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Typological Analysis of Non-technological and Technological Innovation Determinants in Tunisia

Mallek Wafa

Unit of Economic Development Reaserch, University of Sfax

Abstract: This paper examines on the one hand the proximity effects on ICT firm’s innovation, on the other hand it verify the similarity of technological and non technological innovation determinants. Thus, we recall the theoretical background of proximity effect on Co-localized firm’s innovation from both geographical economy and neo-institutionalism work. The results of a logistic regression, performed by a collected data from 58 Co located ICT firms within Elgazala technology Park in Tunis, shows that firm concentration, socio-economic, organizational and cognitive factors exert a favorable effect on technological and non-technological innovation. However, empirical results confirm that innovation determinants differ depending on whether it is technological or non-technological.

Keywords: Proximity, ICT, Technological Innovation, Non-technological innovation, logistic binary regression, Tunisia

Received March 02, 2015; Revised April 12, 2015; Accepted April 20, 2015

Citation: Mallek Wafa, Typological Analysis of Non-technological and Technological Innovation Determinants in Tunisia. Journal of Behavioural Economics Finance Entrepreneurship Accounting and Transport, 3 (2). 71-75. January 2015. doi:10.12691/jbe-3-2-2

1. Introduction

The modern economy is characterized by perpetual change and profound social-economic, political, technological and institutional mutations. Currently, growth paradigm is essentially based on intellectual capital accumulation, where using and producing new knowledge are essential for firms endurance (Schiliro, 2012). This explains in fact, the central role of innovation in productive activities.

Proponents of geographical innovation and neo-institutionalism consider that innovation process is not explained only by technological and financial factors, but also, by socio-economic, cognitive, geographical and organizational factors. Indeed, they recommend mobilizing public policies under industrial agglomeration form, to offer companies the proximities advantages on innovation (Pecqueur, 2008). This is why, Asheim and Gertler (2005) argue that: “we cannot properly understand innovation if we do not appreciate the central role of proximity and concentration in the process”.

Currently, industrial agglomeration policies is mobilized in developed and developing countries, reflecting the return of industrial policy and the strong decision to involve industry in stimulating innovation process (Plunket and Torre, 2009). In fact, several studies have been carried out to study agglomeration effect on firm’s innovation essentially in developed country. From us, we ask how much the proximity between firms operating in the same sector in Tunisia, is able to stimulate their innovation? And how much determinants of their technological and non technological innovation are similar?

To answer these questions, we need to remember first, the theoretical basis of proximity effects on Co-located firms innovation. Then we go through an econometric exercise to verify the relevance of this theoretical contribution in the case of Tunisia and to compare the determinants of technological and non technological innovation in Co located firms within a technology Park.

2. Proximity and Innovation: Some Theoretical References

When we interest at innovation’s proximity impact, we cannot ignore the work of Porter (1990; 1998; 2003). This author advances that cooperation between cluster’s members is an efficient way to stimulate their technological and non-technological innovation. Therefore, he considers collective and cooperative project within industrial agglomeration as a successful stimulator for local synergies and moderator of innovation’s costs and risks. Furthermore, Porter (2003) believes that local competition between Co located actors has a positive pressure on their innovation performance. Regarding to internationalization’s effects on innovation, Porter (2004) thinks that formal or informal relation between agglomerated firms with foreign innovative actors, due to their international visibility, a relevant determinant for their innovation process.

As regards Marshall (1920) and the New Economic Geography propenents, knowledge spillovers, the uncontrolled knowledge’s leakage, available and free for Co localized firms, are an important source for innovation (Bahlmann and Huysmans, 2008). Marshall (1920) considers that turnover and face to face innovator actors in industrial agglomeration as the main forms of knowledge spillovers. Feldman and Florida (1994) advocate that technological infrastructure compsed by universities research, technological centre activities and research laboratory actions set near to Co localized firms is the main form of knowledge spillovers that help firms to innovate.

Finally, undern neo-institutionalist theory’s thinking, particularly the Knowledge based view of cluster, interactive learning and social anchorage of actors, are mains determinants of innovation in Co located firms (Bahlmann and Huysmans, 2008). This theory consider knowledge as a strategic resources that require intensified learning process and a confident social climate to exchange and absorb them (Arikan, 2009). From this, Bathelt et al. (2004) advocate that: Innovation and learning are better understood when they are considered as the result of interactive processes in which actors with different types of knowledge and skills come together and exchange information to solve some technical, organizational, commercial and intellectuals problem.

3. Dataset and Model

The data used in our study were collected from a survey, developed and conducted by ourselves, between November 2013 and Mars 2014 with 58 ICT’s firms Co located in Elgazala Technology Park in Tunis. In order to recognize factors explaining the ICT Co located firms innovation, we will perform a bi varied logistic regression data. This regression is the most fited technique in our case because the depended variables are qualitative’s nature, which take the value 1 if the phenomenon is observed, 0 otherwise (Appendix : Table 2).

The model is expressed as following:

Л (X) is a probability, its value would be between 0 and 1. Either logit function defined by:

If we apply the logit function in Л (X), it will be:

The margin of variation of g ((Л (X)) is either positive or negative, while the range of Л(X) varies between 0 and 1, a regression may so be implemented. In the case where numerous explanatory variables (X₁, X₂, …, Xn) are integrated, the regression model is then written as follows:

As regards the binary nature of explained variables, the analysis of the residuals predicted values and the concept of heteroscedasticity don’t make a sense (Harrell, 2001). Nevertheless, we always interest in the fit’s model quality, for this; we employ the likelihood ratio tests to estimate model’s parameters, the Wald test of coefficients significance to analysis the weight of each factor separately and Pseudo R squared McFadden to evaluate the overall model quality. The selection of explanatory variables, which were the subject of a principal component analysis (PCA) exception the following variables: "Internationalization", "Local Competition" and "Foreign competition" (Appendix Table 4) is conducted by "Backward stepwise" method. Consequently, we introduce all explanatory variables (mentioned in Appendix, Table 3) in the primary model and only significant variable which are less correlated there between and most correlated with the dependent variable, will be retained in the last model (Bourmont, 2012).

4. Results and discussion

The logistic regression’s resultats of technological and non-technological innovation determinants in ICT Co located firms within Elgazala technology Park are presented in the following table:

Table 1. Technological and non-technological determinants of ICT Co located firms in Elgazala Technology Park

Technological innovations in Co located ICT firms within Elgazala technology Park

[picture] | (1)

[picture] | (2)

Non-technological innovations in Co located ICT firms within Elgazala technology Park

[picture] | (3)

[picture] | (4)

4.1. Selected Variables in the Final Models

The four models generated by logistic regression "Backward stepwise" take account only of significant predictors to a maximum threshold of 10%. When we observe the innovation’s determinants in Co located firms within Elgazala technology Park, we conclud that:

Skilled workers explain the technological (product and process) and non-technological (marketing and organizational) innovation. This phenomenon is explained by the important number of skilled engineer and perpetual professional training engaged by ICT firms in Tunisia. This result agree with previous finding by Rahmouni, Ayadi and Yildizoglu (2007); Tlili and Chkir (2011) by performing an econometric analysis based on 529 Tunisian industrial companies. They consider that skilled employees are important factors for stimulating technological innovation in Tunisian industrial firms.

R&D explain only technological innovation in Co located ICT firms in Tunisia. This finding join those proposed by El Elj (2009) and Tlil and Chkir (2011) in their econometric analysis of technological innovation determinants based on 529 Tunisian companies. Nevertheless, non technological innovation in ICT firms doesn’t require R&D expenditure to be performed.

The industrial atmosphere exerts a positive effect on technological innovation (process) and non-technological innovation (organizational) in Co located firms within Elgazala technology Park. This result confirms the conclusions of Araszkiewiez, Lacroi and Rasse (2008) and econometric contributions of Markoff (1999), who consider industrial atmosphere as a fundamental source of Co located ICT companie’s innovation in USA. In fact, knowledge spillovers, by industrial atmosphere component, is relevant for innovation within a sectoral agglomeration structure in Tunis.

Interactive learning and partnerships exert a favorable effect only on technological innovation in Co located ICT firms within Elgazala technology Park. In contrast, internationalization explain only non technological innovation in Co located ICT firms in Tunisia. Thus, cognitive and organizational proximities have a positive and different effect on innovation type. Indeed, this tow types of proximities are important in explaining the innovation behavior among firms co-localized in Tunisia. This finding verify those advanced by Rallet and Torre (2001) Boshma (2005) and Hamdouch and Depret (2009).

Local competition in Elgazala technology park push Co located firms to make a technological innovation (product and process) and a non-technological innovation (Marketing). These results agree with empirical results advanced by Feldman and Audretsch (1999) and conclusions founded by Porter (1990) that show how local competition between micro processor companies in Silicon Valley can accelerate their innovation process.

4.2 Excluded Variables from the Final Models

When we would highlight the variables excluded by the Backward stepwise regression, we notice the rejection of the following variables:

The role of the State: its rejection by the Backward stepwise method means that there is no government intevention’s effect on ICT firm’s innovation. The Tunisian financing system of innovation is fruitless because public incentives are fixed below investment levels from which private investors can pay back the cost of their research. This result is consistent with the finding reached by most empirical studies on innovation determinants in developing countries especially in Tunisia, namely Gabsi, Mhenni and Koubaa (2008); Rahmouni and Yildizoglu (2011); Elj and Abdessalem (2011).

External knowledge’s acquisition: its rejection reflect uselessness of technological acquisition in the improvement of products, production processes, marketing and organizational strategies in ICT firms in Tunisia. This result is similar to the results founded by Gabsi, Mhenni and Koubaa (2008).

The technology infrastructure: the rejection of this variable by the backward regression show the low contribution of technological infrastructure’s knowledge spillovers on ICT firms innovation. This in fact show the divergence between basic research and industrial research. We conclude then, that knowledge dissemination by scientific publications, intellectual services, strategic and technology reports from universities, research laboratories, technology centers and innovative government agencies dont help ICT firms in Tunisia to conduct technological and non-technological innovations. This conclusion fit with those made by Breschi and Lissoni (2001), who consider the knowledge production by the technology infrastructure Co located among industries, does not necessarily serve firms to produce innovation.

The informal cooperation and social anchorage, which actually reflect the social proximity between ICT firms in Tunisia, are rejected by the Backward regression. This means that ICT firms do not develop socially embedded relations through seminars, symposiums and entrepreneurs clubs to fulfil an innovative project… this result contrast thos advanced by Bocquet, Brion and Mothe (2013) that show the importance of social proximity to boost the friendly relations between the actors needed for interactive learning and innovation.

The foreign competition doesn’t serve ICT firms in Tunisia to perform nor technological neither non technological innovation. This conclusion converge to those finded by Karray and Kriaa (2009); Ayadi, Rahmouni and Yildizoglu (2007) who advanced that firms opened to the foreign market are in general a subcontractor which don’t have the total autonomy to conduct innovation.

5. Conclusion

From binomial logistic regression of Co located ICT firm’s data, we deduce the following conclusions: skilled workors, R&D, local competition, partnership, internationlization, interactive learning and industrial atmosphere are significant determinants for innovation. However, these determinants differ depending on whether the type of innovation is technological or non technological. Moreover, we conclude that, organizational and cognitive factors, through partnership, interactive learning and internationalization, are so important for stimulating both technological and non-technological innovation in ICT conglomerated firms in Tunisia. In contrast, social factors through social anchorage and informal coopertaion seems to be non effective factors for innovation in ICT firms in Tunisia. To this end, we conclude that industrial agglomeration strategies play an important role in stimulating innovation in the era of knowledge economy. However, local role of the state is needed to further consolidate the extra spatial determinants of innovation.

Appendix

Table 2. Explained variables for ICT firms Co located in technology Park

Table 3. The explanatory variables

Table 4. Explanatory Variables PCA of 58 Co located firms in the technology Park

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