itself when the organisation uses an MCS interactively, although they do not provide any empirical evidence of the assumption that the MCS has a mediation function on the results. Against this background, a later study (Dunk, 2011) showed that the impact of product innovation on an organisation’s financial performance depends on the role of the MCS. If the latter, and especially the budget, is mainly used as a planning mechanism, there are good chances that innovations will produce an improvement in the company’s performance. The author assimilates the planning function of the MCS to its interactive use as recommended by Simons (1990). Conversely, if the MCS, and especially the budget, is used as a control mechanism, thus taking on a diagnostic role according to Simons’s classification, product innovation will hardly contribute to improving the organisation’s financial results.


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2.3 Informal control and innovation in the literature Starting from the importance of the interaction between managers and subordinates in order to support creativity, another body of literature focuses on the role of informal MCS’s in innovative firms. In this regard, it is crucial to refer to the wider literature devoted to the following topics: group dynamics management, two-ways communication processes, trust, organisational learning, motivation, and corporate culture. All of these are, indeed, variables that affect the likelihood of success of innovation projects and should be taken into consideration when dealing with the design of innovation control mechanisms. Their impact on the behaviour of the resources devoted to the innovation processes could act as a form of social or informal control, alternative or complementary to the traditional MCS mechanisms, which are typically formal. The balance between formal and informal control mechanisms is a widely debated question in the literature. Some authors claim that the development of each innovation project poses a level of risk that MCS mechanisms, both formal and informal, tend to increase (Currie, 1996). The adoption of a formal MCS contributes to producing a lack of trust, whereas a prevalence of informal control systems fosters chaos and confusion. While project managers admit to prefer formal controls, also as a way to legitimise their role, technicians tend to prefer informal controls because their creativity is more valued with the latter. Currie (1996) solves this contradiction by demonstrating that what matters most in determining the success of a project is not only the implemented control mechanism but also, and most importantly, the type of relations that develop between technicians and project managers. Moreover, he believes that, in order for an innovation to be successful, cooperation must be boosted to the detriment of a rigid compartmentalisation of tasks and roles. From a control point of view, this implies that the most effective mechanisms are informal as they are best suited to nurture such collaborative environment. These informal mechanisms include social and relational control mechanisms (Stafsudd, 2009; Dunk, 2011; Dyck and Zingales, 2002; Atkinson and Butcher, 2003; Kreps, 1990; Coffee 2001; Ahmed, 1998; Jassawalla and Sashital, 2002; Valencia et al., 2010; Martins and Terblanche, 2003; Dobni, 2008; Ellonen et al., 2008), such as media exposure, trust, reputation, social norms and, more generally, the corporate culture. These findings are also in line with the results of studies advocating that an adhocracy organisational culture, more than a hierarchical culture, could facilitate product innovation (Naranjo Valencia, 2010). According to the authors, adhocracy fosters creativity, entrepreneurship, openness, risk taking, etc. Conversely, companies should try to avoid a hierarchical culture that emphasises internal control, strict compliance with rules and regulations and internal orientation. Moreover, some scholars have emphasised the effectiveness of inter-personal channels in the development of innovations. Informal channels of communication are more effective in contexts characterised by greater risk, uncertainty, technological novelty and complexity (Fidler and Johnson, 1984; Johnson, 1990, Tatikonda and Rosenthal, 2000). This type of communication, in fact, can provide immediate feedback and this contributes to reducing uncertainty. On the other hand, an excessively formal communication system may hinder the development of solutions to problems and ideas that require cooperation among individuals or innovative approaches that go beyond a rigid definition of tasks.

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As for informal controls, we believe that the mechanisms capable of fostering trust among individuals and the presence of a corporate culture where innovation is a value to be pursued can play a key role and deserve a special emphasis.

3

Research scope and design

Starting from and going beyond the evidence provided in the literature, this paper is aimed at analysing the impact and the characteristics of MCS’s on innovation in Italian firms. To this purpose, three research hypotheses were developed and tested through an empirical analysis. Hypothesis 1

Formal control mechanisms influence the firm’s innovation performance.

The first hypothesis is aimed at testing whether formal MCS’s can be considered as a driver of a company’s innovation performance. As the literature points out, managerial control is a major management mechanism for aligning decisions, actions, and, more generally, the workers’ behaviour. An appropriate MCS may help the organisation to implement those actions that underpin the top management’s strategic goals. According to this hypothesis, formal MCS’s could significantly influence the organisation’s ability to innovate. Hypothesis 2

An interactive use of formal control mechanisms positively influences the firm’s innovation performance.

As a result of the assumption that formal MCS’s, if present, should not act as constraints to innovation processes but they should rather promote autonomy among innovation team members and support the recognition of strategic opportunities, only formal control mechanisms used to coordinate and address activities, rather than controlling them, can positively influence a company’s innovation performance. This hypothesis was tested taking into consideration the way in which strategic planning and budget are used. Hypothesis 3

Informal control mechanisms positively influence the firm’s innovation performance.

The third hypothesis is aimed at testing whether there is a positive cause-effect relationship between the existence of informal control mechanisms, such as trust-based coordination, intrinsic motivation, direct and personal relationships among people, etc., and the ability of the organisation to innovate.

3.1 Research design 3.1.1 Sample The sample involved in this study was selected from the AIDA database (Analisi Informatizzatadelle Aziende – Italian Company information and business intelligence) by Bureau Van de Dijk. The population consists of 3,692 Italian manufacturing firms belonging to sectors of the Italian economy featuring the largest number of registered patents according to the European trend chart on innovation (Hollanders-Arunden). The following sectors have been analysed: biotechnology, chemicals and chemical products,


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electrical and optical equipment; renewable energies, mechanical equipment; transport equipment. A stratified phase sampling was carried out in order to design a sample that would be representative of the reference population, according to the typical redemption rate of these kinds of surveys. Companies were grouped according to their size, geographical location and business sector. Through a random extraction process, 1,076 companies were selected as recipients of the questionnaire. 122 firms responded to the questionnaire and 104 of such questionnaires were considered valid, which accounts for a response rate of approximately 10%, in line with the response rate of large-scale surveys involving executives (Powell and Dent-Micallef, 1997). Appendix outlines the general characteristics of the responding firms in terms of size, industry, and geographical area. The sample reflects the original stratification: for each stratification dimension we conducted 10,000 two-sample Kolmogorov-Smirnov tests measuring the gap between a sample of size n coming from a population with the stratification in the contacted sample and a large sample coming from a population with the stratification observed in the respondent sample. Average p-values were calculated and the results are 0.64 (revenues), 0.16 (sector), and 0.52 (geography). Since they are above the established significance level of 0.10, we accepted the hypothesis that the contacted sample and the respondent sample were drawn from a population with the same stratification.

3.2 Procedure Data were collected through a structured questionnaire divided into four sections: 1

General information on key corporate data.

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Innovation processes, aimed at obtaining information on the level and type of innovation implemented in the last three years, divided into the different categories taken into account for our specific purpose.

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Management control systems regarding the role and the kind of control mechanisms implemented in order to support the innovation processes. With specific reference to the different control mechanisms, the respondents were asked to indicate the degree of importance of the specific tools.

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Respondent data, which involved information on the organisational position of each respondent.

Secondary data, such as business data on the company’s financial performance (turnover), were gathered from the AIDA database. Most items were measured according to a 1–5 Likert-type scale. Initially, we addressed the questionnaire to R&D or innovation managers. In those firms where these specific roles were not present, the questionnaire was addressed to development managers, production managers, technical managers, and managing directors. The data collection process involved three phases. First, measurement scales were developed by reviewing the relevant literature and by conducting on-site interviews with R&D managers from three large companies. The significance of the most crucial aspects relating to innovation performance and managerial control mechanisms was measured on a five-point scale, where five indicated the highest degree of importance.

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Then, the first version of the questionnaire was pre-tested in a focus group with R&D and innovation managers. As a result of these pre-tests, some items of the questionnaire were amended in order to improve clarity and new items identified during the interviews were added. The resulting final version of the questionnaire consisted of 14 items. It was mailed to the companies included in the final sample described below. The survey involved three steps: initial mailing; first follow-up; second follow-up. In the first step, the questionnaires were administered via e-mail to the identified respondents (R&D managers, development managers, production managers, and managing directors). The first follow-up consisted of an e-mail reminder sent to those who had not answered, while the second follow-up was conducted via phone calls or a questionnaire replacement. After the written and the telephone reminders, we received valid responses from 104 firms.

3.3 Constructs and measures for investigation The focus of this study is to analyse the relationship among three constructs (using some descriptive variables): innovation (dependent variable), formal control mechanisms and their use, role of informal control mechanisms (independent variables). The independent variables were measured by a Likert-type scale ranging from 1 (strongly disagree) to 5 (strongly agree). The measures used were selected according to previous studies to fit with the theoretical definition of constructs. Here below is a summary of the theoretical definitions of the constructs and the measures applied in the study.

3.3.1 Innovation The meaning of innovation has been adjusted according to the framework put forward by the OECD and Eurostat. The sample was asked to indicate the kind of innovation developed in the last three years, distinguishing among six items, i.e., introduction of new products, introduction of new services, improvement of existing products, improvement of existing services, innovation for the market, innovation for the organisation but not for the market. This procedure was deemed effective because the population from which the sample was taken belongs to sectors with a high innovation performance. All companies included in the sample were scored (from 0 to 100) according the kind of innovation they implemented. The two variables representing incremental innovation (improvement of products and improvement of services) received a lower score, while the other two variables – new products and new services – were evaluated differently, depending on the relevance of the innovation, with a distinction between innovations that represent something new for the company or something new for the market. The latter received a higher score than the former. The company’s innovation degree was established based on these criteria.

3.3.2 Control mechanisms The distinction between formal and informal control mechanisms is widely shared among MCS scholars. Formal mechanisms consist of explicit sets of structures, routines, procedures and processes (Maciariello and Kirby, 1994) that help managers ensure that their organisation


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strategies and plans are carried out. To measure the use of formal control, the following items were employed: the use of strategic planning, budgeting, cost accounting, standard costing, performance evaluation that typically relate to developing and implementing new products and services (Merchant, 1998; Simons, 1999; Chenhall et al., 2011). Informal control mechanisms, supported by relational norms and trust, promote social interactions and the development of a shared vision, such as participation in committees and teams, as well as decision making, and more broadly, control that emanates from an organisational culture of shared norms and values and the socialisation of its employees to these values (Jager, 1983; Martinez and Jarillo, 1989). Informal control mechanisms include items that cover more open communications, such as dialogue, direct and personal contacts, value sharing, rewards based on the status, coordination and interactions based on mutual trust (Maciariello and Kirby, 1994). Measuring the presence of formal and/or informal control mechanisms necessarily requires two steps: first, the definition of the tools that conventionally fall into the two classes, and secondly the identification of the relative importance of the different mechanisms. This approach was used also in other studies, which allowed us to rely on those modalities that led to distinguish between formal control tools and informal control tools (Chenhall et al., 2011).

3.3.3 Role of formal control mechanisms According to the previously analysed literature, formal control mechanisms are devoted to supporting a strict formalisation of the company’s goals, especially its financial targets, and are aimed at evaluating their achievement. As far as formal control is concerned, the respondents were asked to indicate the degree of importance, measured on a five-point Likert scale, of the following mechanisms in supporting innovation development: strategic planning, budget, cost accounting, standard cost, performance evaluation.

3.3.4 Role of informal control mechanisms Informal control mechanisms facilitate responsibility sharing, encourage cooperation, and contribute to motivating the organisation’s members. As far as informal control is concerned, the respondents were asked to indicate the degree of importance, measured on a five-point Likert scale, of the following mechanisms in supporting innovation development: dialogue, direct and personal contacts, value sharing, rewards based on the status, coordination based on mutual trust.

3.3.5 Interactive and diagnostic use of formal control The respondents were also asked to indicate how strategic planning and budgeting are used in their organisation. These two mechanisms were selected because, according to the literature, they lend themselves better than others to be used in a diagnostic or interactive manner. More specifically, the diagnostic use was tested by asking questions, both for strategic planning and budgeting, on the importance of the following purposes of the mechanism: communicating and controlling the company’s financial performance; monitoring the achievement of innovation projects; monitoring the financial performance of innovation projects.

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Conversely, the interactive use was tested by asking questions, both for strategic planning and budgeting, on the importance of the following purposes of the mechanism: favouring coordination; learning development; addressing uncertainties. As to the degree of importance assigned to the different types of managerial control, we believe that the responses can be considered to be representative of reality for two main reasons: 1

the respondents are managers in charge of innovation processes, which ensures that their answers are qualified

2

the respondents are usually directly involved in innovation planning and control processes: they participate in target-setting activities and use control mechanisms in order to better manage the innovation projects that fall under their responsibility.

3.3.6 Control variables Two important variables used for typifying the companies included in the sample were their age – calculated as from the year of their establishment and size – based on their turnover. Previous studies demonstrate that the age of an organisation is inversely related to innovation output (Hansen, 1992). More specifically, these studies claim that young enterprises are more inclined to innovate than mature organisations. In our analysis, the company’s age is represented by a discrete and quantitative variable. Research also demonstrates that the size of a company may be linked to its innovation performance (Cohen and Mowery, 1984; Ettlie et al., 1984; Bantel and Jackson, 1989). Some studies conclude that big companies have larger financial and human resources to devote to innovation projects than smaller enterprises (Nord and Tucker, 1987), while others prove that, due to their higher complexity, formalisation, and rigidity, large organisations may have some resistance to innovation (Cameron and Quinn, 1999). Conversely, small business can be more innovative because they are more flexible, have a better ability to adapt, and fewer constraints in implementing changes (Damanpour, 1991). Corporate size was measured both by the 2011 turnover and by the number of employees in the same year. Because sales data were highly fragmented, the Neperian logarithm was used to estimate the turnover in order to avoid the scale effect that might have resulted from using the original variable.

3.4 Methodology and results To study the relationship between innovation and control mechanisms, we took a logistic regression approach. This choice is formally justified below through diagnostic tests. The dependent variable is innovation, a dichotomous variable equal to 0 or 1 if the innovation performance of the firm is, respectively, below or above a threshold of 80, on a scale from 0 to 100. The threshold was established in such a way that the sample is evenly split, with 54 firms below or equal to the threshold and 50 firms above. In all models studied below, revenues and age were consistently used as control variables. A


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relationship is considered to be significant if the corresponding p-value is below 0.1, which is a common choice in the literature.

3.5 Control mechanisms We aggregated into the variables mecform and mecinform formal and informal control mechanisms, respectively (strategic planning, budget, cost accounting, standard costing, performance evaluation as formal control mechanisms, and dialogue, personal contacts, values, status, trust as informal mechanisms). In model Mod1, used to test Hypothesis 1, we noted a weaklink between formal mechanisms and innovation, and a negative relation. Hypothesis 1, aimed at testing if a formal MCS can be considered as a driver of a company’s innovation performance, has to be rejected. It seems that a formal MCS has a low impact on innovation processes and, actually, shows a negative relation. Mod1: inno ~ mecform age revenues Table 1 (Intercept) mecform Age Revenues

Relation between formal control mechanisms and innovation performance Estimate 1.536e+00 –4.841e–01 9.631e–03 –2.138e–09

Std. error 1.221e+00 3.297e–01 7.590e–03 1.393e–09

z value 1.258 –1.468 1.269 –1.535

Pr (>|z|) 0.208 0.142 0.204 0.125

In order to better understand the source of this relation, we broke down the formal mechanisms according to the way they are used, making a distinction between their diagnostic and interactive role. As was previously mentioned, the role was investigated with reference to budget and strategic planning as these variables lend themselves, better than others, to be used in the two different ways. Model 2 was used to test Hypothesis 2. This hypothesis was aimed at testing whether there is a positive influence between an interactive use of formal control mechanisms and a company’s innovation performance. Only formal control mechanisms used to coordinate activities (interactive use of control mechanisms), rather than control activities, may actually influence an organisation’s innovation performance. Model 2 which focus on the variables interactivebudget and interactiveplan, shows that the presence of an interactive budget (instead of a diagnostic budget) is positively related with innovation, whilst having interactive or diagnostic plans does not have a major effect. According to our findings, there is a positive and significant relationship between the use of formal mechanisms to coordinate organisational activities and the probability of being a highly innovative company, although this is true only for the budget. Hence, Hypothesis 2 is accepted. This means that formal control mechanisms used to coordinate activities, in this specific case budget tools, can indeed influence a company’s innovation performance. Those companies that use budget as a coordination toolhave more chances to become innovative champions. Mod2 : inno ~ interactivebudget interactiveplan age revenues

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Table 2

Relation between interactive use of formal control mechanisms and innovation performance Estimate

Std. error

z value

Pr (>|z|)

(Intercept)

–4.633e+00

2.356e+00

–1.967

0.0496*

interactivebudget

5.959e+00

2.950e+00

2.020

0.0434*

interactiveplan

1.443e+00

3.100e+00

0.466

0.6415

Age

9.131e–03

7.677e–03

1.190

0.2342

Revenues

–2.632e–09

1.516e–09

–1.736

0.0825

Notes: The asterisk denotes that the test is significant at 1% level. List of significance levels: ***0; **0.001; *0.01; 0.05. A lower level is interpreted as more evidence in favour of a significant relation between the independent and dependent variables.

Model 3 was used to test Hypothesis 3, aimed at showing whether there is a positive cause-effect relationship between the existence of informal control mechanisms and the ability of an organisation to innovate. According to this hypothesis, informal control mechanisms positively affect a company’s degree of innovation. Informal mechanisms in model Mod3 do not show any sign of significance. Even after breaking down the aggregate measure of informal mechanisms into dialog (post-decision dialog), personal (personal relationships), motivation (stimulus to motivation), status (status recognition) and trust (trust-based coordination), no significant relationship with innovation emerged. Hypothesis 3 has to be rejected. There is no positive cause-effect relationship between the presence of informal control mechanisms, such as trust-based coordination, intrinsic motivation, direct and personal relationships among people, etc., and the ability of the organisation to innovate and become an innovative champion. Mod3 : inno ~ mecinform age revenues Table 3

Relation between informal control mechanisms and innovation performance Estimate

Std. error

z value

Pr (>|z|)

(Intercept)

1.920e+00

1.435e+00

1.337

0.1811

mecinform

–5.486e–01

3.625e–01

–1.513

0.1302

Age

1.091e–02

7.484e–03

1.458

0.1447

Revenues

–2.687e–09

1.473e–09

–1.825

0.0681

3.6 Diagnostic checks In this subsection we formally justify our choice of the logistic model. We conducted three well-known goodness-of-fit tests for logistic regression: x

Modified Hosmer and Lemeshow test. Hosmer et al. (1988) propose a statistic computed by a grouping method based on percentiles of the estimated probabilities

x

Osius and Rojek test. Osius and Rojek (1992) derive an easily computed large-sample normal approximation to the distribution of the Pearson chi-square statistic for assessing the fit of the model

The role of managerial control in innovation processes x

363

Stukel test. Stukel (1988) proposes a test that determines whether the basic form of the model is consistent with the shape and symmetry of the logistic function.

We strongly believe that these methods do not test the overall significance of the regressors but rather the validity of the chosen model, regardless of significance. In this respect, we fully agree with Hosmer et al. (1997), who recommend that the overall assessment of fit be examined using a combination of tests, namely the Hosmer-Lemeshow decile of risks test, the Osius and Rojek normal approximated test, and Stukel’s test on the link function. All the tests show p-values that are well above 0.10 for all the analysed models, thus strongly indicating the appropriateness of the logistic regression in this study.

4

Discussion

This paper focuses on the impact of MCS’s on product/service innovation processes. Although the literature highlights the relevance of MCS’s in supporting innovation, there is little empirical evidence of this relation. Our empirical analysis confirms that the role of MCS’s in supporting innovation processes isa research field that, so far, has not been investigated thoroughly enough. The results of our analysis do not endorse the idea that an MCS, acting as a fundamental driver of the individual behaviour, may drive an organisation to pursue innovation-oriented decisions and actions and may account for improved innovation performance. Materialising innovation requires the adoption of other managerial mechanisms, above all, organisational mechanisms, capable of stimulating knowledge development and, hence, innovation. The evidence gathered in our research seems to deny some of the conclusions of other studies. Most notably, it conflicts with the position of those authors who claim that a systematic monitoring of innovation-related costs is crucial in order to not jeopardise the shareholders’ profitability interests or to curb any innovation excess. Moreover, even informal mechanisms do not seem to have any joint effect on an organisation’s innovation performance. Our results are actually in line with the findings of those who studied the implications of the interactive use of MCS’s, as defined by Simons, leading to the conclusion that an MCS acts as an obstacle to innovation only when it is used for pursuing diagnostic functions, e.g., for control purposes in the strictest sense. More specifically, we have found that product/service innovation is positively associated with an interactive role of the MCS. In fact, the analysis of the collected data highlights that, although they are necessary in order to evaluate business performance, control mechanisms become effective innovation drivers only when they are implemented for the purpose of fostering coordination, communication, and learning within the groups devoted to innovation. The implications of these results for practitioners are clear. Organisations hoping to enhance their innovation performance should pay careful attention to the MCS as it could either enhance or inhibit product innovation. According to our findings, organisations should specifically endeavour to develop an MCS that can blend the monitoring activity with free thinking and the search for opportunities. Conversely, companies should try to avoid diagnostic MCS’s, strict compliance with rules and regulations, a rigid performance evaluation and internal

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orientation. These results are in line with studies that approached the topic of the winning organisational culture in innovation processes (Naranjo Valencia et al., 2010). As was previously pointed out, these studies promote the adhocracy culture, i.e., internal value systems fostering creativity, entrepreneurship, openness, risk taking, etc. A participatory work environment, making more information available to the members of the organisation, increases their awareness, commitment and involvement, thus contributing to innovation (Damanpour, 1991). Based on the results of our study, we offer managers the following recommendations: an MCS may help increase the effectiveness and efficiency of innovation processes, while not acting as one of their drivers. An MCS does not need to be highly formalised. Using mechanisms that, although formalised, stimulate the autonomy of the innovation team members is undoubtedly a preferable option. Autonomous teams drive the organisation to increase its chances of introducing unexpected opportunities. Autonomy increases the likelihood for individuals to be internally motivated to create new knowledge.

5

Limits and future direction of research

In spite of its contributions, the results of this paper should not be interpreted without considering the limitations of an empirical study. This study actually has three main limitations. First, the data involved in it were collected from one source. Although most studies are primarily designed to use a single informant, multiple informants would strengthen the validity of the research findings. A second limitation is that we have analysed the impact of managerial control on innovation without distinguishing between the different phases of the innovation development process, i.e., the creativity phase and the design phase. These two phases might produce opposite results. The third limitation of our analysis is the relatively small size of the sample (104 firms) and the heterogeneity of the questionnaire respondents. Future studies should address these limitations in different ways. For instance, in order to examine the causality of these relationships, the characteristics of managerial control should be observed separately for each phase of the innovation process. Moreover, in our opinion, future research should study the moderator effect of some variables on the MCS-innovation relation. For example, the type of innovation might well influence this relation. Again, it could be interesting to evaluate if and to what extent the types and the mix of control mechanisms used to support innovation can be related to some strategic and organisational choices implemented by innovative firms. Finally, while we studied the relationship between managerial control and output innovation, it could be interesting to use, for example, other innovation measures, such as input indicators. All these limitations could be addressed in future research projects, thereby helping to build a richer and sounder theoretical basis for studying innovation in business organisations.


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Appendix Table A1

Sample descriptive analysis

Size

Frequency

Small (sales 10 mln < 50 mln €)

39%

Large (sales > 50 mln €)

31% 100%

Industry Chemicals and chemical products

27%

Biotechnology

8%

Renewable energies

7%

Electrical and optical equipment

15%

Mechanical equipment

24%

Transport equipment

12%

Other

7% 100%

Geographical area North West

32%

North East

40%

Centre

20%

South and Islands

8% 100%

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Table A1

Sample descriptive analysis (continued)

Respondent

Frequency

R&D manager

25%

Top management

29%

Controller/AFC department

16%

Other

19%

Missing

11% 100%