Approaches to Innovation Process Assessment: Complex Results from an Exploratory Investigation

ARTICLE International Journal of Engineering Business Management Approaches to Innovation Process Assessment: Complex Results from an Exploratory Investigation Regular Paper Ondrej Zizlavsky1* 1 Brno University of Technology, Brno, Czech Republic *Corresponding author(s) E-mail: [email protected] Received 20 July 2015; Accepted 26 November 2015 DOI: 10.5772/62052 © 2015 Author(s). Licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Managing innovation is vital for many enterprises to survive in a competitive and dynamic environment. Thus, decision-makers have always been involved in the chal‐ lenge of finding the right performance measures for the innovation process. This paper tackles the issue of innova‐ tion performance measurement and management. Its main goal is to present complex results obtained from the Czech manufacturing industry within the research project “Innovation Process Performance Assessment: A Manage‐ ment Control System Approach among Czech Smalland Medium-sized Enterprises” financed by the Czech Science Foundation – 13-20123P. The results reveal many positive aspects of the investigated issue. Czech companies are aware of the importance of innovation and engage in it in various forms. Moreover, the vast majority of the respond‐ ents also evaluated the implemented innovations no matter the size of the company. Smalland medium-sized enter‐ prises (SMEs) and large companies differ in terms of their innovation performance measurements and management control techniques and methods. However, both groups adopt traditional measurement tools rather than modern techniques. Here, the gap between global and Czech companies is noted. Therefore, this paper is supposed to motivate researchers to conduct more large-scale studies in the area of innovative performance measurement systems and their implementation in different business sectors and areas. Keywords Innovation Process, Management Control, Performance Measurement, Czech Manufacturing Indus‐ try 1. Introduction Innovation is the driving force of growth. It ensures competitiveness and offers opportunities for differentia‐ tion. Therefore, innovation is to be related to all that has to do with permanent, substantial customer benefits and a perceivable competitive edge: the development of new, successful business models. Hence, the entrepreneurial challenge lies in the successful management of innovations. As such, the question is not one of whether or not to innovate but rather of how to do so successfully [1]. Yet without the evaluation and continual review of innovation projects as a whole, improvements will not occur and lessons will not be learnt. This paper investigates how Czech manufacturing compa‐ nies measure and manage the performance of their inno‐ 1 Int J Eng Bus Manag, 2015, 7:25 | doi: 10.5772/62052 vation process. With regard to the identified objective of the research project (to learn and study the current issues of the management and control of innovations and their performance measurement as these areas are currently being approached in Czech industry, as well as the foreign expert literature and the practices of innovative Czech manufacturing companies) the following research hypoth‐ eses were defined: Hypothesis 1: Innovation is mainly performed by mediumand large-sized companies in the Czech business environ‐ ment who have sufficient resources. Hypothesis 2: Large companies perform innovation regularly – it is a part of their business model. Hypothesis 3: Large companies tend to invest greater sums of money in innovation (measured by the percentage of the annual budget). Hypothesis 4: Large companies tend to evaluate their innovative activities more frequently than SMEs. Hypothesis 5: Large companies tend to have implemented their innovation management control systems for longer than SMEs. Hypothesis 6: Large companies implement modern techniques of innovation management control. The study investigates the correlation between innovation management control systems (MCSs - including R&D expenditure, approaches to the evaluation of innovation projects, the methods utilized, the tools used, the period of implementation, etc.) and company size as the most important contingent factor. Therefore, as its exploratory aim, this study investigates the role of company size in the implementation of innovation MCSs. The paper consists of three parts. The first part briefly points out the importance of the topic (for more on inno‐ vation management control’s state of the art, see, e.g., [2-5]) as well as the research aim and the stated initial hypotheses. It is followed by a brief literature review. The core section consists of the interpretation of the original research design and procedure, including the determination of the sample size. The next part presents the complex results of the empirical investigation of the Czech manufacturing industry and their discussion. The research was conducted under the project “Innovation Process Performance Assessment: A Management Control System Approach among the Czech Smalland Medium-sized Enterprises”. The author of that paper is also the author of the present research project. 2. Literature review The old adage states: “You cannot manage what you do not measure”. This is especially true of innovation, whereby it is necessary to ensure focus, intelligibility and discipline, particularly with regard to the initial, inventive phase of the innovation process. Innovation is a continuous process. Companies are continually making changes to their products and processes and are always gathering new knowledge. Measuring such a dynamic process is much more complex than is the case with a static activity [6]. Therefore, measuring performance and the contribution to the value of innovation has become a fundamental concern for managers and executives in recent decades [7]. Previous research on innovation performance measure‐ ment has mainly focused on how managers choose their management control mechanisms [e.g., 8-11], on the effects of individual control mechanisms on specific outcomes [e.g., 9, 12-14], on how these effects may be moderated [e.g., 15, 16] and on how they pay off in the innovation process [e.g., 17]. In addition, many researchers have conducted studies to determine the degree to which innovation really improves a company’s performance [e.g., 18-24]. The professional literature provides the following: •A positive correlation [e.g., 22-28]. •A negative correlation [e.g., 29-31]. •A U-shaped correlation [e.g., 32-33]. •No clear correlation [e.g., 34-37]. Despite the abundance of books and publications written over the past few years in the field of performance meas‐ urement, the problem of defining a rigorous model for measuring innovation and its impact on a company’s financial performance has not been solved [38, 39], al‐ though some notable and interesting attempts have recently been published [e.g., 40-43]. The most typical indicator used comprises R&D expenses [44-46]. However, unlike most of the previous studies on innova‐ tion, in this study innovation is not measured through R&D expenditure alone. There are several well-known limita‐ tions for these measurements [47]. The importance of other dimensions of innovation, such as managerial or organiza‐ tional change, investment in design or skills, and the management of the innovation process itself, is increasing‐ ly acknowledged [48]. Therefore, this study deals with economic indicators. For clarity, they are divided into financial and non-financial metrics in the research (see Section 7). 3. Research procedure This section provides an overview of the data used for this study and the main characteristics of the research sample. After extensively examining the previous relevant and related literature and research in innovation, management control, performance measurement and related topics [2-6, 49], the field study was begun in 2014. Three types of data were collected for this study: a questionnaire and inter‐ views, company data and public information (data from a survey conducted every two years by the Czech Statistical Office were considered). 2Int J Eng Bus Manag, 2015, 7:25 | doi: 10.5772/62052 As concerns the methodological approach, following recent examples [50-55], a questionnaire-based survey was implemented to gather information and determine the real state of any solved issues of the management control of innovation activities. The survey method is often used to collect systematic data since it is timeand cost-efficient and allows the carrying out of a statistical analysis [56]. In addition, the replication of questions is possible and thus it presents a comparison of the results and a pattern analysis. The first step was to define the research sample. Before the research commenced, the circle of respondents was duly considered. The research could be limited based on company size, the field and the distribution of companies in the Czech Republic (CR). It was decided to carry out the research via a random selection of various-sized innovative companies from the manufacturing industry in the CR. This choice is related to the fact that managerial tools primary originated - and were subsequently developed - in manufacturing companies. The second feature is the fact that the manufacturing industry is considered to be the most significant industry in the development of the Czech economy because it is the largest sector. This allows for a sufficient number of companies to be contacted to partici‐ pate in the study. It is estimated that the target population consists of over 11,000 manufacturing companies. According to Czech Statistical Office and its 2012 survey, 51% of 5,449 innovative companies belong to the manufac‐ turing industry. Moreover, these companies contributed revenues comprising 45.4% of the total of the Czech economy in 2012 [53, p. 15]. In order to establish innovation success, it is first necessary to decide at what level the process will take place. Innova‐ tion effects can be measured at: i) the macro level (distin‐ guishing national and sector levels), ii) the meso level (the level of the company’s product family) and iii) the micro level (the level of innovation projects). At the macro level, there is a wide range of known and sophisticated means of measuring innovation potential and performance, such as, in Europe, the Innovation Union Scoreboard [57] and the Regional Innovation Scoreboard [58], while in the CR innovation surveys are regularly performed by the Czech Statistical Office. The macro level has been the subject of abundant research and studies in recent decades [e.g., 59-65]; therefore, the present study does not investigate this level and bases its considerations on the findings of the aforementioned studies. However, there are several reasons for analysing the link between innovation and productivity at the firm microlevel. First, it is companies that innovate, not countries or industries. Second, aggregate analysis hides a lot of heterogeneity. The performance of companies and their characteristics differ both between countries and within industries; countries‘ innovation systems are characterized by mixed patterns of innovation strategies which have an impact on companies‘ behaviour; moreover, companies may adopt multiple paths to innovation, including nontechnological ones. The advantage of micro-level analysis is that it attempts to model the channels through which companies‘ specific knowledge assets or channels can have an impact on their productivity, and therefore it sheds light on the role that innovation inputs, outputs and policies play in economic performance [48]. The key was to approach as many respondents as possible and so to acquire a sufficiently large data-scale factor for the evaluation of the primary research. The inquiry itself provided quantitative as well as semi-qualitative data on the current state of the issue in question. Simplicity and the relative brevity of the questionnaire - thereby affecting the respondent‘s willingness to fill it out - were important factors when creating it. The following types of questions were used: •Those with selectable answers and the option to select just one. •Those with selectable answers and the option to select several answers. •Those with pre-defined answers with an evaluation scale. •Some questions had the option to fill in answers freely. The questionnaire was structured into two parts. The first part consists of general information about the company, whereas the second part focuses on innovation measure‐ ment and management and applied management control tools and methods. The structured questionnaire also enables additional comments. As such, the respondents could express their opinion on certain questions regardless of the degree of their own innovation. The data acquired are presented in tables and graphs that are summarized in the following section. The questionnaire part of the research project, titled “Innovation Process Performance Assessment: A Manage‐ ment Control System Approach among Czech Smalland Medium-sized Enterprises” and sponsored by the Czech Science Foundation (GACR), was web-based so as to facilitate access to a large number of respondents. Once drawn up, the questionnaire should be tested on a sample population to determine whether all the items are understandable and clear. Therefore, the questionnaire was pre-tested by a number of academics and then sent to several practitioners for further review. Minor adjustments in the wording and layout were made in order to further the understanding of the questionnaire. None of these respondents considered the questionnaire to be difficult to complete. After several iterations of item editing and refinement, the questionnaire was administered to the full research sample. 3 Ondrej Zizlavsky: Approaches to Innovation Process Assessment: Complex Results from an Exploratory Investigation The survey consisted of 18 questions and was conducted by sending a fully standardized questionnaire by e-mail to each company (a link to the electronic questionnaire was included in the e-mail). The e-mail provided a brief introduction clarifying the purpose and objectives of the research project. It was sent exclusively to CEOs, top managers, executive officers or else - in small companies - directly to the owners. The survey was anonymous, took approximately 10 to 15 minutes to complete, and was conducted from April to November 2014. In addition, the survey respondents were asked to indicate whether they would be willing to participate in a followup interview. The aim of the follow-up interviews was to analyse the questionnaire responses in greater depth. The interviews were semi-structured and conducted with a degree of flexibility. A list of the main questions was sent in advance to facilitate the interviews. Although the questionnaire was semi-structured, the individual ques‐ tions were understood as topics for discussion. Numerous incentives revealed during the meeting with businessmen took the form of extended comments in section no. 5. 4. Determining the sample size The companies addressed were those that, by their princi‐ pal activities, belong in the manufacturing industry (according to CZ-NACE rev. 2, division C, section 10-33). Data on the total number of companies in the target population of the survey are taken from the Czech Statisti‐ cal Office. It is estimated that the target population consists of over 11,000 manufacturing companies [53]. A selective sample of these companies was obtained from the database Technological Profile of the Czech Republic (www.tech‐ profil.cz). A random sample of 2,877 innovative companies was drawn from the basic sample. In addition, the answer to the most frequently asked question concerning sampling (“What sized sample do I need?”) was given at the beginning of survey. In general, three criteria will usually need to be specified to determine the appropriate sample size: i) the level of precision, ii) the level of confidence or risk, and iii) the degree of variability in the attributes being measured [66]. There are several approaches to determining a sample size. It is often assumed that the samples in surveys are often large enough such that an estimate made from them is approximately normally distributed [67, p. 11]. However, in the social sciences, the populations from which samples are drawn are generally marked by a high degree of nonnormality. Applying the central limit theorem as well as the scope and target population of the survey, it can be assumed that the distribution of the acquired data is approaching a normal distribution. Therefore, the total sample size required for this study is calculated using Cochran’s formula [67] by taking 5% as the estimated percentage prevalence of the population of interest, 2 02 Z pq ne = where n0 is the required sample size, Z2 is the abscissa of the normal curve that cuts off an area α at the tails (1-α equals the desired confidence level), e is the desired level of precision, p is the estimated proportion of an attribute that is present in the population, and q is (1−p). The value for Z is found in statistical tables, which contain the area under the normal curve. Therefore, the required return sample size (n0) for this study was computed as follows: 22 02 2 1,96 0.5 0.05 385 0.05 Z pq n respondents e * * = = = 5. Research results After the first posting at the beginning of April 2014, the non-responding companies received a reminder at the end of May or the beginning of June; a follow-up was sent a few months later. At the end of November 2014, 354 completely filled-in questionnaires were collected. This number is very close to the calculated sample size. Hence, data acquired are considered statistically significant. The real response rate of more than 12% (354 completed questionnaires from 2,877 potential respondents) can be considered to be good because the response rates of mailback questionnaires are usually less than 10%. The de‐ tailed statistics of the questionnaire inquiries are shown in Table 1. Basic sample Manufacturing enterprises in the Czech Republic Selective sample Innovative manufacturing enterprises in the Czech Republic Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Response Number 63 94 123 74 354 % 10.08% 12.24% 12.60% 14.57% Non-response Number 562 674 853 434 2,523 % 89.92% 87.76% 87.40% 85.43% Total Number 625 768 976 508 2,877 % 100.00% 100.00% 100.00% 100.00% Response rate % 10.08% 12.24% 12.60% 14.57% 12.30% Table 1. Overall statistics and distribution of companies engaged by the research survey. Source: Own research (n=354). Moreover, using the Pearson chi-square test and data from Table 1, no statistically significant difference between the two groups (the sizes of the respondents and the nonrespondents) was found. The null fragmental hypothesis FH0 will be tested so that any random values are not 4Int J Eng Bus Manag, 2015, 7:25 | doi: 10.5772/62052 dependent in comparison with the alternative fragmental hypothesis FH1. FH0: The size of the company and respondence are not related to each other. FH1: The size of the company and respondence are related to each other. The calculated test criterion for micro companies: ( ) Chi Square 3.662; DF 1; P Value 0.056 63 complete questionnaires and 562 potential respondents - = = - = The calculated test criterion for small companies: ( ) Chi Square 0.004; DF 1; P Value 0.949 94 complete questionnaires and 674 potential respondents - = = - = The calculated test criterion for medium companies: ( ) Chi Square 0.122; DF 1; P Value 0.727 123 complete questionnaires and 853 potential respondents - = = - = The calculated test criterion for large companies: ( ) Chi Square 2.927; DF 1; P Value 0.087 74 complete questionnaires and 434 potential respondents - = = - = For a selected significance level, α = 0.05 is determined to be quantile chi-square (1) = 3.841. Because the value of the test criterion was not realized in the critical field (3.662 < 3.841 and P-Value = 0.056 for micro companies; 0.004 < 3.841 and P-Value = 0.949 for small companies; 0.122 < 3.841 and P-Value = 0.727 for medium companies; 2.927 < 3.841 and P-Value = 0.087 for large companies), the alternative fragmental hypothesis FH1 is rejected on a 5% level of significance and the null fragmental hypothesis FH0 is accepted. It is important to note that reminders were made for nonresponding companies, and in many cases the respondents answered that they would not fill-in the questionnaire due to: i) a lack of interest in surveys of this kind, ii) bad experiences of analogous surveys, iii) a lack of time, iv) the existence of internal policies related to non-participation in academic research, or v) not targeting specific competent executives (for the vast majority of addresses listed in the database). Thus, an important factor may be the fact that many e-mails did not arrive at the appropriate place. This could be evidence of the difficulties created by this kind of research as well as that innovation is a strategic issue for such companies. 6. General characteristics Questions from the first part of the questionnaire were related to the basic characteristics data of each company, such as the company‘s size, its origin, market, etc. Compa‐ ny size is a traditional contingency factor in economic research. Specifically, this section studies the impact of one factor linked to company size, i.e., the number of employ‐ ees. However, the revenue data were collected with the help of the questionnaire as well. Nonetheless, only the number of employees is a matter of concern for most parameters. In fact, this factor is usually the basis of company classification. The distribution of companies by size is based on EU law and the Recommendation of the European Commission 2003/361/EC of 6 May 2003 [68, p. 36]. This standard divides into four groups: micro-, small-, mediumand large-sized companies. Figure 1 shows the percentages obtained using the number of employees indicator. It is important to note that reminders were made for non-responding companies, and in many cases the respondents answered that they would not fill-in the questionnaire due to: i) a lack of interest in surveys of this kind, ii) bad experiences of analogous surveys, iii) a lack of time, iv) the existence of internal policies related to non-participation in academic research, or v) not targeting specific competent executives (for the vast majority of addresses listed in the database). Thus, an important factor may be the fact that many e-mails did not arrive at the appropriate place. This could be evidence of the difficulties created by this kind of research as well as that innovation is a strategic issue for such companies. 6. General characteristics Questions from the first part of the questionnaire were related to the basic characteristics data of each company, such as the company‘s size, its origin, market, etc. Company size is a traditional contingency factor in economic research. Specifically, this section studies the impact of one factor linked to company size, i.e., the number of employees. However, the revenue data were collected with the help of the questionnaire as well. Nonetheless, only the number of employees is a matter of concern for most parameters. In fact, this factor is usually the basis of company classification. The distribution of companies by size is based on EU law and the Recommendation of the European Commission 2003/361/EC of 6 May 2003 [68, p. 36]. This standard divides into four groups: micro-, small-, mediumand largesized companies. Figure 1 shows the percentages obtained using the number of employees indicator. Figure 1. Distribution of companies engaged in the research survey (n=354, number of employees) Source: Own research. The first empirical evidence of the survey emerged by way of descriptive statistics. It has been noted via the analysis of questionnaires that innovation is mostly performed by SMEs 17.80% 26.55% 34.75% 20.90% Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Figure 1. Distribution of companies engaged in the research survey (n=354, number of employees). Source: Own research. The first empirical evidence of the survey emerged by way of descriptive statistics. It has been noted via the analysis of questionnaires that innovation is mostly performed by SMEs (80.51% in total), respectively by medium enterprises (44.63% of respondents) followed by small enterprises (28.53% of respondents) and large enterprises (19.49% of respondents) and with micro companies (7.34% of respond‐ ents) at the tail. It can be assumed that the companies were aware of the threat of losing their competitiveness such that it could potentially lead to their demise. While large enterprises focused on operational efficiency and costs savings, SMEs were able to react to changes in the environment through 5 Ondrej Zizlavsky: Approaches to Innovation Process Assessment: Complex Results from an Exploratory Investigation innovation. The bigger the company, the more organiza‐ tionally demanding any innovative changes are, which is why mainly smaller businesses with a flexible organiza‐ tional structure innovate presently. Large companies naturally strive to support innovation as well, but due to their more complicated organization, bureaucratization of the innovation and decision-making process inhibits not only inventiveness but also slows the pace at which new inventions move through the corporate system towards market. The importance of SMEs to the development of the Czech economy is therefore increasing. This is also high‐ lighted by the Concept for Support of Small and Medium Entrepreneurs for the period 2014–2020, carried out by the Ministry of Industry and Trade of the Czech Republic. (80.51% in total), respectively by medium enterprises (44.63% of respondents) followed by small enterprises (28.53% of respondents) and large enterprises (19.49% of respondents) and with micro companies (7.34% of respondents) at the tail. It can be assumed that the companies were aware of the threat of losing their competitiveness such that it could potentially lead to their demise. While large enterprises focused on operational efficiency and costs savings, SMEs were able to react to changes in the environment through innovation. The bigger the company, the more organizationally demanding any innovative changes are, which is why mainly smaller businesses with a flexible organizational structure innovate presently. Large companies naturally strive to support innovation as well, but due to their more complicated organization, bureaucratization of the innovation and decision-making process inhibits not only inventiveness but also slows the pace at which new inventions move through the corporate system towards market. The importance of SMEs to the development of the Czech economy is therefore increasing. This is also highlighted by the Concept for Support of Small and Medium Entrepreneurs for the period 2014–2020, carried out by the Ministry of Industry and Trade of the Czech Republic. Figure 2. The ratio of innovative enterprises to the total number of enterprises engaging the CZSO surveys by size (CZSO, 2010; 2012; 2014). However, these results contrast with studies by the Czech Statistical Office [52, 53, 69] that consider large companies to be innovation leaders in the CR (see Figure 2). Previous studies conducted over 2009-2011 under the sponsorship of the Internal Grant Agency of the Faculty of Business and Management Brno University of Technology have reached similar contradictory conclusions [70-72]. Thus, and for better understanding, the classification according to turnover has been considered (see Figure 3). 52.3% 63.5% 80.7% 46.7% 64.0% 78.6% 38.2% 57.6% 78.1% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0% 90.0% Small Medium Large CZSO 2010 CZSO 2012 CZSO 2014 Figure 2. The ratio of innovative enterprises to the total number of enterprises engaging the CZSO surveys by size (CZSO, 2010; 2012; 2014) However, these results contrast with studies by the Czech Statistical Office [52, 53, 69] that consider large companies to be innovation leaders in the CR (see Figure 2). Previous studies conducted over 2009-2011 under the sponsorship of the Internal Grant Agency of the Faculty of Business and Management Brno University of Technology have reached similar contradictory conclusions [70-72]. Thus, and for better understanding, the classification according to turnover has been considered (see Figure 3). Figure 3. Distribution of companies engaged in the research survey (n=354, turnover) Source: Own research. On the one hand, given a certain level of innovation inputs, larger companies might have higher innovative sales intensity because they can appropriate innovation benefits more easily than SMEs and/or because of economies of scale. On the other hand, SMEs might use innovation inputs more efficiently because of entrepreneurial ability or their greater flexibility in the production process. Previous evidence has indicated that although larger companies are more likely to sell innovative products, this probability increases less than proportionately with size, and that among innovative companies the share of innovative products among total sales tends to be higher with smaller companies [e.g., 73]. A study by the OECD [48] also provides mixed results: size is positively correlated, negatively correlated or not correlated with turnover (sales) from innovations. Economies of scope and scale and knowledge flows within companies seem to play a role in commercialization. It is very difficult to either validate or invalidate Hypothesis 1 (“Innovation is mainly performed by mediumand large-sized companies in the Czech business environment who have sufficient of resources.”) based on these contrary results. What is the most important from managerial point of view is the finding that companies perform innovation. However, they differ in terms of the form of innovation (see Table 5). The essential question is not whether to innovate or not, but how to innovate. Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Czech Number 26 78 112 27 243 21.45% 22.87% 40.63% 15.05% Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Figure 3. Distribution of companies engaged in the research survey (n=354, turnover). Source: Own research. On the one hand, given a certain level of innovation inputs, larger companies might have higher innovative sales intensity because they can appropriate innovation benefits more easily than SMEs and/or because of economies of scale. On the other hand, SMEs might use innovation inputs more efficiently because of entrepreneurial ability or their greater flexibility in the production process. Previous evidence has indicated that although larger companies are more likely to sell innovative products, this probability increases less than proportionately with size, and that among innovative companies the share of innovative products among total sales tends to be higher with smaller companies [e.g., 73]. A study by the OECD [48] also provides mixed results: size is positively correlated, negatively correlated or not correlated with turnover (sales) from innovations. Econo‐ mies of scope and scale and knowledge flows within companies seem to play a role in commercialization. It is very difficult to either validate or invalidate Hypothe‐ sis 1 (“Innovation is mainly performed by mediumand largesized companies in the Czech business environment who have sufficient of resources.”) based on these contrary results. What is the most important from managerial point of view is the finding that companies perform innovation. Howev‐ er, they differ in terms of the form of innovation (see Table 5). The essential question is not whether to innovate or not, but how to innovate. Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Czech Number 26 78 112 27 243 % 100.00% 77.23% 70.89% 39.13% 68.64% Czech with foreign participation Number 0 17 42 39 98 % 0.00% 16.83% 26.58% 56.52% 27.68% Foreign Number 0 6 4 3 13 % 0.00% 5.94% 2.53% 4.35% 3.67% Total Number 26 101 158 69 354 % 100.00% 100.00% 100.00% 100.00% 100.00% Table 2. Origin of companies. Source: Own research (n=354). The vast majority of the companies addressed (68.64% of respondents) had Czech owners, 27.68% of the companies have foreign participation, and only 3.67% had foreign owners (see Table 2). Here, 55.93% of the inquired companies are engaged in innovative business within the CR, of which 12.99% operate on the domestic market within the whole CR, 42.94% operate on regional markets only within the CR, 30.79% do business in EU member and candidate coun‐ tries, and the remaining 13.28% do business around the world (see Table 3). The majority of the respondents (76.55%) carried out innovation irregularly and randomly, i.e., as a consequence of intuitive and immediate decisions, or reverse the negative development. Only 23.45% of the respondents executed innovation regularly, i.e., as a standard part of their businesses which is systematically managed. 6Int J Eng Bus Manag, 2015, 7:25 | doi: 10.5772/62052 Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Regularly Number 2 17 29 35 83 % 7.69% 16.83% 18.35% 50.72% 23.45% Irregularly Number 24 84 129 34 271 % 92.31% 83.17% 81.65% 49.28% 76.55% Total Number 26 101 158 69 354 100.00% 100.00% 100.00% 100.00% 100.00% Table 4. Period of innovation. Source: Own research (n=354). Here, Hypothesis 2 (“Large companies perform innovation regularly – it is a part of their business model.”) will be tested. Again, the chi-square test was applied. For this purpose, Question 5 “Over what period does your company realize innovation” is used. The null fragmental hypothesis FH0 will be used to test whether the random values are not dependent in comparison with the alternative fragmental hypothesis FH1: FH0: The size of the company and the period of innovation are not related to each other. FH1: The size of the company and the period of innovation are related to each other. The calculated test criterion for large companies is as follows: Chi Square 35.531; DF 1; P Value 0.000 - = = - = For a selected significance level, α = 0.05 is determined for a quantile chi-square (1) = 3.841. Because the value of the test criterion was realized in the critical field (35.531 > 3.841 and P-Value = 0.000), the fragmental null hypothesis FH0 is rejected on a 5% level of significance and the alternative fragmental hypothesis FH1 is accepted. This means that the random values are dependent and that the relation between Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Czech regional market Number 5 16 21 4 46 % 19.23% 15.84% 13.29% 5.80% 12.99% Czech national market Number 14 49 62 27 152 % 53.85% 48.51% 39.24% 39.13% 42.94% EU market Number 6 31 49 23 109 % 23.08% 30.69% 31.01% 33.33% 30.79% Global market Number 1 5 26 15 47 % 3.85% 4.95% 16.46% 21.74% 13.28% Total Number 26 101 158 69 354 % 100.00% 100.00% 100.00% 100.00% 100.00% Table 3. Market orientation. Source: Own research (n=354). the size of the company and the period for realizing an innovation was demonstrated. Next, the respondents answered the question about what innovations had been implemented by the company during the last three years, while what importance they had for the company represented another part of the research. They could select from four predefined answers (see the innova‐ tion classification according to the Oslo Manual 2005 [46]). The questionnaire included a list of examples for each type of innovation. Since the respondents were able to select more answers for this question, a recalculation had to be carried out whereby the relative frequency was determined as a percentage of the number of selected answers out of the total number of respondents in the group. Some of the key research findings are summarized in Figure 4. Next, the respondents answered the question about what innovations had been implemented by the company during the last three years, while what importance they had for the company represented another part of the research. They could select from four predefined answers (see the innovation classification according to the Oslo Manual 2005 [46]). The questionnaire included a list of examples for each type of innovation. Since the respondents were able to select more answers for this question, a recalculation had to be carried out whereby the relative frequency was determined as a percentage of the number of selected answers out of the total number of respondents in the group. Some of the key research findings are summarized in Figure 4. Figure 4. Implemented innovations (n=354). The most-performed innovation type was product innovation (38.42% of respondents), followed by process innovation (29.38% of respondents) and marketing innovation (20.90% of respondent). Organizational innovation is at the tail, with 11.30% of respondents. These balanced results highlight the fact that product innovations often require process innovations (e.g., in the form of acquiring new production technologies), and in order for these product innovations to be successful on the market and bring the company higher value, it is often necessary to seek new distribution channels via marketing innovations. Moreover, many of the innovators in the manufacturing industry implemented both product and process innovation. The measurement instrument used in the questionnaire to estimate the importance of innovation was evaluated via a five-item Likert scale: 1 – very important, 2 – important, 3 – neutral, 4 – not important, 5 – completely unimportant. In the summary of the percentage ratio of positive answers, namely the values 1 (very important) and 2 (important), the order of individual possibilities was determined. Therefore, the results show that the respondents see the importance of innovations for their company in the following order: innovation of products, processes, marketing and organization. The evaluation of the importance of 38.42% 29.38% 11.30% 20.90% Product innovation Process innovation Organisational innovation Marketing innovation Figure 4. Implemented innovations (n=354) The most-performed innovation type was product innova‐ tion (38.42% of respondents), followed by process innova‐ tion (29.38% of respondents) and marketing innovation (20.90% of respondent). Organizational innovation is at the tail, with 11.30% of respondents. These balanced results highlight the fact that product innovations often require process innovations (e.g., in the form of acquiring new production technologies), and in order for these product innovations to be successful on the market and bring the company higher value, it is often necessary to seek new distribution channels via marketing innovations. More‐ over, many of the innovators in the manufacturing industry implemented both product and process innovation. The measurement instrument used in the questionnaire to estimate the importance of innovation was evaluated via a five-item Likert scale: 1 – very important, 2 – important, 3 – neutral, 4 – not important, 5 – completely unimportant. In the summary of the percentage ratio of positive answers, namely the values 1 (very important) and 2 (important), the order of individual possibilities was determined. There‐ fore, the results show that the respondents see the impor‐ tance of innovations for their company in the following order: innovation of products, processes, marketing and organization. The evaluation of the importance of individ‐ ual types of innovation for companies is shown in Table 5. 7 Ondrej Zizlavsky: Approaches to Innovation Process Assessment: Complex Results from an Exploratory Investigation Cronbach’s alpha coefficient for particular types of inno‐ vation is above 0.97, and the overall Cronbach’s alpha is 0.9828, which is higher than the commonly used bench‐ mark value 0.70. This means strong internal consistency and good reliability of scale. The main motives leading to the commencement of such innovation activities are growth of revenues/profits, reaction to demand, increased quality, increased market share and, last but not least, inspiration by competitors. The motives of innovation activities represent a starting point for innovation strategies. Strategic marketing and research - with a nomination by top management - are also involved in strategy proposal and formulation. The objective of every innovation strategy rests on achieving a competitive advantage, leading to the company‘s improved position on the market; any other objectives are derivative [53, 74]. Innovation expenditure includes all expenses for both inhouse and externally purchased activities that aim at the development and introduction of innovations, regardless of whether these innovations have been introduced yet. They comprise current expenditure (e.g., labour costs, externally purchased goods or services, etc.) and capital expenditure (e.g., machinery, instruments, intangible assets, etc.). Innovation expenditure is an important metric to deter‐ mine the quantity of resources that a company provides for carrying out innovation activities. To overcome the unwillingness of the respondents to transmit confidential information, four categories were predefined: innovation expenditure based on actual needs up to 5% of an annual budget, 5-10% of an annual budget and more than 10% of an annual budget (see Table 6). The most frequent innovation expenditure involved up to 5% of an annual budget, especially in SMEs. SMEs invest in innovative activities according to actual needs. The largest contribution in this regard is made by micro companies (65.38% of respondents), followed by small (38.61% of respondents) and medium companies (36.08% of respondents). In contrast, the inverse is observed for expenditure of 5-10%, ranging from 11.54% for micro companies to 34.78% for large companies. Large companies (23.19% of respondents) devote more than 10% of their annual budget for innovation, while micro companies invest into innovation according actual need (65.38% of respondents). In other words, the larger the company, the higher the expenditure, the more regularly planned it is, and the greater the amount that is annually spent on innovation. Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Actual needs Number 17 39 57 9 122 % 65.38% 38.61% 36.08% 13.04% 34.46% Up to 5% of annual budget Number 6 37 75 20 138 % 23.08% 36.63% 47.47% 28.99% 38.98% 5-10% of annual budget Number 3 21 23 24 71 % 11.54% 20.79% 14.56% 34.78% 20.06% More than 10% of annual budget Number 0 4 3 16 23 0.00% 3.96% 1.90% 23.19% 6.50% Total Number 26 101 158 69 354 % 100.00% 100.00% 100.00% 100.00% 100.00% Table 6. Innovation expenditures. Source: Own research (n=354). In what follows, research Hypothesis 3 (“Large companies tend to invest greater sums of money in innovation (measured by the percentage of the annual budget).”) will be tested. The chisquare test was used. The FH0 partial null hypothesis, stating that random quantities are independent, was tested against the FH1 partial alternative hypothesis. For this purpose, Question 8 “Estimate the total amount of expen‐ diture on innovation by percentage of the annual budget” is used. The null fragmental hypothesis FH0 will be tested as to whether the random values are not dependent in comparison with the alternative fragmental hypothesis FH1: FH0: The size of the company and the percentage of the annual budget invested in innovation are not related to each other. FH1: The size of the company and the percentage of the annual budget invested in innovation are related to each other. The calculated test criterion for large companies is as follows: Chi Square 59.624 DF 1; P Value 0.000 - = = - = 1 Very important 2 Important 3 Neutral 4 Not important 5 Unimportant Cronbach's Alpha No % No % No % No % No % Product innovation 103 29% 85 24% 67 19% 42 12% 57 16% 0.9761 Process innovation 96 27% 78 22% 67 19% 64 18% 49 14% 0.9730 Marketing innovation 62 18% 99 28% 85 24% 69 19% 39 11% 0.9798 Organization innovation 57 16% 81 23% 79 22% 60 17% 77 22% 0.9794 Table 5. Importance of particular innovation types for companies. Source: Own research (n=354). 8 Int J Eng Bus Manag, 2015, 7:25 | doi: 10.5772/62052 For a selected significance level, α = 0.05 is determined for a quantile chi-square (1) = 3.841. Because the value of the test criterion was realized in a critical field (59.624 > 3.841 and P-Value = 0.000), the fragmental null hypothesis FH0 is rejected on a 5% level significance and the alternative fragmental hypothesis FH1 is accepted. This means that the random values are dependent and that the relation between the size of the company and the percentage of the annual budget invested in innovation was demonstrated. More‐ over, the result of this test corresponds with the earlier Hypothesis 1 - “Innovation is mainly performed by mediumand large-sized companies in the Czech business environment who have sufficient resources.” 7. Innovation management control Well-managed innovations successfully commercialized in the market are a tool that companies can use to win competitive advantages that will allow them to prosper even under such conditions as the recent recession. It is a modern trend to seek to innovate, but innovations must be implemented prudently and in a targeted manner. More‐ over, innovative activities are very costly and they can tie a substantial part of a company’s available resources for a significant period of time. The effort and resources expend‐ ed must be recouped if the company is to stand a chance of surviving in a strongly competitive environment. The need for an MCS is crucial for innovation. Therefore, a key area of the survey was the issue of the evaluation of and responsibility for innovative activities – namely how the key decisions are made and how it is decided whether a given innovation is viable. When asked whether they had evaluated the implemented innovative projects, the vast majority (79.38% of respondents) an‐ swered affirmatively. On the other hand, what is somewhat disquieting is the fact that this area was neglected by 20.62% of the respondents, even though innovations had been implemented by them (see Table 7). Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Yes Number 5 23 49 24 101 % 19.23% 22.77% 31.01% 34.78% 28.53% Somewhat yes Number 11 62 72 35 180 % 42.31% 61.39% 45.57% 50.72% 50.85% Somewhat no Number 8 9 21 8 46 % 30.77% 8.91% 13.29% 11.59% 12.99% No Number 2 7 16 2 27 7.69% 6.93% 10.13% 2.90% 7.63% Total Number 26 101 158 69 354 % 100.00% 100.00% 100.00% 100.00% 100.00% Table 7. Evaluation of innovation projects. Source: Own research (n=354). Based on these data, Hypothesis 4 (“Large companies tend to evaluate their innovative activities more frequently than SMEs.”) is tested. Independence statistical testing of two qualitative characters is carried out for statistical depend‐ ency verification. For this purpose, Question 9 “Has your company implement an R&D management control sys‐ tem?” is used. The null fragmental hypothesis FH0 will be tested as to whether the random values are not dependent in comparison with the alternative fragmental hypothesis FH1. FH0: The size of the company and the evaluation of innovation are not related to each other. FH1: The size of the company and the evaluation of innovation are related to each other. Calculated test criterion for large companies is as follows: Chi Square 1.967 DF 1; P Value 0.161 - = = - = For a selected significance level, α = 0.05 is determined for a quantile chi-square (1) = 3.841. Because the value of the test criterion was not realized in a critical field (1.967 < 3.841 and P-Value = 0.161), the fragmental alternative hypothesis FH1 is rejected on a 5% level significance and the null fragmental hypothesis FH0 is accepted. In other words, SMEs are aware of the importance of innovation evaluation and they perform it as well as large companies. On the other hand, SMEs use different techniques of management control to large companies (see Table 11). For those enterprises which responded affirmatively to the above question (281 in total), the period since the company had implemented an innovation MCS was examined. Category (Number of employees) Micro (1-9) Small (10-49) Medium (50-249) Large (>250) Total Less than 5 yearsNumber 12 29 25 8 74 % 57.14% 35.80% 20.00% 14.81% 26.33% From 5 to 10 years Number 7 33 56 25 121 % 33.33% 40.74% 44.80% 46.30% 43.06% From 11 to 15 years Number 2 14 36 15 67 % 9.52% 17.28% 28.80% 27.78% 23.84% More than 15 years Number 0 5 8 6 19 0.00% 6.17% 6.40% 11.11% 6.76% Total Number 21 81 125 54 281 % 100.00% 100.00% 100.00% 100.00% 100.00% Table 8. Period of innovation MCS implementation. Source: Own research (n=281). Hypothesis 5 (“Large companies tend to have implemented their innovation management control systems for longer than SMEs”) will be tested. 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