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AbstractThe relationships between different types of innovation have been analyzed from two opposing perspectives. On the one hand, the distinctive view assumes that the determi-nants of each type of innovation are different and therefore each type of innovation con-tributes differently to the performance of the company. On the other hand, the integrative view considers that the different types of innovation are interdependent and that their simultaneous implementation generates a synergistic effect on firm performance. Using data from Spain belonging to the Technological Innovation Panel (PITEC) for the years 2008, 2009, 2010, and 2011, we determine which of the two approaches is predominant. To perform the hypothesis test, we use the so-called complementarity approach, and for the interpretation of those tests, we use the exploration–exploitation approach. We find out that only one of the six non-trivial restrictions tested is complementary. Therefore, these results confirm the predominance of the distinctive view, which is the predominant stream in the field of innovation literature.

Keywords: Innovation, complementarity, exploration, exploitation.

JEL codes: O32.

Esic Market Economics and Business JournalVol. 46, Issue 3, September-December 2015, 9-31

Complementarity between different types of innovation: an oasis in

the middle of the desert

Manuel Guisado-González* and José Luis Coca Pérez

University of Extremadura

* Corresponding author. Email: [email protected]

ISSN 0212-1867 / e-ISSN 1989-3558© ESIC Editorial, ESIC Business & Marketing SchoolDOI: 10.7200/esicm.152.0463.1ihttp://www.esic.edu/esicmarket

10 Manuel Guisado-González and José Luis Coca Pérez

1. Introduction

The innovation process is increasingly dominated by the improvements in the various branches of technology. It is easy to verify that the technological develop-ment has followed an exponential progression from earlier times, especially after the revolution brought about by information technologies in the 1980s. Such an increase in the speed of change requires companies to adjust to this change and even lead it if they want to remain viable in a competitive environment, that is, one that is global and subject to permanent stress.

However, without prejudice concerning what is stated above, it is not appropriate to reduce the concept of innovation to its technological aspect. In fact, the literature that initially focused on this slope has been showing a growing interest in analysing non-technological innovations (Mol and Birkinshaw, 2009; Polder et al., 2010). In line with this interest, the second edition of the Oslo Manual (OECD, 1997) picked up the concept of non-technological innovation for the first time. In turn, surveys such as the CIS (Community Innovation Survey) have been gradually introducing questions related to such types of innovation, contributing in this way to the reali-zation of studies in this field.

The distinction between technological and non-technological innovations allows us to distinguish between innovations related to the primary activities of the organization and those that have an administrative character (Damanpour, 1991). Within the first group, we distinguish between product and process innovations, understanding as product innovation those products or services that are introduced to meet the needs of consumers and as process innovation those new elements introduced into the production process or the operation of a service (Damanpour and Gopalakrishnan, 2001). On the other hand, in the group of non-technological innovations, we differentiate between marketing and organizational innovations. We understand that organizational innovation involves the implementation of new organizational methods in business practices, in the organization of work, and/or in external relations, while innovation in marketing involves the implementation of new marketing strategies that differ significantly from those that the company has previously employed (OECD, 2005).

When studying these different types of innovation, the specialized literature has treated them for a long time as if they were compartmentalized departments, consid-ering that even the different types of technological innovation (product and process innovation) are influenced by different variables (Baldwin et al., 2002; Fritsch and Meschede, 2001) and produce a different impact on the innovative performance of the company (Damanpour et al., 1989). Such attitudes, reluctant to accept any possibility of an interrelationship between different types of innovation, especially when such interrelation takes place between technological and non-technological innovations, have been considered as representatives of the so-called distinctive view. However, the assumptions defended by this stream of knowledge have begun to be questioned by some authors, who, championing the so-called integrative view, have

Complementarity between different types of innovation: an oasis in the middle… 11

argued that each type of innovation can only be understood in terms of its interde-pendence with other types (Damanpour and Gopalakrishnam, 2001). In this sense, a growing number of studies highlight the potential benefits of carrying out several types of innovation simultaneously (e.g. Battisti and Stoneman, 2010; Damanpour et al., 2009).

This relationship of complementarity between different types of innovation is the focus of our investigation. We understand as complementarity the synergistic effect that occurs between two strategies, so that the adoption of one strategy increases the marginal return of the other (Milgrom and Roberts, 1990). The model developed by Milgrom and Roberts (1990) takes as a reference the mathematical model created by Topkis (1978), which developed the lattice theory, within which it is possible to formalize precisely the conditions of complementarity between groups of variables.

Our interest lies in investigating the possible relationship of complementarity among product innovation, process innovation, and non-technological innovation (a variable that groups organizational and marketing innovation) in the context of the Spanish manufacturing sector. If they are complementary, the integrative view could receive further support. In the opposite case, that is, if there were substitutability or no relationship between the different types of innovation, the distinctive view would be reaffirmed. By considering non-technological innovation, we aim to mitigate, as much as possible, the pro-technology bias that is so prevalent in this kind of study (Edgerton, 1999).

However, the interest of our analysis is not restricted to the above area; it is also our intention to address in the present study two of the most common problems that affect most of the literature on the subject. The first problem lies in the fact that most of the empirical works in this area have used cross-sectional data, which makes them dependent on the familiar problems of unobserved heterogeneity. To avoid them, our analysis makes use of panel data.

The second problem occurs in the assumption of immediacy that most research-ers have given to investment in innovation, because most of the empirical studies establish a direct relationship between the investment in innovation made in a giv-en year and the performance that the company has earned in the same year. This assumption can be criticized, because such investments require at least a period of two years to show their true results (Bessler and Bittelmeyer, 2008). That is why in this paper we analyze the two possibilities, using alternatively current innovation variables and variables lagged two years.

In what follows, the structure of this paper is organised as follows: in section two, we briefly present the relations between different kinds of innovation, both from the distinctive and integrative perspective. In section three, we present the relations between complementarity-substitutability and exploration-exploitation. In section four, the data, variables and methodology; and in section five, the results and their discussion. Finally, in section six we present the conclusions.


2. Relations between different kinds of innovation

a) Distinctive view

The distinctive view rests on the principles of analytical thinking, assuming that the understanding of a phenomenon can be achieved through understanding the behaviour of the different parts into which this phenomenon is divided (Ackoff, 1999). The result of this view is a course of action that is present in multiple analyses on innovation. In them, we see that different types of innovation (such as product and process innovation) are studied as if they were phenomena that contribute dif-ferently to the competitiveness of companies and to their growth and assuming that the determinants of each type of innovation are also different (Damanpour, 2010).

The studies that make up the so-called distinctive view have addressed the study of the determinants of each type of innovation and its corresponding impact on firm performance separately. There are numerous examples of this at the level of product innovation (e.g., Li and Atuahene-Gima, 2001), process innovation (e.g., Baer and Frese, 2003), organizational innovation (e.g., Sapprasert, 2010), and marketing innovation (e.g., Moreira et al., 2012).

The principles advocated by the distinctive view also rely on contributions from other theories, among which stands the product life-cycle theory (Abernathy and Utterback, 1978). This theory considers that the market for products undergoes a life cycle during which the nature of innovation is predictable (Utterback, 1978). This means that during the first years in the evolution of an industry, a group of com-panies struggle to obtain a market position and, consequently, this will be a period dominated by product innovation. Subsequently, a dominant design will emerge and will become common for most companies in the industry, and the standardization of such a design will foster the preponderance of process innovation. Finally, there will come a time when the possibilities for improving the process will be very limited, making possible the advent of product innovations to fight to replace the established standard, so the process described above will recommence. Therefore, this theory considers product and process innovation as elements that must take place alterna-tively and does not contemplate an interrelationship between them.

At other times, the prevalence of the distinctive view has been attributed to the so-called supply-side orientation (Damanpour, 2010), as this orientation emphasizes the need to provide different resources to the different forms adopted by technolog-ical change.

b) Integrative view

Although, in general, the specialized literature has followed the principles of the distinctive view, there are some authors who have considered this stream as errone-ous, attributing to it the instability of the empirical results observed when analyzing


the contribution of different kinds of innovation to firm performance (Wolfe, 1994). A new trend, known as the integrative view, has arisen because of such criticism.

This view, far from considering the different types of innovation as separate elements, presumes complementarity between them (Damanpour, 2010). Such an assumption highlights the enormous potential of performing different types of inno-vation simultaneously (Pisano and Wheelright, 1995), because the aforementioned interdependence can result in gaining a sustainable competitive advantage over time.

With regard to its empirical analysis, the relationship between different types of innovation has been scrutinized in two ways: comparing the behaviour of the deter-minants of each type of innovation through the causal relationships between them (e.g. Fritsch and Meschede, 2001; Gunday et al., 2011), and studying their coex-istence through the correlation coefficients (e.g. Damanpour and Gopalakrishnan, 2001; Gunday et al., 2011).

Nevertheless, all the above methods are only an approximation when it comes to revealing whether there is a relationship between different types of innovation. However, the advent of complementarity analysis, prompted by the seminal work of Milgrom and Roberts (1990), can overcome the limitations of the previous meth-ods and therefore scrutinize the relations between different types of innovation in a better way. In recent years, some studies have tried to analyze the interrelationships between different innovation strategies through the use of this method (e.g. Percival and Cozzarin, 2008).

Moreover, besides the different methods used for the analysis of such interrela-tionships, it is also necessary to know the theoretical basis on which the integrative view settles. In this respect, we must state that this view relies on a plurality of the-oretical approaches, among which we emphasize the resource-based view, the new concepts of the product life-cycle theory, and market orientation.

The resource-based view conceives each company as a unique set of resources developed throughout its history (Wernerfelt, 1984; Peteraf, 1993) and, at the same time, as an organization that is able to generate new resources that are properly adapted to the demands of its environment (Teece et al., 1997). On this basis, this view believes that building competitive advantages for the company comes from a unique correct combination of its heterogeneous resources (Wernerfelt, 1984; Peteraf, 1993). Furthermore, such a combination should lead to a synergistic effect between them; that is, their combined effect is greater than the expected effect of the sum of its parts (Athey and Stern, 1998). The popularization of this approach has stimulated the interest of researchers in the analysis of the complementary assets (e.g., Adegbesan, 2009) to try to unravel the keys to the generation of competitive advantages. We note that this theory is in line with the principles of the integrative view, since that view indicates that the construction of a competitive advantage depends on the comple-mentarity between the different types of innovation that are implemented.

On the other side, although we mentioned above the product life-cycle theory as the conceptual basis of the distinctive view, the increasing technological acceleration is substantially modifying the previously described alternation between product


innovation and process innovation, an issue on which the integrative view is based. The original vision of the product life-cycle theory, previously used to justify the distinctive view, is becoming less plausible. This is because the speed of the techno-logical change and global competition drastically limit the duration of a dominant design, so the simultaneous implementation of product innovation and process inno-vation is crucial to the survival of firms (Pisano and Wheelwright, 1995).

Finally, market orientation also means another theory of reference for those who support the integrative view. This market orientation is constructed in opposition to the supply-side orientation and implies that in companies there is a vocation to deliver superior value continuously (Narver and Slater, 1990). To accomplish this premise, product and process innovations must be necessarily interdependent; otherwise, it would not be possible to meet the needs of the client continuously (Bhoovaraghavan et al., 1996).

3. Complementarity-substitutability, and exploration-exploitation

The existence of a relationship of substitutability constitutes the antithetical and hidden face of complementary relationships. On this issue, Tushman and O’Reilly (1997) indicate that the excessive tension between exploration and exploitation can undermine the performance of the firm.

The potentially substitutionary nature of the two activities comes from the fact that the exploration of new ideas and products reduces the rate of improvement of existing competencies (March, 1991). Obviously, improvements of existing com-petencies make experimentation less attractive (Levitt and March, 1988). Overall, the revenues associated with exploration are more variable and their achievement is related to the long term, while the revenues associated with exploitation are sub-ject to less uncertainty and materialize in the short term. He and Wong (2004), in relation to product and process innovation, found this kind of divergent behaviour among firms specializing in exploration. Companies that specialize in exploration and exclude exploitation incur costs associated with experimentation without gain-ing much profit. In addition, companies that specialize in exploitation and exclude exploration compromise their survival (March, 1991).

Therefore, the simultaneous implementation of exploration and exploitation may cause the company to achieve superior performance than the sum of their separate implementations. In that case, we could say that these activities are complementary. Conversely, it can happen that the performance does not vary or even falls, so these activities will be substitutive or will not have any relationship, respectively. In gener-al, only a few combinations in the continuum of exploration–exploitation are opti-mal, so we expect that the interaction between activities will be mostly substitutive. In fact, few companies obtain extraordinary returns.

In general, the exploration is related to novelty, since it refers to the generation of new ideas and products, while exploitation is related to the refinement of the


activities that the company already carries out, to gain efficiency (Tiemessen et al., 1997). That is why some authors have indicated that innovation is the visible result of exploration and exploitation (e.g., Jansen et al., 2006).

However, since innovation is the visible result of exploration and exploitation, it is feasible to perceive a certain degree of partnership among the three technologies analyzed in this study and the exploration–exploitation dichotomy. Thus, product innovation (novelty) will be the result of exploration activities, and process innova-tion (efficiency) will be the result of exploitation activities. Concerning non-techno-logical innovations, the association is not as clear, since those innovations depend on two vectors: on one side, the organizational innovations that are largely related to efficiency and, consequently, to exploitation activities; on the other, marketing innovations that are related to novelty, either in products or in markets, and, con-sequently, to exploration activities. The final influence of non-technological innova-tions depends on which of the two vectors is predominant in each company.

Accordingly, given that few combinations of exploration and exploitation are complementary, we expect few complementary relationships between product inno-vation, process innovation, and non-technological innovation.

4. Data, variables and methodology

The data used in this study come from the Technological Innovation Panel (PITEC), managed by the Spanish National Statistics Institute (INE). PITEC is a firm-level panel database on the innovative activities of Spanish firms based on Community Innovation Survey data (CIS).

We based the construction of the panel data database that we use in our study on the PITEC databases for the years 2008, 2009, 2010, and 2011. The number of com-panies surveyed in these databases is 12813, 12817, 12821, and 12828, respectively. From these databases, we selected manufacturing companies, because our study focuses on this kind of business. After removing observations with missing values and those that had some sort of impact on the variables of interest, we obtained a database with 4880 observations for each of the years under analysis and 19520 observations for the whole data database. Our panel data are strongly balanced, that is, all observations are represented in all the time periods.

To perform the test of complementarity proposed in the seminal study by Mil-grom and Roberts (1990), it was necessary to define a function of firm performance. The variables most commonly used to measure performance are productivity, sales, exports, and the percentage of total sales from new products, among others (Bessler and Bittelmeyer, 2008). In the strict field of innovation, the two variables most com-monly used to measure performance are labor productivity (e.g., Roper et al., 2008) and the percentage of total sales from new products (e.g., Cassiman and Veugelers, 2006). In this study, we used as the dependent variable the natural logarithm of labor productivity, since our goal was to test the complementarity of product innovation,


process innovation, and non-technological innovation. Therefore, we used a broad measure of performance that reflected the influences of many different sources that generate innovation productivity. From a strictly conceptual perspective, the percent-age of total sales from new products does not collect the direct impact of process innovation and non-technological innovation.

Regarding the independent variables representing the three types of innovation analyzed, the PITEC asked companies if during the period of analysis they conducted product innovations and process innovations (0 no, 1 yes). In relation to non-tech-nological innovations, the PITEC posed three questions related to organizational innovation and four related to innovation in marketing (0 no, 1 yes). If the answer to at least one of the seven questions was affirmative, we considered that the company made no technological innovation (Mol and Birkinshaw, 2009). When estimating the coefficients of the regression model, these three forms of innovation were enlarged to eight possible combinations, of which each combination represented exclusive-ly the interaction of the three analyzed innovations (product innovation, process innovation, non-technological innovation). For example, (1, 1, 0) represented that only product innovation and process innovation were present. Thus, using the cor-responding regression coefficients, we estimated the contributions of combinations of innovations to the labor productivity.

Besides the mentioned combinations of innovations, we introduced into the model different control variables to explore the different sources of innovation and the obstacles to their development. In this regard, we introduced size, as the eco-nomic literature indicates that this variable has a large influence on the innovative outcome (Cohen, 1995) and productivity of firms, because the larger the firm is, the greater its exploitation of economies of scale and scope (Jovanovic, 1982). Fur-thermore, we introduced export intensity into the model, as companies operating in international markets are subject to greater competitive tension and enjoy greater market power. Both properties have a significant influence on the productivity of firms (Bernard and Jensen, 1999). We also considered the R&D intensity, since this is a proxy for the absorptive capacity of firms (Cohen and Levinthal, 1990), which is essential for successfully integrating technology and knowledge generated outside the walls of the company. In this sense, there is abundant economic litera-ture that relates RD intensity to innovation and hence to the productivity of firms (e.g., Hervas-Oliver et al., 2011). Moreover, the literature on innovation indicates that companies that cooperate with other organizations are more likely to innovate (Ahuja, 2000) and those that are part of a group exhibit better innovative perfor-mance (MacGarvie, 2006). That is why we incorporated into our model variables and group cooperation.

The literature on innovation indicates that the means of legal protection of inno-vations are an important factor that affects the innovative performance of firms and their ability to capture potential revenues generated by innovations (Veugelers and Cassiman, 1999). Consequently, we incorporated this variable, since the productivity of enterprises is influenced by these legal forms of protection.


In general, the economic literature suggests that sources of knowledge are pos-itively related to innovation activities (Damanpour et al., 2009). Therefore, we incorporated into the model the following variables – internal sources and industrial and scientific external sources – to determine their contribution to the labor produc-tivity of firms. Furthermore, we incorporated a number of variables that measure the different obstacles that companies face when they try to engage in innovative activities, which obviously influence the corresponding performance (Cassiman and Vegeulers, 2006).

Finally, as innovation systems may differ between industries, it is necessary to control these possible asymmetries, so we included industry dummies at the two-dig-it industry classification level.

A precise definition of how the variables were constructed can be found in Table 1.

Table 1. Variable definitions and descriptive statistics

Variable name Variable constructionSample mean

(standard dev.)

Labor productivity(dependent variable)

The log of sales per employee. 5.1966(0.3597)

Product innovation

The firm introduced a new product (0, 1). 0.6000(0.4858)

Process innovation The firm introduced a new process (0, 1). 0.6050(0.4840)

Non-technologicalinnovation

The company made or modified at least one of the following practices or methods: work organization, division of respon-sibilities, external relations, product design, product promo-tion, sales channels, or prices (0, 1).

0.5049(0.4992)

RD intensity The relationship between internal and external R&D expenditures and the total sales of the firm.

0.0430(0.3176)

Legal protection The sum of the scores of the following methods for protect-ing inventions or innovations (1 (used) and 0 (not used)): patents; registration of design; trademarks; copyright. Res-caled between 0 (not used) and 1 (high).

0.0988(0.1902)

Internal sources The importance of innovation inside the company or the group to the innovation process (between 0 (not used) and 3 (high)). Rescaled between 0 (not used) and 1 (high).

0.6285(0.4176)

Industrial external sources

The sum of the scores about the importance of the following information sources to the innovation process. The sourc-es are related to the industry (between 0 (not used) and 3 (high)): suppliers; clients; competitors; fairs and exhibitions; journals; and professional associations. Rescaled between 0 (not used) and 1 (high).

0.3384(0.2758)


Variable name Variable constructionSample mean

(standard dev.)

Scientific external sources

The sum of the scores about the importance of the following information sources to the innovation process. The sources are related to the scientific field (between 0 (not used) and 3 (high)): commercial laboratories; universities; public research centres; and technological centres. Rescaled between 0 (not used) and 1 (high).

0.2209(0.2593)

Cost obstacles This is a measure of the importance of costs as an obstacle to the innovation process (between 0 (not relevant) and 3 (high)). Rescaled between 0 (not relevant) and 1 (high).

0.6271(0.3537)

Financial obstacles The sum of the scores about the importance of the following obstacles to the innovation process (between 0 (not relevant) and 3 (high)): lack of funds within the company or group and lack of external funding. Rescaled between 0 (not rele-vant) and 1 (high).

0.6105(0.3380)

Knowledge obstacles

The sum of the scores about the importance of the following obstacles to the innovation process (between 0 (not relevant) and 3 (high)): lack of qualified personnel; lack of informa-tion on technology; lack of information on the market; and difficulty in finding cooperation partners. Rescaled between 0 (not relevant) and 1 (high).

0.4040(0.2584)

Market obstacles The sum of the scores about the importance of the following obstacles to the innovation process (between 0 (not relevant) and 3 (high)): market dominated by established enterprises and uncertain demand for innovative goods or services. Res-caled between 0 (not relevant) and 1 (high).

0.5331(0.3125)

Group The firm belongs to a group (0, 1). 0.3969(0.4892)

Cooperation The firm cooperates with other enterprises or institutions (0, 1).

0.2743(0.4461)

Export intensity The export share in the total firm sales. 0.2748(0.3161)

Size The log of the number of employees. 1.7306(0.6078)

Industry Dummies for: food, beverages, and snuff, textiles, clothing, leather and footwear, wood and cork, cardboard and paper, printing, chemicals, pharmaceuticals, rubber and plastics, minerals, metallurgy, metal manufacturing, electronic and optical computer products, electrical equipment, other machinery, motor vehicles, ship building, aircraft construc-tion, other transportation equipment, furniture, other manu-facturing activities, and repair of machinery (0, 1).


It is beyond the scope of this study to present the mathematical formalization of supermodular functions of the lattice theory. However, following Milgrom and Roberts (1990), we can present their most important corollary in relation to the evaluation of the complementarity of three business policies (P1, P2, P3). Assume that each policy can be executed by the company (Pi = 1) or not performed (Pi = 0) and i€{1,2,3}. The function H (P1, P2, P3) is supermodular, and P1 and P2 are comple-mentary policies only if:

H110 + H000 - H100 - H010 > 0 (in absence of non-technological innovation)H111 + H001 - H101 - H011 > 0 (in presence of non-technological innovation)

The two inequalities indicate that the implementation of a policy while the other policy is being executed has a greater incremental impact on the performance of the company than the execution of this policy in isolation.

According to Topkis (1978), if there are k variables, the number of non-trivial inequalities to be tested will be 2k–2 ∑k–1 i. In our particular case, since there are three variables to consider, the number of restrictions to be tested will be six.

Most of the literature on innovation that has tested the complementarities between different forms of innovation or between different innovation strategies has used cross-sectional data (e.g., Cassiman and Veugelers, 2006). Far fewer studies have used panel data (e.g., Martínez-Ros and Labeaga, 2009). However, Miravete and Pernias (2006) stressed that the complementarity between product innovation and process innovation endorsed in many studies is largely due to the presence of unobserved heterogeneity. Therefore, given that cross-sectional analysis does not allow us to overcome the problems of unobserved heterogeneity, we inclined towards the use of panel data, which allowed us to avoid it.

The econometric technique that we used to estimate the coefficients is maxi-mum-likelihood random effects. This technique allowed us to obtain the coefficients of all the innovation profiles (which are strictly necessary to test the existence of complementarity) to the extent that the output of the regression provided a constant that could be removed to avoid the perfect multicollinearity caused by the presence in the model of all of the dummies representing the eight possible combinations of the three modes of innovation tested (product innovation, process innovation, and non-technological innovation). Furthermore, this econometric technique had the added advantage of providing estimations of all the coefficients, even in the event that there were time-invariant regressors.

Another problem that we tried to overcome in this study is referred to as the alleged delay in the influence of technological innovations on the productivity gains of firms. In general, as we noted above, most of the studies on the complementarity of different types of innovation have used cross-sectional data. This involves the implicit assumption that the effect of innovation on firm performance is immediate. However, common sense tells us that, in most cases, the effect of innovations on the productivity of firms tends to appear a few years later (Bessler and Bittelmeyer,

i=1


2008): newly planted trees do not bear fruit immediately. In this regard, Belderbos et al. (2004) and Bloom and Van Reenen (2002) noted that the impact of innovation on firm productivity occurs with a certain lag. The use of panel data helped us par-tially to overcome this problem, because this kind of econometric analysis considers multiple years (in our study, four years). However, it seemed desirable to go a little further. Therefore, in this study, we considered two different alternatives to compare the results. In the first, all the variables used in the econometric estimations belonged to the years 2008, 2009, 2010, and 2011; in the second, all the variables belonged to the four years mentioned above, with the exception of the dummy variables rep-resenting the different combinations of innovation types analyzed, which belonged only to the years 2008 and 2009. Thus, we could analyze the impact of innovations on the productivity of the firms two years later.1 Therefore, when testing the poten-tial complementarity between the three types of innovation analyzed, we were able to determine whether the use of lagged variables yielded different results and wheth-er they were more diverse than those obtained using variables that were not lagged.

5. Results and discussion

With regard to the descriptive statistics, Table 1 shows the mean and standard deviation of the variables used in this study. Accordingly, we can see that product and process innovations are developed by a similar number of companies, 60% and 60.5%, respectively, while the percentage of companies that develop non-technolog-ical innovations is somewhat lower, reaching 50.49%.

Regarding the combination of these forms of innovation, Table 2 gives the fre-quency distribution for each of the eight sections of innovation considered. The results are similar in relation to the presence and absence of lagged variables. In this regard, we note that the simultaneous use of all the types of innovation is the choice for most companies (32.44% in the absence of lagged variables and 34.78% with lagged variables), followed by the option not to develop any type of innovation (19.84% and 16.72%, respectively) and the option to develop product and process innovations simultaneously in the absence of non-technological innovation (12.16% and 12.82%, respectively). The other options do not exceed 10% frequency in any case, the development of non-technological innovation without any other type of innovation being the least-used option (4.36% and 3.89%, respectively).

(1) Our desire would be to consider product innovation, process innovation, and non-technolog-ical innovation in the years 2006, 2007, 2008, and 2009, to delay by two years all the innovation variables of the panel data. However, the PITEC did not provide information on non-technological innovation in 2006 and 2007.


Table 2. Distribution of innovation profiles

(Product, process, non-technolog.)

Frequencies (%)(2008, 2009, 2010, 2011)

Frequencies (%)(2008, 2009)

(0, 0, 0) 19.84 16.72

(1, 0, 0) 9.05 8.89

(0, 1, 0) 8.44 8.52

(0, 0, 1) 4.36 3.89

(1, 1, 0) 12.16 12.82

(1, 0, 1) 6.34 6.64

(0, 1, 1) 7.33 7.73

(1, 1, 1) 32.44 34.78

Returning to Table 1, it is noteworthy that of the different sources of technolog-ical information that feed companies, the domestic actions are the ones that receive more credit (0.6285), while the external sources, whether of an industrial (0.3384) or a scientific origin (0.2209), fail to achieve such recognition. In turn, the differ-ent types of obstacles that companies face do not receive the same valuation, those relating to cost (0.6271) and those of a financial nature (0.6105) being the ones that generate the greatest concern, followed by market barriers (0.5331) and those obsta-cles relating to the scope of knowledge (0.4040). On the other hand, the percentage of entities belonging to the group rises to 39.69%, while only 27.43% of companies are involved in cooperation with other enterprises or institutions.

The results of the regressions of labor productivity are shown in Table 3. In this table, we differentiate the regression that does not use lagged innovation variables from the one that does. The first conclusion that we can draw is that, irrespective of the inclusion or not of lagged variables, all the possible combinations of innovation types show a positive and significant impact on labor productivity. The greatest influence corresponds to the combination of the three types of innovation simulta-neously (1, 1, 1).


Table 3. Productivity regressions: dependent variable labor productivity

All the variables with data from 2008, 2009, 2010,

and 2011

The variables of innovation profiles with data from 2008 and 2009. The remaining variables with data from

2008, 2009, 2010, and 2011

Coef. S.E. Coef. S.E.

RD intensity -0.07204*** 0.00403 -0.310302*** 0.01775

Legal protection 0.02022** 0.00930 0.03124** 0.01466

Internal sources 0.00878* 0.00511 0.02460*** 0.00759

External sources: industrial 0.01141 0.00897 0.01904 0.01377

External sources: science 0.01561* 0.00875 0,03371** 0.01352

Cost obstacles 0.00022 0.00565 -0.01018 0.00887

Financial obstacles -0.03728*** 0.00644 -0.03916*** 0.00993

Knowledge obstacles 0.00571 0.00777 0.00514 0.01197

Market obstacles 0.00791 0.00610 0.01589* 0.00928

Group 0.09924*** 0.00636 0.14141*** 0.00884

Cooperation 0.00812** 0.00402 0.00832 0.00629

Export intensity 0.02207*** 0.00610 0.07767*** 0.00947

Size -0.02760*** 0.00706 0.03280*** 0.00818

(0, 0, 0) 5.35198*** 0.01777 5.16351*** 0.01933

(1, 0, 0) 5.37626*** 0.018409 5.21723*** 0.02082

(0, 1, 0) 5.36878*** 0.018273 5.19475*** 0.02032

(0, 0, 1) 5.36005*** 0.018782 5.18067*** 0.02164

(1, 1, 0) 5.37762*** 0.018322 5.19772*** 0.02020

(1, 0, 1) 5.37816*** 0.018803 5.18087*** 0.02137

(0, 1, 1) 5.36601*** 0.018569 5.18588*** 0.02050

(1, 1, 1) 5.38162*** 0.018362 5.20917*** 0.02000

Year 2009 -0.05869*** 0.00268 - -

Year 2010 -0.03789*** 0.00272 - -

Year 2011 -0.02178*** 0.00281 0.01106*** 0.00243

Industry dummies Included Included

Model Wald chi2(46) = 1.46e + 06p-value = 0.0000

Wald chi2(44) = 1.45e + 06p-value = 0.0000

Statistical significance of the coefficients: at 1% ***, 5% **, and 10% *.


Regarding the other variables, the coefficients are also quite similar, but with some caveats. When the model does not use lagged innovation variables, the cooper-ation has a positive and significant impact on labor productivity, but this significance disappears with the use of lagged variables. The opposite case occurs when we refer to market barriers, as the coefficient has a positive and significant value when the model uses lagged innovation variables and it is not significant when those variables are not employed. Moreover, the behavior of the variable size is not uniform because its coefficient is significant in both models but its influence is positive in the model that uses lagged variables and negative in the model in which there are no lagged variables. In addition, we must emphasize that the industrial external sources, cost obstacles, and knowledge obstacles have no significant influence on labor productiv-ity in either of the two models analyzed.

Finally, we emphasize that we introduced year dummies into both models because otherwise the secular variation in labor productivity would be attributable only to the variables introduced into the two models. Thus, this allows us to verify that this secular variation has a negative and significant influence on labor productivity in the model that does not use lagged innovation variables and a positive and significant influence on the model in which the variables of innovation are lagged two years. This result is a clear indication that changes in innovation influence labor produc-tivity with a certain lag.

Table 4 shows the test of complementarity/substitutability performed. The first issue to note in relation to the hypothesis tests is that the results of the two models differ substantially. The model that does not use lagged innovation variables results in one relationship of substitutability and five interactions in which there is no evi-dence of a relationship between the pairs of innovation tested.

Table 4. Complementarity tests

Innovation variables not lagged

Innovation variables lagged two years

Chi2 P-value Chi2 P-value

Prod

uct–

proc

ess

Non-technological innovation = 0

T1 = H110 + H000 - H100 - H010 = 0

T2 = H110 + H000 - H100 - H010 ≤ 0

Complements/substitutes/no relation

3.55

Substitutes

0.0595

0.9702

11.58

Substitutes

0.0007

0.9996

Non-technological innovation = 1

T1 = H111 + H001 - H101 - H011 = 0

T2 = H111 + H001 - H101 - H011 ≤ 0


0.06

No relation

0.7998 1.75

No relation

0.1865


Innovation variables not lagged

Innovation variables lagged two years

Chi2 P-value Chi2 P-value

Prod

uct–

non-

tech

nolo

gica

l

Process innovation = 0

T1 = H101 + H000 - H100 - H001 = 0

T2 = H101 + H000 - H100 - H001 ≤ 0


0.42

No relation

0.5151 10.57

Substitutes

0.0011

0.9994

Process innovation = 1

T1 = H111 + H010 - H110 - H011 = 0

T2 = H111 + H010 - H110 - H011 ≤ 0


0.76

No relation

0.3840 2.66

No relation

10.29

Proc

ess–

non-

tech

nolo

gica

l

Product innovation = 0

T1 = H011 + H000 - H010 - H001 = 0

T2 = H011 + H000 - H010 - H001 ≤ 0


1.39

No relation

0.2385 2.75

Substitutes

0.0972

0.9513

Product innovation = 1

T1 = H111 + H100 - H110 - H101 = 0

T2 = H111 + H100 - H110 - H101 ≤ 0


0.07

No relation

0.7908 12.93

Complements

0.0003

0.0001

However, in the model that uses lagged innovation variables, there are three relationships of substitutability, one of complementarity, and two of no relationship. This is a clear indication that the influence of innovation on business productivity occurs with a certain time lag. Therefore, in the following, we will adhere to the results of the model that uses lagged innovation variables in our interpretation of the hypothesis tests.

These tests indicate that product innovation and process innovation maintain a substitutive relationship when companies do not perform non-technological inno-vation and that there is no relationship between them when companies engage in non-technological innovation. Therefore, this result supports the distinctive view, which indicates that the determinants of the two types of innovation are different. Thus, within the framework of the Spanish manufacturing sector and in the context of the time period analyzed, the simultaneous action of product innovation and process innovation does not produce synergies. This is probably due to product innovation, which, as a result of exploration activities, consumes large investments in R&D during the period analyzed. Those investments increase the productivity of the company in the long term, but they reduce it in the short term. This decrease is


not able to compensate for the potential productivity gains generated by the exploita-tion activities associated with process innovation, the implementation of which does not usually necessitate large investments in R&D. Therefore, it is possible that the non-complementarity in the short term between the two kinds of innovation lies in the different needs of R&D expenditure required by each type of innovation. In this respect, some studies show that product innovation is more related to R&D invest-ment than to process innovation (e.g., Hervas-Oliver et al., 2011; Rouvinen, 2002). The lack of synergy detected does not mean that the relationship between the two types of innovation is substitutive per se. In this regard, we have previously pointed out that very few combinations of product innovation (exploration) and process inno-vation (exploitation) generate complementarities. We can guess that the achievement of complementary combinations mainly depends on the evolutionary state of product innovations, as these have a much longer maturity period than process innovations in relation to productivity. Therefore, in this case, the complementary test simply indi-cates that in the analyzed period no complementary combination was reached. In the future, complementarities may arise due to the maturation of the product innovations undertaken and their convenient exploitation through process innovations.

Furthermore, the hypothesis tests reveal that the relationship between product innovation and non-technological innovation is substitutive when process innova-tion is not present and they have no relation when process innovation is present. Non-technological innovation is shaped by organizational innovation (exploitation activity that normally impacts on the profitability and productivity of the company in the short term) and marketing innovation (more related to exploration activities and therefore with a long-term maturation process that affects productivity). Again, we can guess that the innovations that impact positively on productivity in the long term but negatively in the short term have a greater negative impact during the peri-od of analysis than the positive impact that might result from organizational innova-tions. When considering the presence of process innovation, it helps to compensate for the negative impact of product and marketing innovations on productivity in the short term. Therefore, the simultaneous action of product innovation and non-tech-nological innovation changes from substitutive to having no influence on the labor productivity of firms. Again, the hypothesis tests reaffirm the preponderance of the distinctive view versus the integrative view.

Finally, the relationship between process innovation and non-technological innovation changes from substitutive to complementary depending on the pres-ence or absence of product innovation. Companies that do not implement product innovation but develop marketing innovation (which matures in the long term and consequently can decrease productivity in the short term) and process innovation simultaneously can experience a negative impact on their short-term labor produc-tivity. However, when companies also implement product innovation, some market-ing innovations can influence the business sales in the short term, producing a less negative impact on the productivity of the company in the short term. That is why the hypothesis test for this assumption reveals complementarity.


The relations between exploration and exploitation are dynamic, so the achieve-ment of a complementary combination depends largely on the evolutionary status of the exploration activities (mainly product innovation and marketing innovation). It is probable that there are very few successful combinations of exploitation activities at the service of efficiency (process innovation and organizational innovation) and exploration activities at the service of survival (product innovation and marketing innovation) in the short term. Hence, it is evident that the substitutive relationship between types of innovation should not be reviled per se, because some of these combinations can become complementary relationships in the long term; to reach the oasis it is necessary to pass through many deserts.

In any case, from a short-term perspective, the hypothesis tests reveal that, as the relationships of substitution and no relationship are the majority, the distinctive view will prevail over the integrative view.

6. Conclusions

Much of the empirical literature that has analyzed the relationship among dif-ferent types of innovation has done so using cross-sectional data. However, there are some studies indicating that many of the synergies discovered by the hypothesis tests in this kind of analysis are not consistent, because the estimated coefficients are affected by so-called unobserved heterogeneity. Furthermore, the use of cross-sec-tional data also implies that the influence of innovation on firm performance measures is instantaneous. However, some authors have warned that this influence operates with a lag, so again the estimated coefficients are not entirely consistent.

In this study, we tried to overcome the two drawbacks mentioned. Accordingly, we used panel data, which allowed us to use econometric techniques that bypass the problems of unobserved heterogeneity. Moreover, the use of proxies lagged two years for the different types of innovation analyzed also allowed us to overcome these problems. The comparison of the results and hypothesis tests between the model that uses current innovation variables and the model that uses innovation variables lagged two years confirmed that this latter class of model reflects, in a more accurate and diverse way, the influence of different types of innovation on the labor productivity of firms.

In addition, the six complementarity tests performed between the different pairs of innovation analyzed (product innovation, process innovation, and non-techno-logical innovation) revealed that during the period of the analysis the substitution relationships were majoritarian and only the relationship between process innova-tion and non-technological innovation, with the presence of product innovation, was complementary. These results support, from a short-term perspective, the distinctive view against the position of the integrative view.

Furthermore, in this study, we combined the so-called complementary approach with the exploration–exploitation approach. From this combination, we deduced


that we cannot expect complementarity in most relationships between different types of innovation. However, we can expect that as exploratory innovations mature, some of the relationships of substitutability can become complementary. Therefore, we are of the opinion that we should not make mechanistic interpretations of this kind of test. Before recommending the implementation of public innovation policies to try to prevent the simultaneous promotion of two kinds of innovation, the test for which showed that there are alternatives, it seems necessary to analyze in depth the current maturation phase of the innovations related to exploration activities. Mechanically rejecting the simultaneous development of certain types of innovation because short-term tests reveal that the relationship between the two types of inno-vation is substitutive may jeopardize the achievement of future complementarities and therefore the future survival of the company.

References

Abernathy, W.J. and Utterback, J.M., 1978, “Patterns of industrial innovation”. Technology Review, Vol. 80, Issue7, pp. 40-47.

Ackoff, R.L., 1999, Re-creating the corporation: A design of organizations for the 21st century. New York: Oxford University press.

Adegbesan, J.A., 2009, “On the origins of competitive advantage: Strategic factor markets and heterogeneous resource complementarity”. Academy of Manage-ment Review, Vol. 34, Issue 3, pp. 463-475.

Ahuja, G., 2000, “Collaboration networks, structural holes and innovation: a longi-tudinal study”. Administrative Science Quarterly, Vol. 45, Issue 3, pp. 425-455.

Athey, S. and Stern, S., 1998, An empirical framework for testing theories about complementarity in organizational design. Cambridge, MA: National Bureau of Economic Research.

Baer, M. and Frese, M., 2003, “Innovation is not enough: Climates for initiative and psychological safety, process innovations, and firm performance”. Journal of Organizational Behavior, Vol. 24, Issue1, pp. 45-68.

Baldwin, J., Hanel, P. and Sabourin, D., 2002, “Determinants of innovative activity in Canadian manufacturing firms”. In Kleinknecht, A. and Mohnen, P. (eds.), Innovation and Firm Performance. Palgrave, pp. 86-111.

Battisti, G. and Stoneman, P., 2010, “How innovative are UK firms? Evidence from the fourth UK Community Innovation Survey on synergies between technological and organizational innovations”. British Journal of Management, Vol. 21, Issue 1, pp. 187-206.

Belderbos, R., Carree, M. and Lokshin, B., 2004, “Cooperative R&D and firm per-formance”. Research Policy, Vol. 33, Issue 10, pp. 1477-1492.

Bernard, A.B. and Jensen, B.J., 1999, “Exceptional exporter performance: Cause, effect, or both?” Journal of International Economics, Vol. 47, Issue 1, pp. 1-25.


Bessler, W. and Bittelmeyer, C., 2008, “Patents and the performance of technology firms: Evidence from initial public offerings in Germany”. Financial Markets and Portfolio Management, Vol. 22, Issue 4, pp. 323-356.

Bhoovaraghavan, S., Vasudevan, A. and Chandran, R., 1996, “Resolving the process vs. product innovation dilemma: A consumer choice theoretic approach”. Man-agement Science, Vol. 42, Issue 2, pp. 232-246.

Bloom, N. and Van Reenen, J., 2002, “Patents, Real Options and Firm Perfor-mance”. The Economic Journal. Vol. 112, Issue 478, pp. C97-C116.

Cassiman, B. and Veugelers, R., 2006. “In search of complementarity in innovation strategy: Internal R&D and external knowledge acquisition”. Management Sci-ence, Vol. 52, Issue 1, pp. 68-82.

Cohen, W., 1995, “Empirical Studies of Innovative Activity”. In Stoneman, P. (ed.), Handbook of the Economics of Innovation and Technological Change. Blackwell.

Cohen, W.M. and Levinthal, D.A., 1990, “Absorptive capacity: a new perspective on learning and innovation”. Administrative Science Quarterly, Vol. 35, Issue 1, pp. 128-152.

Damanpour, F., 1991, “Organizational innovation: A meta-analysis of effects of determinants and moderators”. Academy of Management Journal, Vol. 34, Issue 3, pp. 555-590.

Damanpour, F., 2010, “An integration of research findings of effects of firm size and market competition on product and process innovations”. British Journal of Management, Vol. 21, Issue 4, pp. 996-1010.

Damanpour, F. and Gopalakrishnan, S., 2001, “The dynamics of the adoption of product and process innovations in organizations”. Journal of Management Stud-ies, Vol. 38, Issue 1, pp. 45-65.

Damanpour, F., Szabat, K.A. and Evan, W.M., 1989, “The relationship between types of innovation and organizational performance”. Journal of Management Studies, Vol. 26, Issue 6, pp. 587-602.

Damanpour, F., Walker, R.M. and Avellaneda, C.N., 2009, “Combinative effects of innovation types and organizational performance: A longitudinal study of service organizations”. Journal of Management Studies, Vol. 46, Issue 4, pp. 650-675.

Edgerton, D., 1999, From innovation to use: Ten eclectic theses on the historiogra-phy of technology”. History and Technology, Vol. 16, Issue 2, pp. 111-136.

Fritsch, M. and Meschede, M., 2001, “Product innovation, process innovation, and size”. Review of Industrial Organization, Vol. 19, 335-350.

Gunday, G., Ulusoy, G., Kilic, K. and Alpkan, L., 2011, “Effects of innovation types on firm performance”. International Journal of Production Economics, Vol. 133, Issue 2, pp. 662-676.

He, Z.L. and Wong, P.K., 2004, “Exploration vs. exploitation: An empirical test of the ambidexterity hypothesis”. Organization Science, Vol. 15, Issue 4, pp. 481-494.


Hervas-Oliver J.L., Albors Garrigos J. and Gil-Pechuan I., 2011, “Making sense of innovation by R&D and non-R&D innovators in low technology contexts: A forgotten lesson for policymakers”. Technovation, Vol. 31, Issue 9, pp. 427-446.

Jansen, J.J.P., Van den Bosch, F.A.J. and Volberda, H.W., 2006, “Exploratory inno-vation, exploitative innovation, and performance: Effects of organizational ante-cedents and environmental moderators”. Management Science, Vol. 52, Issue 11, pp. 1661-1674.

Jovanovic, B., 1982, “Selection and the Evolution of Industry”. Econometrica, Vol. 50, Issue 3, pp. 649-670.

Levitt, B. and March, J.G., 1988, “Organizational learning”. Annual Review of Sociology, Vol. 14, pp. 319-340.

Li, H. and Atuahene-Gima, K., 2001, “Product innovation strategy and the perfor-mance of new technology ventures in China”. Academy of Management Journal, Vol. 44, Issue 6, pp. 1123-1134.

MacGarvie, M., 2006, “Do Firms Learn from International Trade?” Review of Eco-nomics and Statistics, Vol. 88, Issue 1, pp. 46-60.

March, J.G., 1991, “Exploration and exploitation in organizational learning”. Organization Science, Vol. 2, Issue 1, pp. 71-87.

Martínez-Ros, E. and Labeaga, J., 2009, “Product and process innovation: Persis-tence and complementarities”. European Management Review, Vol. 6, Issue 1, pp. 64-75.

Milgrom, P. and Roberts, J., 1990, “The economics of modern manufacturing: Technology, strategy, and organization”. American Economic Review, Vol. 80, pp. 511-528.

Miravete, E. and Pernias, J., 2006, “Innovation complementarity and scale of pro-duction”. Journal of Industrial Economics, Vol. 54, Issue 1, pp. 1-29.

Mol, M.J. and Birkinshaw, J., 2009, “The sources of management innovation: When firms introduce new management practices”. Journal of Business Research, Vol. 62, Issue 12, pp. 1269-1280.

Moreira, J., Silva, M.J., Simões, J. and Sousa, G., 2012, “Drivers of marketing innovation in portuguese firms”. Amfiteatru Economic Journal, Vol. 14, Issue 31, pp. 195-206.

Narver, J.C. and Slater, S.F., 1990, “The effect of a market orientation on business profitability”. Journal of Marketing, Vol. 54, Issue 4, pp. 20-34.

OECD, 1997, Proposed Guidelines for Collecting and Interpreting Technological Innovation Data. Paris: OECD.

OECD, 2005, Guidelines for collecting and interpreting innovation data. Paris: OECD.

Percival, J.C. and Cozzarin, B.P., 2008, “Complementarities affecting the returns to innovation”. Industry & Innovation, Vol. 15, Issue 4, pp. 371-392.

Peteraf, M.A., 1993, “The cornerstones of competitive advantage: A resource-based view”. Strategic Management Journal, Vol. 14, Issue 3, pp. 179-191.


Pisano, G.P. and Wheelwright, S.C., 1995, “The new logic of high-tech R&D”. Har-vard Business Review, Vol. 73, pp. 93-105.

Polder, M., Van Leeuwen, G., Mohnen, P. and Raymond, W., 2010, Product, process and organizational innovation: drivers, complementarity and productivity effects. MPRA Paper 23719, University Library of Munich, Germany.

Roper, S., Du, J. and Love, J., 2008, “Modelling the innovation value chain”. Research Policy, Vol. 37, Issue 6-7, pp. 961-977.

Rouvinen P., 2002, “Characteristics of product and process innovators: Some evi-dence from the finnish innovation survey”. Applied Economics Letters, Vol. 9, Issue 9, pp. 575-580.

Sapprasert, K., 2010, On factors explaining organisational innovation and its effects. Working Paper No. 20080601. Retrieved from Centre for Technology, Innovation and Culture, University of Oslo.

Teece, D.J., Pisano, G. and Shuen, A., 1997, “Dynamic capabilities and strategic management”. Strategic Management Journal, Vol. 18, Issue 7, pp. 509-533.

Tiemessen, I., Lane, H.W., Crossan, M. and Inkpen, A.C., 1997, “Knowledge man-agement in international joint ventures”. In Beamish, P.W. and Killing, J.P. (eds.), Cooperative strategies: North American perspective. New Lexington Press, pp. 370-399.

Topkis, D.M., 1978, “Minimizing a submodular function on a lattice”. Operations Research, Vol. 26, Issue 2, pp. 305-321.

Tushman, M. and O’Reilly, C., 1997, Winning through innovation: A practical guide to leading organizational change and renewal. Boston: Harvard Business School Press.

Utterback, J.M., 1978, The Dynamics of product and process innovation in indus-try. Cambridge, MA: Center for Policy Alternatives, Massachusetts Institute of Technology.

Veugelers, R. and Cassiman, B., 1999, “Make and buy in innovation strategies: Evidence from Belgian manufacturing firms”. Research Policy, Vol. 28, Issue 1, pp. 63-80.

Wernerfelt, B., 1984, “A resource-based view of the firm”. Strategic Management Journal, Vol. 5, Issue 2, pp. 171-180.

Wolfe, R.A., 1994, “Organizational innovation: Review, critique and suggested research directions”. Journal of Management Studies, Vol. 31, Issue 3, pp. 405-431.


Notes on Contributors

Name: Manuel Guisado-GonzálezPosition: Profesor ayudanteSchool / Faculty: Facultad de Estudios Empresariales y TurismoUniversity: University of ExtremaduraAddress: Avda. de la Universidad, s/n. 10071 Cáceres. SpainTelephone: 927257480 ext 57917Email: [email protected]

Name: José Luís Coca PérezPosition: Profesor Titular de UniversidadSchool / Faculty: Facultad de Estudios Empresariales y TurismoUniversity: University of ExtremaduraAddress: Avda. de la Universidad, s/n. 10071 Cáceres. SpainTelephone: 927257480 ext 57922Email: [email protected]

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