4 Open innovation profiles in Italian manufacturing companies
Abstract
In recent years, researchers have investigated the new paradigm of open innovation (OI), analysing various aspects, such as the determinants, changes in the innovation process, impact on performance, and barriers to the development of this approach. Nevertheless, the vast majority of empirical analyses have been focused on small and medium enterprises (SMEs).
This chapter's objective is to identify and characterize different profiles of openness towards the external environment, and it has been conducted on Italian manufacturing firms, a context which is highly characterized by SMEs. A survey was carried out and a database of 96 manufacturing companies was obtained. A K-means cluster analysis and a univariate analysis of variance (ANOVA) were conducted to identify different profiles of OI and verify the significance levels of differences between clusters.
Initial results suggest that two clusters with different levels of openness can be found, in which group membership is significantly influenced by OI, employees' innovation capability, collaborative management practices, aggressive technology strategy, information and communications technology (ICT) adoption, employee development and, by context, variables such as company size and performance.
Introduction
OI is a phenomenon that has been verified and studied by managerial literature in recent years. The adoption of this strategic paradigm not just by high-tech enterprises but also by those operating in less innovative sectors; and not just large enterprises but also SMEs have attracted particular attention from academics.
The idea that an enterprise can sustain a processes of innovation development in collaboration with other subjects – and therefore must be open to the flow of knowledge, competences deriving from the world ‘outside itself’ – is refuted by the analyses of the best performances attained by open enterprises. Studies demonstrate that enterprises adopting an OI strategy attain best performances in terms of: capacity to innovate (Ahuja, 2000), innovative level of products/services (Laursen and Salter, 2006; Dahlander and Gann, 2007; Lichtenthaler, 2008a), improvement of basic competences (Gassmann and Enkel, 2004) and reduction of development costs and time-to-market of new products/processes (Kolk and Püümann, 2008), though there are conflicting opinions (Knudsen and Mortensen, 2011) and increase of sales volumes and market acceptance of new products (Lichtenthaler, 2008a, 2009; Lazzarotti et al., 2010).
On the other hand, OI is neither a precise and clear strategy nor a monolithic and indistinct phenomenon. OI has many facets and different typologies of representation. Openness towards the external environment can be emphasized to a greater or lesser extent (depth, breadth, integration, and variety), expressed during different phases of the innovative process (Grönlund et al., 2010) with different organizational forms (outsourcing, alliances, licensing, etc.) in various combinations of actors, roles and strength of connections (Lee et al., 2010).
Lastly, the factors that promote or hinder OI strategy will be different. Such factors have been studied for large firms (Lichtenthaler and Ernst, 2009; Gassmann and Enkel, 2004) and some have been tested also on SMEs (Van de Vrande et al., 2009), while studies on other factors are still pending.
The literature on OI in SMEs is relatively recent (Van de Vrande et al., 2009; Lee et al., 2010), and one of the questions still open is the extent to which OI is embedded in SMEs (Lee et al., 2010) and what the differences are within the same SMEs, regarding the level of openness adopted and the factors influencing their open strategy (Van de Vrande et al., 2009).
The present study examines Italian manufacturing firms (a context which is highly characterized by SMEs) to identify and characterize different profiles of openness towards the external environment. The choices of OI strategy will be investigated in terms of contextual and firm-specific factors influencing open innovation.
An online questionnaire survey was carried out, and a database of 96 manufacturing companies was obtained. A K-means cluster analysis and a univariate ANOVA have been conducted to identify different OI profiles and verify the significance levels of differences between clusters.
The next section contains a summary of the literature on OI in SMEs. The following section offers analysis of contextual and firm-specific factors useful for characterizing firms' different modes of behaviour in the OI adoption process. Then we illustrate the methodology adopted, explain the results obtained, and, in the final section, the conclusions of this work are presented.
Open innovation in small and medium enterprises
Chesbrough (2003) defined the OI strategy as ‘the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively’.
In OI processes, organizational boundaries are more porous and firms interact strongly with external actors (Lichtenthaler, 2009). The opening of corporate borders can be measured in different ways. One of these involves the consideration of the number and type of partners involved and the number of the innovation process's phases opened (Laursen and Salter, 2004, 2006; Pisano and Verganti, 2008; Lazzarotti et al., 2010; Enkel et al., 2009; Elmquist et al., 2009; Dahlander and Gann, 2007; Lazzarotti and Manzini, 2009; Keupp and Gassmann, 2009; Gassmann and Enkel, 2004; Chesbrough, 2003). Firms also may open up their innovation processes in two different phases: the phase of acquiring knowledge and technology for the development of innovation (outside-in or technology exploration), and the phase of marketing the innovation itself (inside-out or technology exploitation) (March, 1991; Rothaermel and Deeds, 2004).
Enterprises' modes of conduct in the management of an OI strategy differ depending on whether the enterprises are large or SMEs (Lee et al., 2010). One element of differentiation lies in the choice of what activities are open to collaboration.
For example, while large firms focus collaboration efforts during the technology exploration, small firms principally focus on OI practices in the commercialization phase of the technology. This is precisely because, if they are good in inventions, they often lack the capacity (in terms of marketing channels, manufacturing facilities, and network contacts) to introduce innovations successfully in the market (Narula, 2004; Lee et al., 2010).
There are many ways in which organizations can collaborate. There are, in fact, various combinations of actors, with different roles and strength of their ties. Regarding the type of connections preferred by SMEs in the exploration phase, technology purchasing appears to be most practiced with respect to strategic alliances (Lee et al., 2010). In addition, SMEs tend to use external partners in technology purchasing to avoid knowledge dispersion and stay focused on a specific technology area (Narula, 2004). In particular, they tend to prefer universities and research institutes for strategic alliances because there is no danger of technology exposure (Tidd and Trewhella, 1997). In the exploitation phase, SMEs try to succeed in the market by entering into supplier-customer relations with large firms (Luukkonen, 2005), outsourcing agreements or strategic alliances with other SMEs (Edwards et al., 2005), or with multi-firm networks (Lee et al., 2010).
The main motivations for the adoption of OI by SMEs are: keeping up with market developments and meeting customer demand, developing products more quickly and effectively, incorporating technologies and new knowledge in current products, and improving the innovation process and the corporate brand reputation. In addition, the desire to address a lack of internal skills and gain from complementary resources in order to spread the risk and costs drives the search for external partners (Van der Vrande et al., 2009; Verbano and Venturini, 2012).
Lastly, a firm can encounter impediments in the management of the innovation activity. Keupp and Gassmann (2009) highlight that a firm inclined to resist changes, and therefore unlikely to accept new competences and the knowledge necessary for innovation, and incapable of managing the risks of innovation, will apply OI activities precisely to overcome such internal impediments. Other impediments are: lack of financial resources, costs of innovation and time needed, lack of competent personnel and difficulties to finding them in the labour market, market uncertainty and insufficient market information, lack of technological knowledge, imitation possibilities of technology innovations, disappointment with the quality of partners, and others (Lee et al., 2010: Van de Vrande et al., 2009).
Determinants of open innovation
The factors (or variables) that influence the OI strategy could be split into contextual and firm-specific ones. As illustrated in Figure 4.1, the latter one comprises: firm size, employees' innovation capability, personnel training, inimitably of firm's capability, technology strategy and collaboration management. The contextual variables are those correlated to the external environment of the firm (thus, not dependent on the choices of the individual enterprise) as technology intensity of industry and ICT adoption. The management literature has analysed, in particular, the link between OI and the type of industry in which the firm operates and the market penetration of information technologies.
Type of industry
SMEs can be distinguished, primarily, according to whether they operate in manufacturing or services sectors. Although one might expect that service firms are less likely to adopt a strategy of OI, because they do not engage in formal activities of
R&D, some studies show that there are no differences and that OI is relevant even for small service firms (Van de Vrande et al., 2009).
Second, the industry can be classified based on its technological intensity measured by the ratio between R&D expenditure and the value added (Eurostat, 2009). The firm's belonging to a high-tech sector does not appear to be a variable that influences the openness of innovative processes (Lichtenthaler and Ernst, 2009; Lazzarotti and Manzini, 2009). The adoption of an ‘open strategy’ is now increasing also in low-tech sectors (Gassmann et al., 2010), thanks to the mobility of managers and availability of the intermediary organizations and others to help firms to experiment with this new model (West and Lakhani, 2008).
ICT adoption
Schilling (2010) highlights how information technologies (IT) allow the costs of searching for partners, as well as those related to monitoring the performance of the actors involved in the innovative process, to be reduced. In addition, Tidd and Bessant (2009) underline how ICTs facilitate connections, not just between firms in an area but also between national innovation systems. In addition, their study indicated how they help the formation of networks, in which firms can communicate with technological and research institutes, universities, clients and other organizations in order to meet their own knowledge needs and access a vast quantity of resources. In this way, firms reduce the distances between actors in the supply chain and, therefore, facilitate the passage from closed to OI (Gassmann, 2006; Chesbrough, 2003; Schilling, 2010; Tidd and Bessant, 2009; Dodgson et al., 2006; Hrastinski et al., 2010). Although ICT adoption can be useful to create systems and architectures to share and transfer knowledge in the collaborations, by ensuring a better balance between the risks and benefits of technological transfer (Chiaroni et al., 2010; Parida, 2009; Lichtenthaler, 2008b; Chesbrough et al., 2006), the use of such instruments may not be simple, particularly if employees lack the necessary competences to make the new systems and processes work (Dodgson et al., 2006). In any case Hrastinski et al. (2010) underline that firms can favour OI, even when they are implemented in the most simple forms, in addition to the fact that the use of ICT is now an essential element in the implementation of new innovative processes, defined by Nobellius (2004) as sixth generation R&D.
Firm size
The first firm-specific factor influencing the OI strategy is its size. Some studies showed that larger firms acquire more from outside sources than do small firms (Tidd and Bessant, 2009; Herstad et al., 2008; Lichtenthaler and Ernst, 2009). This depends on whether the complexity of the technological knowledge involved in their products prompts them to seek different sources of knowledge (Veugelers and Cassiman, 2006), or whether they have the necessary resources to open organizational units committed to the search for partners (Rothaermel and Deeds, 2006). A conflicting opinion comes from Gassmann and Enkel (2004), who claim that, nowadays, firm size is decreasing in relevance as a factor. Some authors (Van de Vrande et al., 2009; Keupp and Gassman, 2007; Herstad et al., 2008; Tidd and Bessant, 2009; Schilling, 2010; Verbano and Venturini, 2012; Lazzarotti et al., 2010) have demonstrated how the OI phenomenon is also spreading among SMEs, despite the limitations that these may have compared to large firms. In particular, SMEs appear to have contributed substantially to the recent trend towards outbound OI in order to overcome the problem of lacking assets that are complementary to those possessed within the firm (Arora et al., 2001; Bröring and Herzog, 2008; Teece, 1998). Often, therefore, SMEs can be forced into the adoption of OI to overcome difficulties on account of their reduced size (Lichtenthaler, 2008a; Lichtenthaler and Ernst, 2009; Chesbrough, 2003; Parida, 2009), even though, according to Gassman et al. (2010), in spite of their smallness and lack of resources, which are acknowledged liabilities, they still implement OI far less than multinationals do.
Employees' innovation capability and personnel development
To implement an OI approach, it is essential to create a culture that values external knowledge and competences and that helps the new values of the firm to be understood. In an open perspective, firms cannot have a limited ‘absorption’ capacity (Cohen and Levinthal, 1990), continue to maintain a ‘not-invented-here’ syndrome (Katz and Allen, 1982), or strengthen their learning level by maintaining a ‘do-it-yourself’ attitude (Gassmann, 2006).
Inimitably of firms' capability
Inimitability is the firms' protection of their resources, capabilities and strategies, so that competitors cannot easily replicate them. Some authors (Chesbrough et al., 2006; Schilling, 2010; Laursen and Salter, 2006) have highlighted how the necessity of possessing resources which are difficult to imitate is important in the adoption of OI activities. Tidd and Bessant (2009) and Leonard-Barton (1995), however, underline the risks of excessive focus on internal knowledge and competences, while others affirm that a certain level of protection of intellectual property in the OI model is required (Mazzoleni and Nelson, 1998; Arora et al., 2001; Sousa, 2008; Herstad et al., 2008).
Reed et al. (2012) show that when a firm adopts an OI strategy the economic rents from property rights disappear, those from economies of scale and capital requirements are reduced, but those from experience-curve effects, differentiation, distribution, and switching costs remain. Similarly, rents from the difficult-to-imitate resources of networks and reputation remain intact, and while those from employee knowhow and culture remain, they are likely to be in reduced amount.
Technology strategy
The technology strategy adopted by the firm, and, in particular, technological aggressiveness, can influence OI. Firms defined as being technologically aggressive probably tend to focus on internal development, rather than resorting to the acquisition of new external technologies (Freeman, 1974; Miles and Snow, 1978; Brockhoff and Pearson, 1992). Lichtenthaler and Ernst (2009) show that technological aggressiveness does not influence the acquisition of technology from the outside. This variable appears to influence the external exploitation of technology (Lichtenthaler and Ernst, 2009). The work by Lazzarotti et al. (2010) demonstrates that, considering different dimensions (breadth and integration) of OI, technological aggressiveness influences the openness of innovative processes to a greater number of partners and in a greater number of phases. Their analysis finds support from other authors, according to whom collaborations can become important if the advantages of external commercialization are considered, such as the fixing of industrial standards, entry into new markets, possible learning effects (Arora et al., 2001; Lichtenthaler, 2005), the possibility of having close links with clients and access to new technologies, with the objective of possessing technological leadership; all common aspects of firms pursuing an aggressive strategy (Tidd and Bessant, 2009; Schilling, 2010). In the literature, the debate on the influence of technological aggressiveness on OI still remains open and, according to Lichtenthaler and Ernst (2009), two different categories of technologically aggressive innovators can be found: ‘aggressive proprietary innovators’, which focus on developing new technologies in-house and commercializing them in their own products, according to the closed approach; and ‘aggressive open innovators’, which strongly follow the new OI paradigm.
Collaboration management practices
To implement an OI approach, it is important also to realize some aspects, such as incentive systems and information management; communication platforms; decision criteria for projects; and mechanisms of selection, integration, and control of the partners involved (Schilling, 2010). Moreover, a firm should understand which sources of innovation to use, selecting those which are more suited to its development and having the right cognitive distance, considering the competences and knowledge held within the firm (Dahlander and Gann, 2007; Sousa, 2008; Gassmann and Enkel, 2010).
Objectives and methodology
Research questions
The analysis of the literature has highlighted the following gaps: the extent to which OI is embedded in SMEs (Lee et al., 2010), and what differences there are within the same SMEs regarding the level of openness adopted and the factors influencing their open strategy (Van de Vrande et al., 2009).
This study focuses on the following dimensions of OI: breadth (measured by the number of partners), integration along the innovation process, and the diversification of sources. As in Italy 95 per cent of firms are SMEs and the OI phenomenon has not been properly studied yet, the study's objective is to identify and characterize different profiles of openness towards the external environment, and it has been conducted on Italian manufacturing firms, i.e. a context which is highly characterized by SMEs.
In particular, the research questions were:
1. What are the characteristics of Italian manufacturing companies with regard to the degree of openness of the innovation process?
2. Can clusters with different degrees of openness of the innovation processes be identified within the sample?
3. What are the internal and firm-specific factors that characterize the firm's membership in a cluster that is more or less open (if clusters can be identified)?
Data collection and sample profile
A survey has been carried out using a web questionnaire organized into the following sections: degree of OI, structural-organizational characteristics of the firm, product-technology strategy, knowledge-competence-learning, external context characteristics and performance. Almost all the items have been measured with seven-point Likert-type scales (1 = strongly disagree; 7 = strongly agree).
From the AIDA Bureau van Dijk database of Italian firms, 2,500 manufacturing firms have been randomly extracted and invited to participate in the research by emails addressed to the director of R&D or to the CEO/entrepreneur, followed by phone calls. During the data collection period (January–December 2010), a database of 96 companies was obtained, with a response rate of 4 per cent.
The companies in the sample used for this analysis are mostly located in northern Italy, and most of them are manufacturers of machinery and equipment (22 per cent) or manufacturers of basic metals and fabricated metal products (except for machinery and equipment) (18 per cent); followed by food and beverage (8 per cent); textiles, leather and clothes (7 per cent); electrical equipment (6 per cent); rubber and plastics (6 per cent); wood and furniture (6 per cent); and computer, electronic and optical products (5 per cent). Other sectors were represented to a lower degree.
Of the companies in the sample, 91 per cent are SMEs: 23 per cent medium, 45 per cent small, and 23 per cent micro companies (according to the 2005 European Commission's definitions). Also, the geographical distribution has been analysed, dividing the firms into four geographical areas: North East (38 per cent), North West (35 per cent), Central Italy (26 per cent), and South and Islands (1 per cent). The average age of the firms is 30 years, and the firm size of the sample is not connected to the presence of start up, as only four firms are less than 10 years old, and only one company is less than five years old.
In order to verify whether the sample was representative of the universe of firms being studied, a Chi-Square test was used by considering the industrial sector because the frequency distributions of the reference universe (AIDA database) were well known for this variable. The test conducted confirms that there were no significant differences between the sample obtained and the overall reference universe of firms (α = 0.001).
Variables and data elaboration
The survey data have been analysed using SPSS 17.0 statistical application software. Descriptive statistics have been applied to answer the first research question.
Second, principal components factor analysis (PCFA) by varimax rotation has been implemented in three steps, in order to reduce the number of variables obtained from the questionnaires (see Table 4.1). For the dependent variables, the factor ‘OI’ has been created, explaining 71.7 per cent of the total variance, and KMO test 0.618. To measure the level of OI, three variables have been used: number of partners (breadth), number of activities or phases open to collaborations (integration or depth), and partner variety (given by the number of different types of partners in the collaborations). This last variable has been calculated counting the number of affirmatives (score 2) obtained from six questions about having collaborated in the last five years with each of the seven types of partner: universities and research centres, service companies supporting innovation, government bodies and agencies, clients, suppliers, competitors, enterprises operating in other sectors.
Six factors emerged from the PCFA conducted considering the independent variables, explaining 71.7 per cent of the total variance, and with KMO test 0.787. They are: employees' innovation capability, collaboration management practices, employee development, aggressive technology strategy, inimitability of firm's capability, and ICT adoption.
Third, the factor ‘performance’ was extracted, including the profitability of sales, the profitability of capital invested, and the overall profitability. This factor explains 89.3 per cent of the total variance, and the KMO test is 0.745; it was extracted separately since it does not constitute a determinant of OI, but rather a control variable.
The variables comprising each factor have high factor loadings (i.e. more than 0.5), thus reflecting high construct validity, while the off-factor loadings for the other variables comprising each factor are low (i.e. < 0.37), reflecting discriminant validity for the variables. The factor analysis was carried out using the pairwise option so as to work on a broader sample of firms (N = 96). Later, this analysis was verified by also using the listwise option and the same factors were obtained.
Then, a K-means cluster analysis was carried out in order to group firms into homogeneous categories with regard to the eight factors previously obtained, and with the intent of identifying different profiles of OI. The K-means analysis was conducted using squared Euclidean distance and SPSS17.0 software.
The adequacy of the resulting clusters was also evaluated using a univariate ANOVA to verify the significance levels of differences among the groups, also referring to context variables.
In order to determine the final number of clusters, we took three criteria into account: (a) the statistical properties in terms of the relationship between within-cluster and between-cluster variance (Caliński and Harabasz, 1974; Duda and Hart, 1973), (b) the plausibility of the clusters identified (‘can the clusters convincingly be interpreted as OI profiles?’), and (c) the number of firms per cluster. Based on these criteria, we arrived at a two-cluster solution that is satisfactory in statistical terms and can be interpreted as different OI profiles, as will be shown in the section on results.
Moreover the solutions so obtained using pairwise options (N = 96) was verified to be coherent according to the results provided by the hierarchical cluster analysis using Ward's methods, listwise options and graphical analysis of the resulting dendrogram.
Finally, a correlation analysis between size, industry technology intensity, and clusters of the companies was performed in order to identify other distinctive characteristics.
Results of the empirical analysis
Overview of the openness of the Italian manufacturing companies
The firms in the sample do not appear to have collaborated with many partners or in many phases of the innovation process, since they show near average values for OI breadth and integration. Nevertheless, all the firms have opened their innovation process to at least one partner, and almost all to at least two partners (95 per cent), firstly favouring suppliers (partners for 96 per cent of firms) and clients (88 per cent), but also firms operating in other sectors (51 per cent), universities, research centres, and companies supporting innovation to the same degree (45 per cent). On average, these firms collaborate with 3.7 different types of partner, using chiefly informal alliances as the form of collaboration (see Table 4.2).
The high correlation between the three variables that measure openness, which has led to the creation of the factor with the same name, suggests that firms adopt an open or closed approach, which is consistent in all three dimensions considered (breadth, integration and partner variety (as already noted by Lazzarotti et al., 2010)). In a continuum between open and closed innovation at overall level, firms appear to position themselves around the average.
The reasons for openness appear to be many: in particular, greater agreement is found in the desire to broaden the firm's competence base, stimulate creativity and the generation of new ideas, and increase flexibility, but also to maximize the commercial exploitation of its own technologies before they become obsolete. Therefore, the reasons are aimed at exploration and exploitation, and this confirms the joint approach adopted (outside-in and inside-out).
Economic/financial questions emerge from among the barriers to OI chiefly reported, in particular with regard to the difficulty to effectively manage collaborations (control of times and costs, assessment of partners' quality, management of opportunistic behaviour); this is associated with the lack of competences to manage collaborations (see Table 4.2).
OI profiles
Two groups emerged from the cluster analysis: half of the firms belong to cluster 1 and the other half belong to cluster 2. The two clusters show, for each grouping of variables, the average values reported in Table 4.3.
Table 4.3 also illustrates the univariate ANOVA. The results show significant differences (α < 0.05) between the two groups in six out of eight variables and significant difference at α = 0.1 for one other variable. The variables that most influence the profile of clusters (in Table 4.3 with α < 0.001) are: OI, collaboration management practices, and performance. Significant to a lesser degree, but still important in differentiating the two clusters (in Table 4.3 with α < 0.056), are employees' innovation capability, employee development, aggressive technology strategy, and ICT adoption. Only the inimitability of firms' capability is not significant in the clustering of the companies analysed.
By using the average value of the values obtained in the variables that comprise each factor, it is possible to obtain the profile of the two clusters illustrated in Figure 4.2.
By considering that the variables measure the degree of agreement on a Likert scale (1 = strongly disagree, 7 = strongly agree), it is revealed that the cluster 1-HIGH OPEN, composed of firms with a medium-high level of openness, also shows a greater use of collaboration management practices and the development of personnel, as well as ICT instruments; moreover, it denotes the presence of personnel with aptitudes towards innovation, a certain technological aggressiveness, and a good improvement in firm performances.
However, the opposite behaviour emerges for the cluster LOW OPEN, which does not use the collaboration management practices much and uses those for the development of personnel and ICT instruments even less; it also denotes personnel's low aptitude to innovation, scarce technological aggressiveness, and no improvement in firm performances.
As far as the inimitability of the company's capabilities is concerned, these are not present very much in either cluster and do not constitute a differentiating variable; this is apparent in Table 4.3.
Lastly, to extend the description of the clusters obtained, two control variables have been considered: firm size, classified according to the number of employees as micro, small, medium and large (OECD, 2005), and the technological intensity of the sector the firm belongs to, classified as high, medium-high, medium-low, and low (Eurostat, 2009).
The results illustrated in Table 4.4 show a significant correlation between the level of openness of the innovative process and firm size (p > 95%), while there is no link between the technological intensity of the sector and the adoption of OI. Therefore, it can be concluded that intensity, integration, and partner variety of OI are greater as firm size increases, though all the firms are interested in the phenomenon analysed. This assessment has been conducted by considering
Note: bold text indicates variables with α < 0.001 and italic text indicates variables with α < 0.056.
the two clusters obtained (high open and low open) as well as the open factor (see Table 4.4).
This analysis is aligned to the results of the previous studies which show a positive influence of firm size on the openness degree of innovation process (Arora et al., 2001; Gassman, 2006; Keupp and Gassman, 2007; Herstad et al., 2008; Tidd and Bessant, 2009; Lichtenthaler and Ernst, 2009; Gassman et al., 2010). As demonstrated in Table 4.4, the small firms belong mostly to the low open cluster; in contrast, the large firms are concentrated in the high open cluster and the rest of the firms are equally distributed in high and low open categories.
The results in Table 4.4 do not indicate a clear connection between technology intensity and OI. Nevertheless, 83 per cent of high-tech firms belong to high open cluster, and the medium-low technology firms belong mainly to the low open cluster, while the other types of firms are not clustered.
The lack of a connection between technology intensity and OI confirms the theories of some authors citied previously (Lichtenthaler and Ernst, 2009; Lazzarotti and Manzini, 2009; Gassmann et al., 2010).
The firms of different geographic areas are equally distributed between the high open and low open clusters (see Table 4.5). The degree of openness seems not influenced by the geographical localization inside Italy.
| Technology intensity | Cl.1 High open | Cl.2 Low open | Total | |
| 4-high tech | Manufacture of computer, electronic and optical products, drugs | 71% | 29% | 100% |
| 3-medium-high tech | Manufacturers of machinery and equipment, electrical equipment, motor vehicles, chemicals and chemical products | 53% | 47% | 100% |
| 2-medium-low tech | Plastics and rubber, manufacturers of basic metals and fabricated metal products (except for machinery and equipment) | 38% | 62% | 100% |
| 1-low tech | Textiles, leather and clothes, food and beverage, wood and furniture, others | 50% | 50% | 100% |
| N/A | 6 | 5 | 11 |
CORRELATION ANALYSIS
–cluster membership and technology intensity: Kendall's rho = !0.0871, Sig. (2-tailed) = 0.4762; Spearman's rho = −0.0940, Sig. (2-tailed) = 0.3861, N = 86.
–open factor and technology intensity: Kendall's rho = 0.0564, Sig. (2-tailed) = 0.5188, Spearman's rho = 0.0831, Sig. (2-tailed) = 0.4436, N = 86.
| Size | Cl.1 High open | Cl.2 Low open | Total | |
| 1-Micro | Employees < 10 | 41% | 59% | 100% |
| 2-Small | 10 ≤ employees <50 | 44% | 56% | 100% |
| 3-Medium | 50 ≤ employees <250 | 55% | 45% | 100% |
| 4-Large | Employees ≤ 250 | 89% | 11% | 100% |
| N/A | 0 | 0 | 0 |
CORRELATION ANALYSIS
–cluster membership and size: Kendall's rho = −0.1968, Sig. (2-tailed) = 0.0928, Spearman's rho = −0.2121, Sig. (2-tailed) = 0.0387, N = 96.
–open factor and size: Kendall's rho = 0.1983, Sig. (2-tailed) = 0.0178, Spearman's rho = 0.2504, Sig. (2-tailed) = 0.0147, N = 96.
Discussion and conclusions
The analysis carried out, responding to a variety of stimuli towards the creation of collaboration models (Global Business Summit, 2010), contributes to the study of the OI phenomenon in Italy, and in its manufacturing sector in particular.
Elements of peculiarity in the empirical analysis conducted concern the OI measure, which includes partner variety, expressed as the number of different types of partners involved, the sample of as yet little investigated manufacturing SMEs, and the geographical area (or, rather, the whole Italian territory) which has not yet been investigated (in the literature some analyses were conducted only in the Lombardy region).
At the overall level, this study appears to highlight that SMEs are also widely interested in the OI phenomenon, but not with high intensity, in terms of breadth, integration, and variety of partners. This is also confirmed by the positive correlation that has emerged between the level of openness and firm size. As also found in large firms, the principal partners in collaborations (which are usually expressed in informal contracts) are suppliers and clients, followed by firms operating in other sectors, universities and research centres, and supporting companies. In line with findings of numerous authors (Verbano and Venturini, 2012; Tidd and Bessant, 2009), organizations collaborate particularly to extend their knowledge base and internal resources, and they are often forced for this reason to resort to OI (Lichtenthaler, 2008a; Lichtenthaler and Ernst, 2009). SMEs, however, are hindered in the OI process owing to a lack of resources, particularly financial resources, and the competences necessary for the management and control of collaborations. This affirmation finds support in many studies that have highlighted SMEs' difficulty in accessing credit, in addition to problems concerning planning and management of collaborations (Verbano and Venturini, 2012; Rothwell and Dodgson, 1993; Van de Vrande et al., 2009; Christensen et al., 2005). However, the other barriers studied in the literature and considered in this study do not appear to particularly hinder the firms studied.
Two well-differentiated clusters emerged from the cluster analysis: high open, with greater breadth, integration and partner variety, and low open (the opposite). Companies belonging to cluster 1 (more open) appear to be characterized by adoption of collaboration management practices, ICT supporting OI, and employee development practices; moreover, high open companies have higher employee innovation capability and more aggressive technology strategy, although they do not have higher inimitable capabilities. As far as internal factors are concerned, more open companies are larger, but do not differ with regard to sector of membership. Finally, we can confirm the literature (MacPherson, 1997; Lichtenthaler, 2009; Lichtenthaler, 2008a) about the positive effects of OI on performance improvement.
The results obtained on contextual and internal factors influencing OI will now be discussed with reference to previous results reported in the literature review (see Table 4.6).
| Factors considered | Results from the literature review | Results obtained by this survey | |
| Contextual factors | Technology intensity of the sector | conflicting results | Factor not influencing OI |
| ICT adoption | Factor influencing OI (qualitative analysis) | Factor characterizing high open cluster | |
| Firm-specific factors | Size | Conflicting results | Factor characterizing high open cluster |
| Higher employees innovation capability | Some variables found to influence OI (qualitative analysis) | New construct characterizing high open cluster | |
| Adoption of employee development practices | Some variables found to influence OI (qualitative analysis) | New construct characterizing high open cluster | |
| Inimitability of firm's capability | Conflicting results (qualitative analysis) | Factor not influencing OI | |
| Aggressive technology strategy | Conflicting results, but in most of the cases influencing OI | Factor characterizing high open cluster | |
| Collaboration management practices adoption | Some variables found to influence OI (qualitative analysis) | New construct characterizing high open cluster | |
| Firm's growing performances | Positively influenced by OI | Factor characterizing high open cluster |
- Regarding the technology intensity of the industry, according to Lichtenthaler and Ernst (2009) and Lazzarotti and Manzini (2009) there is no relationship between this variable and OI adoption, while other studies conclude that increasing the rate of technological, market, and product change should entail greater utilization of OI (Gassmann and Enkel, 2004; Fine, 1998; Lichtenthaler, 2009). In the present empirical analysis, we found no significant influence on OI, adding empirical evidence to this point still under debate.
- Referring to ICT adoption, previous literature converges on the importance of this facilitating technologies in the transition from closed to open innovation (Gassmann, 2006; Chesbrough, 2003; Schilling, 2010; Tidd and Bessant, 2009; Dodgson et al., 2006; Hrastinski and Kviselius, 2010; Lichtenthaler, 2008b: Chesbrough et al., 2006). This chapter empirically confirms that the use of Internet for the selection of partners and to facilitate communication in collaborations, and the adoption of virtual prototype and simulation techniques in the joint development of an innovation, characterizes high open firms.
- In the present results, firms belonging to the high open cluster are significantly bigger in size (even if we consider SMEs). This conclusion adds empirical evidence to the conflicting debate about the relationship between OI and firm size (Tidd and Bessant, 2009; Herstad et al., 2008; Lichtenthaler and Ernst; 2009; Rothaermel and Deeds, 2006; Gassmann and Enkel, 2004; Lazzarotti et al., 2010). We conclude that, also in SMEs, OI is very diffuse, though with a level of intensity that increases with size.
- In addition, the factor ‘employees’ innovation capability' characterizes the cluster of high open firms. This concept includes internal collaborations, for which Roussel et al. (1991) had already underlined the advantages, but also the breadth and versatility of personnel knowledge and its capacity to adapt, which are important in the implementation of OI. In fact, given that change favours only prepared minds (Nobellius, 2004), in order to facilitate the implementation of the new paradigm the establishment of a suitable corporate climate (one able to accept and implement the advantages of the new model) appears necessary (Cohen and Levinthal, 1990; Katz and Allen, 1982; Lichtenthaler, 2008a). It appears, therefore, verified that personnel's aptitude to innovation contributes to the creation of a climate favourable to that which Asakawa et al. (2010) define as the policy for OI.
- Considering personnel development, some authors (Amabile, 1983; Woodman et al., 1993; Garvin, 1993; West and Gallagher, 2006; Schilling, 2010) have highlighted the necessity of preparing recognition mechanisms for innovation activity and resorting to continuous training programmes in order to allow the development of suitable competences supporting OI adoption. In the present analysis, in order to define personnel development, another two variables are included in addition to those already present in the literature (the time and resources allocated to employees for the generation of new ideas, and the assignment of creative and challenging objectives to personnel), also obtaining consistent results regarding the positive influence of this factor.
- Regarding the inimitability of firms' capability, the results of the present analysis are inserted in this, as yet, not well-defined framework. In this analysis, the inimitability of intellectual capital and the high specialization of knowledge and competences are not associated with different behaviour with reference to the level of openness studied.
- In spite of the fact that there are conflicting opinions in the literature regarding technological aggressiveness, the empirical analysis conducted highlights how firms engaged in pursuing the objective of becoming sector leaders (by aggressively acquiring new areas of activity through innovation, attempting to impose their standards, using new technologies, and placing emphasis on radical innovation) tend towards open innovative processes, taking advantage of the benefits of OI. However, these results confirm those obtained by Lazzarotti et al. (2010), with reference to a sample of manufacturing firms operating in an Italian region (Lombardy). Lichtenthaler and Ernst (2009), however, highlighted how technological aggressiveness is positively correlated to OI in a sample of medium-large firms, placed in Germany, Switzerland and Austria, and based on the dimensions of acquisition and exploitation of technology from the outside.
- With regard to collaboration management practices, several authors (Williamson, 1985; Schilling, 2010; Parida, 2009; Chiaroni et al., 2010), have investigated the influence of single practices, chiefly with case studies, and concluded that they are necessary for the successful implementation of OI. This research, after having defined a single factor including such diverse collaboration management practices, quantitatively confirms that such practices characterize high open firms.
This chapter describes which factors characterize and distinguish more open firms from those that are less open in their innovative process. However, the level of influence of the group of factors examined on the adoption of OI remains to be further studied, by using a discriminant or a regression analysis. Moreover, the OI phenomenon at the innovative process phase level will be studied in order to identify different modes of openness between the firms in the sample. It is also hoped that the sample be extended to allow a sector analysis, as well as a geographical extension which would allow different countries to be compared. Lastly, the OI phenomenon in service companies has, to date, received unjustly scant attention.
Note
1 This research was supported by the project grant CPDA109359.
References
Ahuja, G. (2000), ‘Collaboration Networks, Structural Holes, and Innovation: A Longitudinal Study’, Administrative Science Quarterly, 45(3): 425–55.
Amabile, T. M. (1983), The Social Psychology of Creativity, New York: Springer-Verlag.
Arora, A., Fosfuri, A. and Gambardella, A. (2001), Markets for Technology: The Economics of Innovation and Corporate Strategy, Cambridge: MIT Press.
Asakawa, K., Nakamura, H., and Sawada, N. (2010), ‘Firms' Open Innovation Policies, Laboratories, and Laboratories' R&D Performance’, R&D Management, 40(2): 109–23.
Brockhoff, K. and Pearson, A. (1992), ‘Technical and Marketing Aggressiveness and the Effectiveness of Research and Development’, IEEE Transactions on Engineering Management, 39(4): 318–24.
Bröring, S. and Herzog, P. (2008), ‘Organizing New Business Development: Open Innovation at Degussa’, European Journal of Innovation Management, 11(3): 330–48.
Calinski, R. B. and Harabasz, J. (1974), ‘A Dendrite Method for Cluster Analysis’, Communs Statist, 3: 1–27.
Chesbrough, H. (2003), Open Innovation: The New Imperative for Creating and Profiting From Technology, Boston: Harvard Business School Press.
Chesbrough, H., Vanhaverbeke W., and West J. (2006), Open Innovation: Researching A New Paradigm, Oxford: Oxford University Press.
Chiaroni, D., Chiesa, V., and Frattini, F. (2010), ‘Unravelling the Process from Closed to Open Innovation: Evidence From Mature, Asset-Intensive Industries’, R&D Management, 40(3): 222–45.
Christensen, J. F., Olesen, M. H., and Kjær, J. S. (2005), ‘The Industrial Dynamics of Open Innovation – Evidence from the Transformation of Consumer Electronics’, Research Policy, 34(10): 1533–49.
Cohen, W. M. and Levinthal, D. A. (1990), ‘Absorptive Capacity: A New Perspective On Learning and Innovation’, Administrative Science Quarterly, 35(1): 128–52.
Dahlander, L. and Gann, D. (2007), ‘How Open is Innovation?’, Summer Conference 2007 on Appropriability, Proximity, Routines and Innovation Copenhagen, CBS, Denmark, 18–20 June.
Dodgson, M., Gann, D., and Salter, A. (2006), ‘The Role of Technology in the Shift Towards Open Innovation: The Case of Procter & Gamble’, R&D Management, 36(3): 333–46.
Duda, R. O. and Hart, P. E. (1973), Pattern Classification and Scene Analysis, New York: John Wiley & Sons.
Edwards, T., Delbridge, R., and Munday, M. (2005), ‘Understanding Innovation in Small and Medium-Sized Enterprises: A Process Manifes’, Technovation, 25: 1119–20.
Elmquist, M., Fredberg, T., and Ollila, S. (2009), ‘Exploring the Field of Open Innovation’, European Journal of Innovation Management, 12(3): 326–45.
Enkel, E., Gassmann, O. and Chesbrough, H. (2009), ‘Open R&D e Open Innovation: Exploring the Phenomenon’, R&D Management, 39(4): 311–16.
European Commission (2005), The New SME Definition. User Guide and Model Declaration, Brussels: Enterprise and Industry Publications.
Eurostat (2009), ‘High-technology and Knowledge Based Services Aggregations Based on NACE Rev. 2’. Available online: http://epp.eurostat.ec.europa.eu/cache/ITY_SDDS/Annexes/htec_esms_an3.pdf (accessed 31 July 2012).
Fine, C. H. (1998), Clockspeed: Winning Industry Control in the Age of Temporary Advantage, Reading, MA: Perseus Books.
Freeman, C. (1974), The Economics of Industrial Innovation, Harmondsworth: Penguin.
Garvin, D. (1993), ‘Building a Learning Organization’, Harvard Business Review, July/August: 78–91.
Gassmann, O. (2006), ‘Opening Up the Innovation Process: Towards an Agenda’, R&D Management, 36(3): 223–8.
Gassmann, O. and Enkel, E. (2004), ‘Towards a Theory of Open Innovation: Three Core Process Archetypes’, in Proceedings of the R&D Management Conference, Lisbon, Portugal, 6–9 July.
Gassmann, O. and Enkel, E. (2010), ‘Creative Innovation: Exploring the Case of Cross-Industry Innovation’, R&D Management, 40(3): 256–70.
Gassmann, O., Enkel, E., and Chesbrough, H. (2010), ‘The Future of Open Innovation’, R&D Management, 40(3): 213–21.
Global Business Summit (2010), ‘Crescita e Sfide Globali, I1 Sole 24 ore’, Harvard Business Review, Milan, 17–18 May 2010.
Grönlund, J., Sjödin, D. R., and Frishammar, J. (2010), ‘Open Innovation and the Stage-Gate Process: A Revised Model for New Product Development‘, California Management Review, 52(3): 106–31.
Herstad, S. J., Bloch, C., Ebersberger, B., and Van de Velde, E. (2008), ‘Open Innovation and Globalisation: Theory, Evidence and Implications’, Vision Era Net Shared Knowledge Bases For Sustainable Innovation Policies, Oslo: NIFU STEP.
Hrastinski, S., Kviselius, N. Z., Ozan, H., and Edenius, M. (2010), ‘A Review of Technologies for Open Innovation: Characteristics and Future Trends’, Proceedings of the 43rd Hawaii International Conference on System Sciences – 2010. System Sciences (HICSS-January 5–8, 2010): 1–10. Available online at: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5428751.
Katz, R. and Allen, T. (1982), ‘Investigating the Not Invented Here (NIH) Syndrome: A Look at the Performance, Tenure, and Communication Patterns Of 50 R&D Projects’, R&D Management, 12(1): 7–19.
Keupp, M. M. and Gassmann, O. (2007), ‘The Competitive Advantage of Early and Rapidly Internationalizing SMEs in the Biotechnology Industry: A Knowledge Based View’, Journal of World Business, Special Issue: The Early and Rapid Internationalisation of the Firm, 42(3): 350–66.
Keupp, M. M. and Gassman, O. (2009), ‘Determinants and Archetype Users of Open Innovation’, R&D Management, 39(4): 331–41.
Knudsen, M. P. and Mortensen, T. B. (2011), ‘Some Immediate – but Negative – Effects of Openness on Product Development Performance’, Technovation, 31: 54–64.
Kolk, A. and Püümann, K. (2008), ‘Co-development of Open Innovation Strategy and Dynamic Capabilities as a Source of Corporate Growth’, Working Papers in Economics No. 173, Tallinn University of Technology (TUTWPE), s. 73–83. Available online at: http://ideas.repec.org/p/ttu/wpaper/173.html.
Laursen, K. and Salter, A. (2004), ‘Searching High and Low: What Type of Firms Use Universities as a Source of Innovation?’, Research Policy, 33(8): 1201–15.
Laursen, K. and Salter, A. (2006), ‘Open for Innovation: The Role of Openness in Explaining Innovation Performance among UK Manufacturing Firms’, Strategic Management Journal, 27: 131–50.
Lazzarotti, V. and Manzini, R. (2009), ‘Different Modes of Open Innovation: A Theoretical Framework and an Empirical Study’, International Journal of Innovation Management, 13(4): 615–36.
Lazzarotti, V., Manzini, R., and Pellegrini, L. (2010), ‘Open Innovation Models Adopted in Practice: An Extensive Study in Italy’, Measuring Business Excellence, 14(4): 11–23.
Lee, S., Park, G., Yoon, B., and Park, J. (2010), ‘Open Innovation in SMEs – An Intermediated Network Model’, Research Policy, 29: 290–300.
Leonard-Barton, D. (1995), Well Springs of Knowledge: Building and Sustaining the Source of Innovation, Boston, MA: Harvard Business School Press.
Lichtenthaler, U. (2005), ‘External Commercialization of Knowledge: Review and Research Agenda’, International Journal of Management Reviews, 7(4): 231–55.
Lichtenthaler, U. (2008a), ‘Open Innovation in Practice: An Analysis of Strategic Approaches To Technology Transactions’, IEEE Transactions on Engineering Management, 55(1): 148–57.
Lichtenthaler, U. (2008b), ‘Integrated Roadmaps for Open Innovation’, Research Technology Management, 51(3): 45–9.
Lichtenthaler, U. (2009), ‘Outbound Open Innovation and its Effect on Firm Performance: Examining Environmental Influences’, R&D Management, 39(4): 317–30.
Lichtenthaler, U. and Ernst, H. (2009), ‘Opening Up the Innovation Process: The Role of Technology Aggressiveness’, R&D Management, 39(1): 38–54.
Luukkonen, T. (2005), ‘Variability in Organizational Forms of Biotechnology Firms’, Research Policy, 34: 555–70.
MacPherson, A. (1997), ‘The Contribution of External Service Inputs to the Product Development Efforts of Small Manufacturing Firms’, R&D Management, 27(2): 127–43.
March, J. G. (1991), ‘Exploration and Exploitation in Organizational Learning’, Organization Science, 2(1): 71–87.
Mazzoleni, R. and Nelson, R. R. (1998), ‘The Benefits and Costs of Strong Patent Protection: A Contribution to the Current Debate’, Research Policy, 27: 273–84.
Miles, R. and Snow, C. (1978), Organizational Strategy, Structure and Process, New York: McGraw-Hill.
Narula, R. (2004), ‘R&D Collaboration by SMEs: New Opportunities and Limitations in the Face of Globalization’, Technovation, 25: 153–61.
Nobellius, D. (2004), ‘Towards The Sixth Generation Of R&D Management’, International Journal of Project Management, 22: 369–75.
OECD (Organization for Economic Co-operation and Development Statistical Office of the European Communities) (2005), Oslo Manual: Guidelines for Collecting and Interpreting Innovation Data, Parigi: OECD/European Communities.
Parida, V. (2009), ‘Challenges In Open Innovation Practices For Industries’, The 3rd Nordic Innovation Research Conference, NIR – 2008 – IEM Arctic workshop: 27–40.
Pisano, G. P. and Verganti, R. (2008), ‘Which Kind of Collaboration is Right for You?’, Harvard Business Review, December: 1–9.
Reed, R., Storrud-Barnes, S., and Jessup, L. (2012), ‘How Open Innovation Affects The Drivers of Competitive Advantage’, Management Decision, 50(1): 58–73.
Rothaermel, F. T. and Deeds, D. L. (2004), ‘Exploration and Exploitation Alliances in Biotechnology: A System of New Product Development‘, Strategic Management Journal, 25(3): 201–21.
Rothaermel, F. T. and Deeds, D. L. (2006), ‘Alliance Type, Alliance Experience and Alliance Management Capability in High-Technology Ventures’, Journal of Business Venturing, 21(4): 429–60.
Rothwell, R. and Dodgson, M. (1993), ‘SMEs: Their Role in Industrial and Economic Change‘, International Journal of Technology Management, special issue: 8–22.
Roussel, P., Saad, K., and Erickson, T. (1991), Third Generation R&D: Managing the Link to Corporate Strategy, Watertown, MA: Harvard Business Press.
Schilling, M. A. (2010), Strategic Management of Technological Innovation, 3rd edition, New York: McGraw-Hill.
Sousa, M. (2008), ‘Open Innovation Models and the Role of Knowledge Brokers‘, Inside Knowledge, 11(6): 18–22.
Teece, D. J. (1998), ‘Capturing Value From Knowledge Assets: The New Economy, Markets For Know-How, and Intangible Assets’, California Management Review, 40(3): 55–79.
Tidd, J. and Bessant, J. (2009), Managing Innovation, 4th edition, Chichester: Wiley & Sons.
Tidd, J. and Trewhella, M. (1997), ‘Organizational and Technological Antecedents for Knowledge Creation and Learning’, R&D Management, 27: 359–75.
Van de Vrande, V., de Jong, J. P. J., Vanhaverbeke, W. and de Rochemont, M. (2009), ‘Open Innovation in SMEs: Trends, Motives and Management Challenges‘, Technovation, 29: 423–37.
Verbano, C. and Venturini, K. (2012), ‘Openness and Innovation: An Empirical Analysis in Firms Located in the Republic of S. Marino’, International Journal of Business, Management and Social Sciences, 3(1): 1–11.
Veugelers, R. and Cassiman, B. (2006), ‘In Search of Complementarity in Innovation Strategy: Internal R&D and External Knowledge Acquisition’, Management Science, 52(1): 68–82.
West, J. and Gallagher, S. (2006), ‘Challenges of Open Innovation: The Paradox of Firm Investment in Open-Source Software’, R&D Management, 36(3): 319–31.
West, J. and Lakhani, K. (2008), ‘Getting Clear About Communities in Open Innovation’, Industry and Innovation, 15(2): 223–31.
Williamson, O. E. (1985), The Economic Institutions of Capitalism, New York: Free Press.
Woodman, R. W., Sawyer, J. E. and Griffin, R. W. (1993), ‘Toward a Theory of Organizational Creativity’, Academy of Management Review, 18(2): 293–321.