French manufacturing firms’ reorganizational performance

Abstract

This article sheds some light on the economics field. It establishes a link between French manufacturing firms’ reorganization and performance by using the matching method, which Donald B. Rubin first introduced. Through matching the 2006 COI survey and the DIANE database, the available data show that reorganization improves French manufacturing companies’ added value, turnover, labor productivity and the productivity of financial capital.

Keywords

Network modernization complexity production knowledge performance method of matching

Kais MTAR, A Doctor of Economics at the University of Nice Sophia Antipolis, MTAR Kais, aged 36 got his Master’s degree in 2007 from the same institution. In 2015, he worked as Assistant Professor on contract basis at the “University of Rennes 2” in France. Apart from taking part in seminars and conferences, Kais has already published an article in the year 2015 which is entitled ‘FDI’s Effects on the Tunisian Economy in a New Context’, (2015), International Journal of Applied Research and Studies 2015, Volume 4, Issue 1, P.1-12.

Souad BANNOUR (born in 1983 in Mahdia, Tunisia) holds a Phd in Economic Sciences (2013) from the University of Sousse and the University of Nice Sophia Antipolis, France. She obtained her Master’s degree in 2007 from the same institution. From 2014 till now, Souad has worked as Assistant Professor on contract basis at the High Institute of Management in Sousse as well as in the Faculty of economic Sciences and Management, Mahdia. She also worked as temporary teacher at the high Institute of Technological Studies in Mahdia. Apart from taking an active part in related seminars and conferences, Souad has already published 3 papers in the year 2015: The first article is entitled:The Determinants of FDI in Tunisia: An Empirical Study through a Gravity Model, Asian Economic and Financial Review 2015, 5(12): 1306-1314. The second one is about: The Attracting Factors of Foreign Direct Investment: Tunisia’s Cause After the Revolution, Journal Advanced Research 2015, Volume 3, Issue 2, P.369-375. The third one is about: FDI’s Effects on the Tunisian Economy in a New Context, (2015), International Journal of Applied Research and Studies 2015, Volume 4, Issue 1, P.1-12.

Xavier Richet is a Professor emeritus of Economic Sciences. He holds several academic credentials such as Master’s degree from Paris 1 and a Phd in Economics (Paris 10). He also worked as Doctor honoris causa of Corvinus university, Budapest as well as a lecturer at a number of prestigious universities such as the university of Orléans, Dauphine, Lyon 2, Grenoble 2, Nice, Ecole supérieure de commerce de Paris, Ecole Nationale d’Administration. Among his international activities, Dr Richet has been very active as professor and visiting researcher in a number of universities abroad: Great Britain, Albania, Algeria, Germany, Australia, Brazil, Bulgaria, Canada, Colombia, China, Cuba, the United States, Hungary, Mexico, Morocco, Moldavia, Poland, Romania, Russia, Czech Republic, Serbia, and Vietnam. He also helped organize international conferences and seminars in France Tunisia, Morocco, Hungary, Bulgaria, and China.Dr Richet authored a number of books among which we can cite the following:

The Hungarian Model, Cambridge, Cambridge University Press, 1989, Les économies socialistes européennes. Crise et transition, Paris, Armand Colin, 1992

Economie de l’entreprise, Hachette, Col. Fondamentaux, 1994, 1996, 2001, 2006. Traduit et publication en russe, slovaque, vietnamien, albanais. Publication en cours en chinois, portugais (Brésil).

1 Introduction

The US housing bubble, which reared its ugly head in 2006, wasn’t long before it hit hard the American financial and banking sectors. Two years later, it swept across all economic sectors and, shortly after, the chain reaction spread across almost all countries of the world spurring sluggish economy, gradual decline in the purchasing power and, more significantly, a considerable drop in the industrial production in the euro area. Hence, under the new environmental constraints, it arises the need for companies to think again about their organizational methods as well as their human resources management. The latter are expressed particularly through the implementation of a package of new managerial arrangements affecting their different organizational dimensions. First, businesses reorganization issues focused mainly on the internal dimension, namely structural changes, modernization of production processes and improving the quality system. Then in the early 1990s, the emphasis shifted to relations with external partners. The third and final aspect of the reorganization of companies addressed the question such as the need for a renewed concept of management and decision-making.

However, these organizational innovations do not always lead to the aimed effect. Some authors complain about the cumbersome procedures and fundamental tensions of these new organizational practices that may come into conflict with the new management. Their influence on performance reveals a phenomenon that has attracted growing interest from researchers for thirty years and this interest has never lost its vigor and is still regarded as a real ‘black box’ [13].

In this context, the present paper attempts to provide answers to the evaluation of the impact of the reorganization on businesses performance. A company is called reorganized only if it uses at least six of the fourteen management practices presented. To solve this problem, the matching method will be used, as initially developed by [33]. This method offers an interesting alternative to the analysis, implementing nonparametric estimates while maintaining the individual character of the impact of reorganization on firm performance. A special feature of this method is its ability to estimate the overall effect ofreorganization only on reorganized firms andfurthermore, the effect is homogeneous within companies, in contrast to estimates by the nonlinear least squares method of a Translog or Cobb-Douglas function production type, which requires an estimation of the overall effect of reorganization on allcompanies [24].

The paper is organized as follows. While section I examines the organizational changes brought to the network company thus leading to the implementation of new management practices, Section II overviews some academic studies that tried to identify and examine the link between the implementation of organizational innovations and the expected benefits. The econometric model and the different estimators are discussed in section III. The available data as taken from the matched findings of the 2006 COI survey and DIANE database are presented in Section IV. Finally, Section V concludes with some comments.

2 Organizational transformation in French companies: Towards an new industrial landscape

In the face of major changes in today’s capitalism due to the financial and economic crisis, the versatility of the consumer as well as the explosion of the Internet and Biotechnology, a consensus has been reached among the scientific committee on the need to modernize the organization of the company in order to beat market competition and secure a leading position. As part of this framework, a growing number of firms facing fierce competition and intransigent demand have opted for major organizational changes at three levels. These changes are both deliberate and imposed.

The first level of change relates to internal reorganizations of businesses, which is translated into a transformation of their hierarchical forms, a modernization of their productive systems and the improvement of their quality systems. The literature shows that the hierarchical structure inherited from the Taylor-Fordist model is no longer compatible with the economic, technological and social trends in which firms operate. Faced with ever-changing economic situation, most companies developed a tendency to set up new structural forms that pass through the delivering of the hierarchy to end the organizational ‘silos’ that weigh heavily on performance and innovations.

The second aspect is the evolution of the production paradigm. In this context, an increasing number of scholar’s havefound that the chain productionsystem has lost considerable ground to other systems based on semi-autonomous work teams and the JIT delivery and production processes. Through these collective arrangements, the organization is working today to make employees work together and simultaneously rather than separately and sequentially [6] to benefit from the economic, organizational and technical benefits.

The third and last line of approach is the improvement of quality management, including a ‘holistic quality’ determined by the quality assurance standards and certificates on the environment or ethics. These standards appear as streamlining tools that bring significant progress to the organization of business and real levers for improvement of the company’s internal expertise. They are seen as the formalization of skills as referred to by [30].

The second category of transformation is the emergence of new forms of partnerships and inter-company coordination. In fact, the phenomenon of inter-firm partnerships is certainly not recent, but they have gained momentum since the early 1980s. They belong to the context of participating in the wave of organizational innovations that have led companies to adopt qualified transverse structures, some observers even called it, ‘second organizational revolution’ [10]. Furthermore, the implementation of new management practices, such as research and or development partnership with companies or private laboratories and research and or development partnership with the CNRS, universities or other organizations, is perceived as a new mechanism that offers a unique opportunity to acquire and build up skills and knowledge which companies cannot do without in order to sustain their growth and vibrancy. According to [26], this new industrial configuration gives rise to a recombination of new methods to organize product development and production processes which, in turn, involves the emergence of innovative ways of coordination, such as reducing the number of first-tier suppliers and customers. Hence the nature of interorganizational relationships is alsotransformed.

The third and last level deals with the emergence of a new project management system which goes hand in hand with the mobilization of new organizational practices. This new system is mainly based on the simultaneous combination of formal arrangements and social mechanisms. Thus, the implementation of a performance dashboard, the use of a monitoring scoreboard and the mobilization of a traceability system appear as relevant tools in the modernization of enterprises. Similarly, adequate social arrangements such as group membership and belonging to a network facilitate knowledge dissemination within the organization.

Although literary reviews offer a wide range of organizational innovations, the only managerial practices kept are those that are obtained by overlapping the list of precisely advanced practices and other practices originating from the 2006 COI survey.

The matching between this analysis and the results from the 2006 COI survey allowed us to come up with fourteen new managerial practices as shown in Table 1. The findings of the COI survey revealed that both monitoring scoreboards (Reporting) and performance dashboards (Balanced Scorecard) seem to be the organizational instruments of choice for French companies (87% and 85%) in sharp contrast with a few companies which claim they belonged to a network (3.24%).

3 Relations between organizational innovations and performance

In a global economy (where demand has become increasingly volatile, fluctuating and uncertain) the implementation of organizational innovation has been widely accepted as the only course of action to boost business performance. Therefore, several empirical approaches attempt to explain this causality.

3.1 The benefits of implementing new management practices

Literature offers an increasing number of variables that seek to measure the contribution of reorganizations. These variables are set out under fourheadings:

Production process: The increasing complexity of product components and the speed of change shed light on theories of contracts and theories of competencies, which proves that the introduction of new management practices helps improve the company’s internal production processes. It also allows reducing dispatch delays and stock levels, to produce significant increase in flexibility and responsiveness, to minimize unnecessary time and fight against failure. This leads to improve customer satisfaction, reduce costs and increase productivity.

Finding 1: Reorganizations boost productivity and reduce costs and thus act positively on added value (VA).

The commercial process: The diversification of the sources of supply and the outburst of the production process are regarded as leading factors towards reorganization as a means of establishing trust between the different partners. Taking into consideration the strategic managerial perspective, the implementation of new management practices such as quality or environmental standards should be regarded as a differentiator on the market within a commercial approach [7].

Finding 2: reorganizations improve the commercial aspect measured by turnover (CA).

The financial dimension: organizational innovations provide verifiable assurance to markets whose products are manufactured and tested according to a minimum of quality standards that meet customers expectations. They are regarded as an information signal on the stock market, which is very sensitive to communication actions undertaken by companies [31]. In addition, financial theory indicates that reorganization can be analyzed as multiple strategies in order to ensure optimal coordination of individuals, therefore, enabling the company to control its business risk and achieve the expected return on its plan assets.

Finding 3: Reorganizations have a positive impact on the financial aspect measured by the productivity of the financial capital.

The organizational dimension: competence and knowledge-based approaches to the theory of the firm indicate that the new managerial practices, via procedures and work standards establish within the company a homogenization of ways of working. It is credited with reducing uncertainty regarding coordination and relational complexity. These new practices are considered as preferred vehicles for the acquisition and transfer of knowledge and resources [29]. On the other hand, the psychological theory reveals that these new management practices are perceived as sources of motivation. Hence, they are regarded as integrated techniques since they tend to mobilize and bolster internal cohesion around a common project of the company [5].

Finding 4: Reorganizations have a positive impact on the internal management which increases labor productivity).

4 Exploring the connection between reorganizations and business performance

Recent research in economics and management has spilled much ink in an attempt to establish a correlation between organizational practices and business performance. [11] grouped these empirical propositions under three approaches:

The universal (universalistic): It includes works that have examined the intensity of use of a particular system of organizational innovations. These works revolve around the assumption that a specific set of managerial practices always yields the best results. [3] point out that the identification of these practices is based on the observation of good organizational practices of firms deemed ‘excellent’ in comparison to others, and that all organizations would improve their performance after adopting them. In this sense, the universal perspective adopts a linear approach to the relationship between one or more organizational innovations and performance variables; this approach can be extended to the entirepopulation [11].

The contingent perspective: This approach shows that the effectiveness of organizational innovations requires a certain degree of consistency with the various factors of contingence prevailing in the business environment. It focuses on the contextualization of the management of human resources [1].

The ‘configurational’ perspective: It assumes that the maximization of the company’s performance is closely linked to the coordination between the internal cohesion and external cohesion. The objective of this complementarity is to achieve synergies between organizational practices that reinforce each practice. These practices create a real chain reaction [34], which will consequently lead to betterperformance.

Although each of these three perspectives is tenable from a theoretical point of view, it is the Universalist approach that will be selected in this paper since it is consistent with aim to gauge how the implementation of new management practices could influence business performance. This study does not attempt to identify the mechanisms that explain the relationship between organizational innovation and economic performance of companies in isolation from their internal and external contexts. The primary objective behind this case study is to evaluate the impact that the implementation of certain organizational arrangements might have on the economic performance of Frenchcompanies.

5 Construction of a reorganization indicator

To measure the total index of reorganization, [28] assumes that a company is reorganized only if it employs at least two management practices from the four organizational devices it proposes. Further empirical work has been based on this study and has been developed in the same process by [24] and [23]. They all believe that a company is said reorganized only if it adopts at least two of the thirteen organizational arrangements.

For the sake of conformity with most of the mainstream literature, a company is said reorganized only if it uses at least two managerial practices, which is highly restrictive. Although this proposal was widely accepted in the early 1990s, it is no longer the case today because industrial companies are increasingly affected by the dynamic change of the environment, which modifies the comparative advantages between companies. As a result, literature shows that companies are involved in major organizational changes that have occurred in the wake of implementation a growing number of organizational innovations during the past two decades [25]. Moreover, the results of the 2006 IOC survey indicate that French companies resort to several management practices at the same time. On average they employ almost six devices (see Table 2).

In addition, a new synthetic indicator of reorganization is proposed, which is the adoption by the same company of at least six of the thirteen managerial practices presented above.

6 Assessment methods in measuring reorganization on the performance of French companies

For the reasons mentioned above, it has been suggested in this work that the causality betweenreorganizations and business performance be tested by taking into account the selection bias. To this end, the Rubin model is put into play.

6.1 The model of Rubin (1974)

Let us consider a set of firms i = 1, 2....... N making or not the object of a treatment (here having reorganized) and y_I denoting business performance variable. The Rubin Causal Model rests mainly on the existence of two unobserved result variables namely Y_0i and Y_1i.. These two variables are random variables associated with each company, and correspond to potential achievements of the performance variable, depending on whether the company is reorganized or not. Potential Y_0i and Y_1i do exist before undergoing the treatment, but only one of them is observed. Thus, for a reorganized firm Y_1i is observed while Y_0i is unknown.

A direct way to test the impact of reorganization is to consider the difference in means performances of reorganized and not reorganized companies. This method relies on the assumption of independence between potential performance variables Y_0i and Y_1i and the variable of certification choice T_i = 1. Under the effect of this assumption, one can deduce that:

$\begin{matrix} C = E (Y_{1 i}) - E (Y_{0 i}) = E (Y_{1 i} / T = 1) \\ - E (Y_{0 i} / T = 0) \end{matrix}$ (1)

The assumption of interdependence between variables of potential performances and variables of reorganization choice is very restrictive. Since the previous independence property is not satisfied, the natural estimator formed by the difference in mean outcome variables is affected by a selection bias: $\begin{matrix} C = E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 0) \\ = E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 0) \\ + [E (Y_{0 i} / T = 1) - E (Y_{0 i} / T = 1)] \\ = [E (Y_{1 i} / T = 1) - E (Y_{0 i} / T = 1)] \\ + [E (Y_{0 i} / T = 1) - E (Y_{0 i} / T = 0)] \\ = C_{T = 1} + β \end{matrix}$ (2)

Selection bias is the term β = E (Y_0i/T = 1) - E (Y_0i/T = 0) in the above expression. This bias is reflected in the fact that the mean situation of reorganized companies has not been the same as that of those untreated companies. However, this difficulty can be circumvented only if each firm, reorganized or not, one can observe the mean performance of non reorganized companies is the same as that of reorganized and not reorganized firms. That is to ask the condition of independence between the variable potential performance of non reorganized companies Y_0i and the treatment choice T: $Y_{0 i} ⊥ T \Rightarrow E (Y_{0 i} / T = 0) = E (Y_{0 i})$

But there are solid reasons to believe that this will not usually be the case. Several key differences exist between the reorganized and non reorganized firms: strategy, market trends, the use of technology, pressure from customers and suppliers, etc. Therefore, (unconditional) independence between latent result variables (Y_1i and Y_0i) and treatment assignment T is very unlikely. A less restrictive condition is to consider that there is a set of conditional observable variables X to which the property of independence between latent results and reorganization is verified. This property requires, in general, consideration of a large number of conditioning variables. As a result, matching on observable characteristics can be difficult to achieve in practice. This problem was solved by [32] who proposed an alternative method that consists of estimating for each reorganized and non reorganized company a probability to be members of a class held by a counterfactual master based on its observable baseline characteristics X: the propensity score, as noted.

6.2 Illustration of different methods and estimation steps

To test the impact of reorganization on firm performance, there is a whole battery of estimators based on assumptions more or less demanding and whose implementation also has variable degrees of complexity [23]. In this paper, the following estimates will be employed: the naive estimator, nearest neighbor estimator, estimator caliper or Radius and finally the matching estimator with kernel function.

6.2.1 The naive estimate

The naive estimate is the simplest solution according to some authors [12]. It consists in the difference between the arithmetic averages of Y_1i and Y_0i.

6.2.2 The estimate by the nearest neighbor [Nearest Neighbour (NN)]:

The Nearest Neighbor matching is probably one of the most straightforward matching procedures [15]. A company from the comparison group is chosen as a match for a treated company in terms of the closest propensity score. The matching of the M nearest neighbors can be achieved (M ≻ 1). In this study, M = 1 is chosen without replacement. The estimator of the average effect of the introduction of certification of certified companies is written asfollows:

$\begin{matrix} C_{T = 1} = \frac{1}{N_{1}} \sum_{T_{i} = 1} [Y_{i}^{1} \\ - \sum_{T_{j} = 0} ω_{N_{0}} (i, j) Y_{j}^{0}] \end{matrix}$ (3)

6.2.3 Caliper or radius matching

By applying this method, a company from the control group is matched with another company from the reorganized group, located inside the caliper (or maximum propensity score distance by which a match can be made) and it is the closest in terms of propensity score. In this research, a level of intensity (radius) 0.01 is imposed which avoids the risk of poor matches and improves the quality of thematch [8].

In many-to-one (Radius) caliper matching, the estimator of program impact may be written as:

$\begin{matrix} C_{T = 1} = \frac{1}{N_{1}} \sum_{T_{i} = 1} [Y_{i}^{1} \\ - \sum_{T_{j} = 0} ω_{N_{0}} (i, j) Y_{j}^{0}] \end{matrix}$ (4)

6.3 The estimate by matching with kernel function

The kernel matching estimator proposed by [21] offers different estimates that follow the previous terminologies by [16] and [14]. For them, the kernel matching uses all cases in the control group to construct the counterfactual for each reorganized company. The final estimator of the reorganization impact, certification in condition, is as follows [21]: $\begin{matrix} \hat{E} [Y_{0} ⊥ P (x) = P (x_{i})] \\ = \sum_{j \in I_{0}} \frac{K [(P (x_{j}) - (P (x_{i})) / h]}{\sum_{j \in I_{\circ}} K [(P (x_{j}) - (P (x_{i})) / h]} y_{j} \end{matrix}$ (5)

6.4 The steps of the estimate

The estimation procedure involves three main steps: calculating the propensity score, determining the propensity score and finally the estimate tospeak:

The calculation of the propensity score: In this step, it is necessary to estimate the probability of selection of each company reorganization or canonical score. The purpose of this assessment is to explain the T variable of the choice of reorganization by observable characteristics X. Assuming that this company’s choice is only the visible manifestation of a latent summarizing incitement the reorganization, the logit model to test whether observable characteristics X is suspected to influence the decision of the choice of reorganization, even though it has a significant effect on the implementation of new management practices. This step is informative because it provides a description of the assignment to treatment.

Determining the propensity score: The common-support of the densities scores of the two groups of companies is determined as soon as each company’s propensity scores are calculated. The question of determining the distribution support of the propensity score is essential in this type of analysis [21]. This support indicates the existence of both the density of the propensity score for groups for reorganized companies and that of none reorganized.

The estimate to speak: Just apply the formulas.

7 Presentation of data

Mobilized information is taken from the matching of the 2006 COI survey, jointly produced by DARES and INSEE, with information about businesses performance from DIANE files.

7.1 Presentation of the survey COI 2006

The survey conducted by the COI in 2006 is the result of a growing interest in research and in international organizations, in the late 1980s, by the organizational and technological business dynamics. It is a deep extension of the first COI 1997 survey [17]. The survey was carried out simultaneously on a representative sample of industrial companies and a representative sample of their permanent employees. The development and coordination of investigative device were performed in the Centre d’Etudes de l’Emploi (France’s Centre for Employment Studies), under the direction of Nathalie Greenan and Danièle Guillemot and jointly produced by other statistical agencies. The survey design was developed by a number of researchers from various disciplines. The survey questioned 16.962 companies with 10 or more employees divided into the following segments: sections D (Manufacturing), E (Production and distribution of electricity, water and gas), F (Construction), G (Trade), H (Hotels and restaurants), I (Transport and communication), J (financial intermediation), K (Real estate, renting and business activities) and groups 92.1 and 92.2 (motion picture and video activities, radio and television). Public companies in these sectors are part of the COI field 2006. The overall response ratewas 85%.

7.2 DIANE

DIANE files (disk for Economic Analysis) are composed of information available in the tax files, namely, in particular, the balance sheet and income statement of 885 422 French companies in 2011. These companies are divided into four targets: A (140.074 companies), B (139.985 companies),C (133.574 companies) and D (471.816 businesses). This source allows identifying 113.823 manufacturing companies.

Information is available from the annual financial statements (balance sheet, income statement, statement of assets and liabilities...) that all public limited company (SA), limited liability company (LLC), a general partnership whose partners are SARL or SA and agricultural cooperatives whose turnover exceeds EUR 75,000, are required to file with the clerks of the commercial courts under penalty of a fine of 1,500 euro [24]. DIANE files have been heavily used in this paper in an attempt to gauge the economic and financial performance of companies.

7.3 Matching data

As stated above, 16.962 companies of 10 or more employees responded to the 2006 COI survey. These companies spread across 8 fields of economic activity. In the present study, only the manufacturing industry has been included. Of the 3,962 manufacturers documented in the file of the COI survey, 2,339 were found in DIANE files for the years 2005, 2006 and 2009.

Companies producing negative added value as well as companies in which at least one of the growth rate of variables (value added, turnover, labor productivity or financial performance) shows an outlier, were then excluded from the data set [24].

In general, the selected sample consists of 2,067 manufacturing companies with 10 employees or more in 2006, only 52.1% of the numbers of initial manufacturing companies are kept.

8 Results and comments

8.1 Logistic regression and construction of common support

The results of estimating the logit model presented in Table 3 show that the financial restructuring and the implementation of the new sites positively influence the probability of reorganization. Similarly, interventions in the European market and the international market lead to a high probability of reorganized. It has also been noted that the use of ICT (Information and Communication Technologies) (such as the Web, LAN, Intranet, Extranet and EDI) significantly increases the likelihood of reorganization. In terms of customer relations, the results show that the membership of the largest customers to a network, their requirement to comply with a quality standard and the provision for a data-processing system accessible to the major customers significantly increases the probability of reorganization.

From the results of the estimation of logistic regression model, propensity scores of each company reorganized and not reorganized have been establishedby retaining only the significance of variables at 10% level or less. Figure 1 shows that the surface of the common propensity score covers a wide range: the region of common support obtained is around [0.18; 0.95].

8.2 The results and interpretation of estimated corporate reorganizations on their performance

Once the propensity score and common support, were established, the next logical step would be to assess the impact of reorganization on the growth rate of the added value, labor productivity and profitability of financial capital between 2006 and 2009. The results of these tests are presented in the following table:

The first results in Table 4 show that the overall effect of the reorganization on the growth rate of the added value and the rate of growth of financial capital productivity is positive and significant in all estimators. This effect appears stronger and quite significant when it is estimated using the nearest neighbor estimator and the radius estimator: reorganized firms have improved their added value growth rate by 11.9% and their growth rates of financial capital productivity by 16.9% compared to non-reorganized firms.

Moreover, one can see that the nearest neighbor estimator and radius estimator show evidence of a positive and significant impact of reorganization on the growth rate of turnover. Finally, the matching results of the impact of reorganization on the growth rate of labor productivity of firms will showcase a positive and significant link only in the nearest neighbor methods.

To sum up, the results seem consistent with the proposed hypotheses. They demonstrate that the reorganizations induce positive effects on the rate of the added value growth, the rate of turnover growth, the growth rate of labor productivity and the growth rate of the productivity of financial capital. The results are also consistent with theoretical propositions that maintain that companies which set up organizational innovations improve performance. Thus, these results converge with the work observed on French data[2 , 18].

9 Conclusion

The aim of this study is to assess the implementation of organizational innovations on economic performance based on 2.067 French companies in the manufacturing industry. To this reason, both the econometric matching methodology as inspired by the causal model of Rubin (1974) and the estimation methods recently described in this framework have been employed. These methods offer new nonparametric estimation techniques developed by a number of econometricians who have sought to estimate the impact of reorganizations in differentiating reorganized companies and not reorganized companies and to provide efficient and consistent estimators, taking into account the selection bias. In this research, and in light of the estimators in use (namely the naive estimation, estimation by the nearest neighbor (M = 1), the caliper method (h = 0.01) and the kernel method), one can easily notice that reorganizations improved added value, turnover, labor productivity and capital financial productivity of companies.

As in all such studies, this paper presents some limitations. The first can be attributed to the choice of performance indicators. In light of the results, it transpires that the performance of companies has been limited to a single dimension, the economic dimension. However, since the 1990s, the concept of performance has become multidisciplinary [22]. Therefore, it would be wise to test the organization-performance relationship while taking into account other levels of analysis.

The second limitation is related to the origin of the data in use, which covered only the year 2006. This study suffers from the limited time interval in which the relation between the implementation of organizational innovation and performance is tested. A growing number of authors believe that the cross- sectional studies are less able to offer a dynamic vision and provide less informative data, more variability and less collinearity between variables than their longitudinal counterparts [19].

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