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We chose the high-technology electronics industry to empirically test our model.
We chose this industry because it is generally considered a "high-velocity environment, where demand, competition and technology are in constant and accelerated change" Wirtz et al,, Therefore, the process of adaptation could be observed and results tested over a reasonable time span where performance data are available i.
To be chosen for this sample group, companies had to be active before December l and have from 10 to employees. Data on innovation practices was collected through face-to-face interviews. A group of innovation consultants from a regional development agency held face-to-face interviews with representatives from each company. The interviews were conducted from a historical perspective, trying to capture information about the companies' innovative best practices during the period from to The face-to-face interview methodology has been shown to be especially effective when there is a high degree of technical complexity in the questions, and when the interviewer is a specialist in the matter Doyle, This methodology allows the researcher to control the quality and real interest of the interviewed manager, in contrast with the uncertainty about the real degree of knowledge from managers responding to mailed surveys.
During these visits from October to February , extended interviews were conducted with the managing director of each company. Each case company received approximately two hours of interviewing and telephone contact. In addition to the interviews, tours of factories, offices, warehouses, and stores were taken in all cases. An initial questionnaire was designed and pre-tested with an initial subsample of 10 companies, in order to clarify and improve the questions.
Of the surveys, The average length for each interview was l09 minutes. Innovation behavior variables: Through the interview process, we captured the innovation behavior of companies through 93 questions that measure the degree of involvement of the companies in key activities, using a Likert scale from 1to 7. The interviewer guided the company to answer each question and assured a consistent enquiry procedure. Each interview generally followed the structure shown until we received answers for every question.
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A simplified outline is shown in Table 1 below. Business performance results variables: According to our model, we selected three business results measures: sales growth, profit per employee, and return on assets. We used the SABI database to collect data of business results from to , which was deemed a sufficient time span to ensure that the most successful companies, in an extremely fast-changing industry, had time enough to adapt their strategies in terms of continuous technological disruptions and strong international competence.
We obtained data of the three financial figures for each company in our sample and calculated the sales growth rate, average profit per employee, and return on assets during the research period. Innovation behavior of companies: exploratory factor analysis.
We initially speculated that the behavior of companies could be explained around the nine main innovation dimensions proposed in the conceptual model. For that purpose, an exploratory factor analysis was performed of the variables. Through the exploratory factor analysis of data, we found that these 93 questions proposed by literature could be reduced to l9 factors see Appendix. Consequently, we recognize that some of the nine dimensions of innovation traditionally considered in the literature could be further subdivided into more refined elements; i. In Table 2 , we map our l9 factors to each of the nine relevant dimensions in our model.
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Innovation behavior and firm structure: Clusterization of innovative behavior. In order to determine if there are innovation practices related to different industrial environments, we conducted a cluster analysis to classify companies according to their similar innovation practices, using each of the variables innovation management practices. We used the complete-linkage method, where similarity between clusters is the smallest minimum diameter sphere that can enclose all observations in both clusters and assigns each observation a dimensional vector to a cluster.
The observation company is assigned to minimize the Euclidean distance.
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Five clusters were found. Looking at the companies forming each cluster, we see that these five clusters corresponded to five different kinds of electronics companies Fig. These results show relevant relationships between the company typology and environment size, structure, range of products, and position in the value chain and innovation management practices, such as companies with similar typologies and environments also having similar innovative behaviors Table 3.
Figure 4: Average dimension scores for companies in each cluster. We wanted to further research the relationships in our model Figure 3 between innovation management practices as they modify a company's resources and competences and business parameters, having had sustained business results from to In order to determine if innovation patterns related to different business performance in the mid- and long-term, we performed the following test:.
Companies were ranked according to their positions concerning each of the three measures parameters sales growth, profit per employee, and return on assets. In each case, ranks were divided in quartiles; each company. Additionally, we compared the average of each cluster to find which ones had better business performance. However, the chi-square test of independence showed no relationship between cluster membership and quartile distribution asymp.
We found statistical significance showing that companies from Cluster 1 have higher results than Clusters 2, 3, and 4 in terms of turnover variation Table 5. Clusters 1 and 4 showed the best performance with A Chi-square of We found that Clusters 1, 4, and 5, which showed more innovative behavior, were generally better than Clusters 2 and 3, with a significant difference in their profit per employee media Table 7.
The analysis relating clusters to their position on quartiles measured according to their return on assets media showed that Clusters 1 and 5 were statistically better than 2 and 3 Table 9 , with A final test was carried out with an integrated ranking. Data for each dependent variable performance measure was separately tabulated in a descending order, and all of the companies were ranked. The ranks were averaged to obtain a mean rank score, then the companies were arranged in descending order of these mean scores. Finally, the companies in the panel were the divided into four final quartiles, to measure overall performance based on the mean quartile scores.
In order to find which innovation practices are most related to business performance, we built a lineal regression model according to our model, where business performance is the dependent variable and a function of innovation management practices independent variables. Three regressions have been run with the three business results indicators. Table 12 represents the results of the regression where turnover variation is the dependent variable explained by innovation management practices. There is significant evidence that some innovation management practices influence business turnover the model adjusted R-square was 0.
Two innovation practices are strongly supported: companies with high innovation systematization tend to increase their turnover, and those who use advanced methods and ICT in product development and production exhibit a higher propensity for improvement in their sales. Further research is needed to clarify this point. Models where profit per employee and return on assets as independent variables explained by innovation manage -ment practices did not show an acceptable R-coefficient and F-test, so there is no significant evidence that some innovation management practices influence these business results indicators.
Our empirical analysis reveals that, depending on the industrial environment, companies use different innovation management practices. Nevertheless, for the entire sample, the systematization of innovation is the main factor positively related to improvements in business performance. This is in agreement with some previous results in other contexts.
For instance, Battisti and Iona found that establishment size, ownership structure, and product market concentration are important determinants of the intensity of management practices in the British establishment. Our research shows that something similar may happen in the high-technology sector.
Each of the five clusters of companies corresponds to a particular industrial environment and, at the same time, seems to be related to a different degree of innovation management. For instance, the first cluster formed by companies with a high level of innovation management corresponds to medium-sized companies in the subsector of medical devices and telecommunications.
On the other hand, companies in the second cluster tend to be smaller companies with strong leadership i. The third cluster contains medium-sized companies with little professionalization in the management team and with poor skills in innovation project management. The fourth one is also formed by medium-sized companies -suppliers of control and verification devices - that showed strong design management and operative flexibility, but no capabilities for branding. Finally, the last cluster belongs to medium and large companies that are professionalized, are suppliers for multinational companies, and have high levels of quality of branding but low levels of design and commercial innovation.