Absorptive capacity

Relationships between Innovatory success, Technological Opportunities

and Absorptive Capacity. “Empirical Study of the Dutch Machine-Manufacturing Industry.”

Bachelor Thesis Study: International Business Administration

University: RSM Erasmus University Location: Rotterdam, the Netherlands

Author: Ing. Remco Jan de Kramer Mail: [email protected]

IBA Research Project/Bachelor Thesis-11 (BTH00)

RSM Erasmus University Rotterdam 2005-2006

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Abstract

This research was conducted to analyze how technological firms apply their ‘absorptive capacity’ internally through conducting in-house R&D activities or externally through assimilating technological opportunities to achieve more innovatory successes. The hypotheses testing were conducted based on the data collected from Dutch machine-manufacturing industry (2002). The results showed several important insights. Firstly, firms’ continuously conducting in-house R&D activities does positively increase firms’ chance to achieve more innovatory success which is measured by the average sales generated by new products. Secondly, when firms exercise the tacit capability absorptive capacity to exploit the right and beneficial technological opportunities from the external environment, those finally applied technological opportunities help firms to achieve more innovatory success. However, the rest of taking-in technological opportunities becomes obsolete and costly. Thirdly, when the availabilities of profiting from in-house R&D activities and external technological opportunities are both present, firms equipped with higher absorptive capacity are more able to apply external technological opportunities to obtain the profits.

1. Introduction Technological change is one of the principal drivers of competition. It plays a major role in industry structure change, as well as in creating industries. (Porter, 1985) In order to survive in such radically changing turmoil, any firm no matter what size can not ignore the exogenously generated knowledge but must innovate in time. Innovation is the result of a combined act of firm-specific determinants (in-house R&D activities, firm size, etc) and external influences (technological opportunities, R&D spillovers, etc.). In-house R&D activities play a fundamental role referring to firms’ internal ‘technological capabilities’. They will systematically broaden the existing stock of knowledge and apply themselves efficiently in the innovation process. R&D activities are meant to respectively increase the probability of the arrival rate of product and process innovation. As to the external factors which determine the success of innovation, it is the total amount of readily available and exploitable resources—technological opportunities—to be considered (Becker and Peters, 2000). Such opportunities are diverse. They vary in the varieties and usefulness of technological knowledge involved. They not only vary from one industry to another but also from one firm to the other. In order to fully take advantage of technological opportunities, firms need to develop certain idiosyncratic capability to identify, assimilate and exploit such externally generated knowledge and then apply for own commercial ends. Such capability composes an important part of a firm’s ability to create new knowledge. The exercise of such capability represents a type of learning that differs from learning-by-doing. ‘Learning-by-doing’ refers to the automatic process by which the firm becomes more practiced and more efficient at doing what it is already doing (Tidd, Bessant and Pavitt, 2001, p339). In contrast, with such capability a firm may acquire outside knowledge that will permit it doing something quite different and quite ‘new’. Such capability was firstly called ‘Absorptive Capacity’ by two researchers—Cohen and Levinthal in their breakthrough work



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in 1989 and 1990. The current literatures have discussed a number of issues on absorptive capacity from different perspectives1. Such as analyzing the sources of absorptive capacity (Cohen and Levinthal,1990; Veugelers, 1997; Cockburn and Henderson, 1998), the effects of absorptive capacity on inter-organizational level (Nicholls-Nixon,1993; Mowery, Oxley and Silverman, 1996; Koza and Lewin, 1998; Lane and Lubathking, 1998), the hindering factors for internal subunits knowledge transfer (Szulanski, 1996), the country specific factors for developing absorptive capacity (Luo, 1997; Shenkar and Li, 1999), the impact of absorptive capacity on the efficiency of new product development (Stock, et al, 2001) and the effects of absorptive capacity on firms’ innovation process and between external factors such as technological opportunities and spillovers and organizational innovate efforts (Becker and Peters, 2000; Nieto and Quevedo, 2005). From their studies, we saw the firm-specific absorptive capacity positively correlates to in-house R&D expenditures and the external innovation factors, such as technological opportunities and spillover. However, few of the studies directly relate the firm-specific absorptive capacity to the firms’ innovatory success. Hence we took the internal and external factors, which are closely related with the generation and application of absorptive capacity, such as in-house R&D activities and technological opportunities, together with absorptive capacity into a conceptual model to analysis their joint effect on innovatory success.

This paper has been organized as following: Section 2 considers the aim, theoretical and practical objectives and relevancy of our research; Section 3 explains the literature review and five hypotheses in the conceptual model; Section 4 describes the data collection results and how to process each measurement of variables and finally provides the statistical tools for SPSS processing; Section 5 presented the summarized results of the SPSS processing as well as diagnosing the findings; Section 6 provides the conclusion for our research.

2. Aims, Objective and Relevancy 1 See Appendix A for summery of historical researches done on absorptive capacity



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Our research aim is to test whether the critical impact of absorptive capacity applies to the innovatory success of the Dutch machine-manufacturing firms. Our theoretical objective is to improve the theoretical view on the firms’ innovatory success from the perspective of absorptive capacity. We will focus on the triggering effect of absorptive capacity in the process of transforming technology opportunities to the sources of innovatory successes of Dutch firms. As indicated on the European Innovation Scoreboard (2004), limited innovative entrepreneurial activities and problems with financing of early stages of innovation are some of the bottlenecks, which hold back the innovative potential of the Netherlands. Taking this fact into account, the practical contribution of our study will provide Dutch firms a revealing view on the importance of accumulating firm-specific absorptive capacity, in order to increase their innovative potential and achieve more innovatory success. De facto, in certain sense our study in nature is the most similar to the previous studies done by Veugelers (1997), Becker and Peters (2000) and Nieto and Quevedo (2005). However Becker and Peters did their studies among German manufacturing firms while Nieto and Quevedo’s study was based on the samples of Spanish industrial technological firms which carried out innovative activities. Although Veugelers did her study among Dutch technological firms with the outlays on R&D in 1997, the aim of her study was major to test the effect of internal basic R&D activities on the firms’ ability to absorb external information. She didn’t study the effect of absorptive capacity and its related factors on the firms’ innovatory success. Thus from the above comparison, our research will fill the academic gap of the impact of absorptive capacity on the innovatory success of Dutch technological firms.

3. Literature Review and Hypotheses Formulation Scholars of technological changes have observed that firms investing in own R&D activities can be able to utilize information, which is available externally (Cohen and Levinthal, 1989).Tilton (1971, p71), for example, states that one of the main reasons firms invested in R&D in the semiconductor industry was that, “…an R&D effort provides an in-house technical capabilities that could keep these firms abreast of the latest semi-conductor developments and facilitate the assumption of new technology development elsewhere.” According to the break-through work of Cohen and Levinthal (1989; 1990), they recognized that while R&D activities obviously generate innovations, it also develops the firm’s ability to identify, assimilate, and exploit knowledge from the environment—what they called a firm’s ‘learning’ or ‘absorptive capacity’. Absorptive capacity not only refers to a firm’s ability to imitate new process or product innovation, but also includes the firm’s ability to exploit outside knowledge of a more intermediate sort, such as basic research findings that provide the basis for subsequent applied research and development. A significant benefit of R&D was found to the firms’ existing knowledge base which is built via the development of a stock of prior knowledge constituting the firm’s absorptive capacity. Clearly, it indicates that by carrying out own R&D activities the prior knowledge is generated as a by-product, which fundamentally determines a firm’s absorptive capacity. Hence the first hypothesis between



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own R&D activities and a firm’s absorptive capacity was formulated as following:

The more in-house R&D activities are conducted; the more experience and prior

knowledge is generated, thus the more accumulated absorptive capacity of firms. The firms’ innovatory success is defined as the positive impact of R&D-induced improvement and extensions in technical and organizational know-how on economic magnitudes, such as increase in productivity, turnover and profits (Becker and Peter, 2000). Studies show that firms consider their in-house R&D to be their most important source of innovation. This perception also appears to be supported by evidence on R&D spending and firm sales: a firm’s R&D intensity (its R&D expenditures as a percentage of its revenues) has a strong positive correlation with its sales growth rates, sales from new products, and profitability (Roberts, 2001). By continuously conducting R&D activities, the firm can both develop new technological products to satisfy continuous customers’ demands and improve own organizational process efficiency to operate more effectively. The more new and market popular products developed, the more turnover and profits will be achieved. By improving process efficiency, the organizational productivity can be increased and the organization can be more responsive to the external opportunities which could be potential sources of the innovatory success. Thus both consequences induced by firms’ own R&D activities can positively contribute to innovatory success. Thus the second hypothesis between in-house (own) R&D activities and innovatory success was formulated as following:

The more in-house R&D activities are conducted; the more innovatory successes

are gained by firms. The consequences of own R&D activities could contribute to the firms’ innovatory success. However, the second face of R&D recognized that the absorptive capacity also has a positive effect on the productivity of innovative activities (Cohen and Levinthal, 1990; Cockburn and Henderson, 1998) and improves the efficiency of the process of development of new products (Atuahene-Gima, 1992; Stock et al., 2001). The role which absorptive capacity plays in establishing technological cooperation agreements has been found as a critical element in explaining firms’ success (Mowery et al., 1996; Koza and Lewin, 1998; Kumar and Nti, 1998; Lane and Lubatkin, 1998; Shenkar and Li, 1999). Absorptive capacity accrued in any development efforts may positively impact a firm’s successful application of innovation (Schilling 2002). This implies that the firms with the higher absorptive capacity, the more innovatory successes will be achieved. A few empirical studies proved that there is positively relationship between absorptive capacity and the firms’ efforts to innovate (Cohen and Levintal, 1989 and 1990; Veugelers, 1997; Becker and Peters, 2000; Nieto and Quevedo, 2005). The higher firms’ absorptive capacity, the more efforts will be put into innovation in the present and in turn the more new innovation will be generated, thus the more chances of firms to achieve innovatory success. The third hypothesis between firms’ absorptive capacity and innovatory success was formulated as following:

The higher the absorptive capacity of the firm, the more chances the firm will



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achieve innovatory successes. March and Simon (1958, p188) suggested most of the innovations result from borrowing rather than invention. As a consequence of the dynamic technological changes, innovative firms continuously expand and optimize their in-house R&D by apply technological opportunities (Becker and Peters, 2000). The technological opportunity reflects the possibilities for technological progress in different industries. It indicates how easy in terms of time and costs it is to bring about innovation in a given field of knowledge which is in a given industry (Nieto and Quevedo, 2005). The degree of technological opportunity depends on the nature of technological fields themselves, on the path they have followed in the past, on how long they have been in existence and their closeness to basic science (Nelson and Winter, 1992). Empirical studies (Becker and Peters 2000; Geroski 1990; Harabi 1995) emphasize that technological opportunities do have a crucial influence on the type, range and results of firm’s innovative activities. Improvements in production arising from making use of technological opportunities lead to the achievement of more efficient production process, greater technological knowledge and improved competencies of R&D personnel. Provided that the necessary organizational requirements are available, such as qualified R&D personnel, the adaptation of know-how drawn from the stock of technological opportunities broadens the firms’ capabilities and so increases the probability of firms’ achieving innovatory success. Thus the fourth hypothesis between technological opportunities and firms’ innovatory success was formulated as following:

The higher level of readily available technological opportunities, the more chances

the firm will achieve innovatory successes. It has been verified that absorptive capacity constitutes a factor for success in processes of technological knowledge transfer within organizations (Szulanski, 1996). Absorptive capacity represents the important analytical links between the external stock of technological opportunities and the in-house capabilities in developing and improving products (Cohen and Levinthal 1990; Malerba and Torrisi 1992; Cantner and Pyka, 1998). As the fourth hypothesis formulated in this paper pointed out that the firms operating in the environments with a high level of technological opportunity will have more chances to achieve innovatory success. However the structural variable technological opportunity is not totally exogenous. Its impact on innovatory success will depend on some internal characteristics of the firm (Teece et al, 1997). In fact, the availability of technological opportunity in a given sector does not affect all the firms operating in it with the same intensity. The extent to which they make use of those opportunities will largely depend on the knowledge and capacities each firm has at its disposal. Only firms having accumulated a critical stock of know-how and possessed a certain capacity of absorption could be able to take advantage of such a pool of industrial technological opportunities. In other words, the relationship between the external technological opportunities and firm’s innovatory success will be moderated by firms’ absorptive capacity. In general science-based technological opportunities basically ask for a higher level of absorptive capacities than those generated by other knowledge sources such as customers (Nelson and Wolff, 1997). Thus, the fifth hypothesis was formulated as following:



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The absorptive capacity exercises the triggering effect over the impact of

technological opportunities on firms’ innovatory success.

4. Methodology and Description of the Empirical Study

The Data Collection Results The data collection pool is based on the contact database of Dutch machine-manufacturing industry obtained from Dutch Chamber of Commerce in The Hague. We designed an electronic survey to collect the sample data on each variable presented in the conceptual model. The applied questionnaire was adopted and accustomed from the similar one made by Nieto and Quevedo(2005). Since it is better to draw conclusions as less dependent as possible on specific and local circumstances, we chose to email the questionnaire among the entire geographical range of our sample population (Verschuren and Doorewaard, 1999, p145). In the questionnaire (attached in Appendix B), we used open questions to measure firms’ average R&D spending and annual volume of sales in the last three years as well as the percentage of sales generated by new products of firms in the last five years; and the seven-point likert scale for each statement relating to absorptive capacity. As the original database did not contain every firm’s e-mail address, we firstly looked for the missing e-mail addresses of the 1226 companies in the database. Due to some objective causes (no websites, bankruptcy, unwilling to provide e-mail address etc.) we were unable to find the e-mail addresses for 297 companies. The rest of sample size represented 75.7 % of the total population before distribution of the survey. Thus, we finally emailed the questionnaire to the rest of 929 potential respondents. In the whole data collection process, we tried to direct the electronic survey to R&D Managers or CEOs of our sample firms, who are more known of and creditable on the answers to our specific open questions. After distribution of the electronic questionnaire, 58 e-mails failed to deliver, further reducing the sample size to 871 of the total population. Because of the initial poor response rate, the survey has been sent again for 3 times. In the end, we achieved the final response rate at 3.92% that is 48 responses. Although the response rate is rather low comparing to 19.75% response rate in similar research done by Nieto and Quevedo in 2005, it is still above the minimum response rate of 2.4% for the preconditions of performing all the SPSS analysing techniques. Thus, the final sample size still formed a sufficient basis for further statistical data analysing. Low response rate increases the probability that non-response bias will be substantial. Among the final 48 responses, there exists partial incomplete non-response problem on the questions of either average sales or R&D expenditures or percent of sales produced by new products. We estimated the reason for this problem is because the respondents of some machine-manufacturing firms don’t know the exact figures or their firms don’t put enough efforts on R&D activities, or the credential concerns making them hesitate to give away specific key company-operation figures. The last issue have already been taken into account



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when we designed this research. However as those 48 respondents did give us their ratings on measuring absorptive capacity and because of the small sample size concern, we kept all the blank entries in our final data input for SPSS testing. However, because of such small sample size and substantial non-response bias the final research results of our paper can not be taken as the generalized scientific fact abstracted from Dutch machine-manufacturing industry and only could be regarded as the exploratory insights for further more extensive research.

Measurements of Variables in the Model In the conceptual model, we have defined one dependent variable—Innovatory Success (IS) and three independent variables—In-house R&D activities (IRD), Absorptive Capacity (ACAP) and Technological Opportunities (TO). Innovatory Success (IS) In the Section 3 Literature Review innovatory success was defined as positive impact of R&D-induced improvement and extensions in technical and organizational know-how on economic magnitudes, such as increase in productivity, turnover and profits (Becker and Peter, 2000). The sales generated by new products or the number of patents received by firms (Nicholls-Nixon, 1993) can be used to quantitatively measure the degree of ‘success’ brought by firms’ innovative activities. However due to the unselected nature of our sample, small-sized Dutch firms hardly receive any patents but probably generate sales from the little modified products, which could be induced by small-scale innovative activities. Those little modified products could keep generating the regular amount of sales by meeting the normal customers’ needs. The big-sized firms, such as Philips are more capital adequate thus more inclining to have larger R&D spending in various projects of developing different new products in order to meet more diverse customers’ needs. Therefore the more new products developed, the more sales generated. In this research, we measure firms’ innovatory success by the volume of sales generated by new products, which is the quantitative data in essence. By the survey we obtained the data on percentage of sales generated by new products of Dutch manufacturing firms in the past five years and average sales of firms in the past three years. IS=Average sales generated by new products=Percentage (%) of sales generated by new products*Average sales In-house R&D Activities (IRD) In the empirical studies, in-house R&D activities could be measured by R&D spending, the number of employees working for R&D or the total working hours of employees for R&D. (Nieto and Quevedo, 2005). We used the number of average R&D spending to quantify the independent variable IRD in our model. The use of average terms of R&D spending in the last three years allows us to average out the oscillations in firms’ R&D spending due to some macro-environment changes or abnormal situations. Thus, it makes the data input more



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objective and in turn our final conclusion more comprehensive. The measurement level for in-house R&D activities is also quantitative in essence. We directly obtained the data on the amount of average R&D spending of Dutch firms through open questions of survey. IRD=Average R&D Spending Technological Opportunities (TO) This is the only qualitative variable in our model. The degree of technological opportunities depends on the nature of technological fields themselves, on the path they have followed in the past, on how long they have been in existence and their closeness to basic science (Nelson and Winder, 1982). It is hard to precisely quantify the technological opportunities. Thus, we took the recommendation of Geroski (1990) and adopted an indirect measurement used by Nieto and Quevedo in their work (2005) for technological opportunities. We introduced a dummy variable d_TO to represent the degree of technological opportunities. Each Dutch firm who replied our survey has a BIK company classification code used by Dutch chambers of commerce2 to classify the Dutch manufacturing industry in detailed. Then the degree of technological opportunities from which firms benefit can be estimated by performing an analysis of difference (one-factor ANOVA) of the variable In-house R&D activities (IRD) which is measured by average R&D spending, on the basis of BIK-code firm classifications. Since firms belonging to the same group are involved in very similar industrial activities, therefore their research interests-In-house R&D activities must be much alike. Firms in the same operating environments would have similar areas of interested technological exploitation and thus their R&D spending in the similar areas must be alike. By performing the One-factor ANOVA analysis, we found the mean of average R&D spending of our respondents is 32,346,940 euros. We saw from the following table that there are two divergent categories above and below the mean of average R&D spending of firms in 15 BIK categories. The BIK company categories above the average ratio line, they have higher average R&D spending, thus they have higher lever of technological opportunities indicated by d_TO=1. The categories below the average line means those firms have lower R&D spending, thus they have relatively lower level of technological opportunities, which is indicated by d_TO=0.

2 See Appendix C: BIK company code of Dutch manufacturing industry



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Table1: the summary of BIK company industries in aspect of the degree of TO

BIK Code Sector Number of firms % of Total High degree of technological opportunities: d_TO=1

2924 Manufacture of other general purpose machinery incl. their parts n.e.c3

7 15.9%

Low degree of technological opportunities: d_TO=0 2875 Manufacture of general metal products 2 4.5% 2911 Manufacture of engines and turbines 1 2.3% 2912 Manufacture of Pumps and compressors 5 11.4% 2913 Manufacture of Taps 2 4.5% 2914 Manufacture of Bearings 1 2.3% 2922 Manufacture of lifting and handling

equipment 3 6.8%

2923 Manufacture of non-domestic cooling and ventilation equipment

3 6.8%

2941 Manufacture of Electronic Tools 1 2.3% 2942 Manufacture of machine-tools for metal

working 3 6.8%

2951 Manufacture of machinery for metallurgy 2 4.5% 2953 Manufacture of Machinery for Food 4 9.1%

3 “2924” category includes Manufacture of packaging machines, weighing appliances and retail shop machines,

slot machines, general purpose machinery, parts of machines, and repair of other general purpose machinery,

according to more detailed BIK company classification.



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2954 Manufacture of machinery for textile4 2 4.5% 2956 Manufacture of other special purpose

machinery n.e.c5 7 15.9%

3330 Manufacture of equipment for guarding/controlling industrial processes

1 2.3%

Effective Total 44 100%

Index of Absorptive Capacity (ACAP) As there is no standard measurement for operational representation of the variable Absorptive Capacity (ACAP), we adopted the most recent measuring method of absorptive capacity in the work of Nieto and Quevedo (2005) in order to make it testable. They have identified four principal factors influencing the accumulation of absorptive capacity either positively or negatively from the works of Cohen and Levinthal (1990) and Fiol and Lyles (1985). Based on the work of Cohen and Levinthal (1990, p544), they pointed out firms should concentrate on the organized way of communication between firms and the external environment and on the ‘nature of know-how and experience within the firm’. They saw the trade-off between the internal and external components in the absorptive capacity, since the attention is directed to how the relationship between ‘shared knowledge and range of knowledge’ among individuals affects the development of organizational absorptive capacity. They noted that it is beneficial to the development of organizational absorptive capacity when there is the ‘diversity of knowledge’ among individuals. The work of Fiol and Lyles (1985, p804-805) highlighted the importance of a firm’s strategic positioning as an element determining its ability to learn—absorptive capacity. They stated that the ‘organization’s strategic posture’ partly determines its learning capacity. R&D strategy needs to align with the overall organizational strategy, thus the overall organizational strategy limits the objective and the range of R&D activities to develop absorptive capacity. Hence the overall organizational strategy influences absorptive capacity by limiting the decision making process of R&D division and by setting the context of interpreting the environment for R&D activities. Hence, those four principle factors measuring absorptive capacity concluded from the above discussion are 1) communication with the outside environment, 2) level of know-how and experience in the organization, 3) diversity and overlaps in the knowledge structure and 4) strategic positioning. We made five to six statements for each major factor measuring absorptive capacity in the questionnaire. In total, there are 23 statements6 belonging to the four major factors mentioned above. All 48 Respondents gave their ratings on those 23 statements, which can be regarded as qualitative. We performed multi-regression analysis to ascertain the relationship between 23 minor measuring factors of ACAP and the innovatory success (IS). The underlying assumption of this operation is that those minor factors which proved to significantly and 4 “2954” category includes Manufacture of machinery for textile and apparel, for leather production and for

laundries and dry-cleaning. 5 “2956” category includes Manufacture of machinery for the processing of rubber and plastics, machinery for the

production of petroleum, machinery for the graphics industry and machinery for other specific purposes. 6 See Appendix B-the questionnaire for the details about 23 statements of ACAP.



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positively relate to the variable innovatory success will affect ACAP through the firms’ commitment to R&D (Nieto and Quevedo, 2005). However after the first run of

multi-regression analysis, we found this model is fit ( =86.2%) but invalid with the

significant level at 12.8%. We thought there must be some strongly irrelevant independent variables in the model, which interfere the validity of the model when we performed the regression analysis. Thus we removed the two variables with the highest significant levels: B7 (sig.=86.5%) and D18 (sig.=82.9%) and retest the model. In the second run, the whole model

still keeps fit with at 86.2% and the significant level lowers to 4%, which is valid

according to our predetermined 5% level. Thus in this valid model, we found seven independent variables significantly related to innovatory success summarized in the following table.

Table 2: the significant aspects of ACAP related to the variable innovatory success

Order of statement Aspect Beta Coefficient (t-value)

A4 Whether a technology supplier to other firms 0.811** (2.765)

C17 Whether most of the projects for new product development are carried out by the employees from different departments working as a team.

0.730** (2.530)

D18 Strategic posture at achieving maximum product quality 1.603*** (3.423)

C12 Flat organization structure -0.565** (-2.710)

C14 Whether most of employees have a wide range of experience -1.011** (-2.623)

Notes: i. ***significant at 5% level; **significant at 1% level

By identifying three independent variables which are positively and significantly related to IS (A4, C17 and D18), we summed up the respondents’ ratings on each of them to get the index of the quantitative variable ACAP for the following testing of model hypotheses. The higher the index of ACAP the higher level of absorptive capacity the firm has equipped.

Statistical Tools By combining our own objective of research and the statistical processing methods in the paper of Nieto and Quevedo (2005), we developed the following two-level analysis and the accompany equations for testing the hypotheses in the model.

At the first level, from to , we will perform the linear regression analysis to ascertain

the single relationship between every independent variable and the dependent variable innovatory success (IS). As we discussed in the Section 3, the absorptive capacity influences



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the firms’ innovatory successes also through its interaction with in-house R&D activities and

in the process of taking external technological opportunities. Thus in the hypotheses

and , we will also perform linear regression analysis to ascertain the relationship between

the internal and external interaction of firm and its innovatory success.

At the second analysis level, as our main focus is still on the assumed triggering effect of absorptive capacity on firms’ innovatory process through internal and external dynamics, we will apply multi-regression analyses to test the following equations. We manipulated the

appearance of the variable ACAP in the following two equations. The equation was

developed by integrating and , excluding the variable ACAP temporarily. The other

equation was developed by integrating all the independent variables, the interactions

between ACAP and technological opportunities (d_TO) and that between ACAP and in-house R&D activities (IRD), in order to comprehensively test the whole model.

5. Results Table 3: Results of Regression Analysis

Independent Variables

IRD --- (0.987)

0.820*** (8.234)

0.910*** (2.799)

1.128*** (2.887)

ACAP --- (0.675)

--- (0.559)

d_TO 0.531*** (3.656)

--- (-0.292)

-20.121*** (-35.916)



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ACAP*IRD 0.838*** (8.807)

--- (-1.970)

ACAP*d_TO 0.548*** (3.819)

20.590*** (35.632)

2.5%

(sig.=0.330) 67.3%*** 1.2%

(sig.=0.504) 28.2%*** 30%*** 70.2%*** 67.3%*** 99.7%***

Notes: i. The dependent variable of the first hypothesis is absorptive capacity (ACAP), while the dependent variable of the rest

is innovatory successes (IS). ii. ***significant at 5% level; **significant at 1% level. iii. Under the value of the beta

coefficients it is the t-value in the brackets.

Table 3 shows the summarized results for testing hypothesized equations formulated in the former section, including the five hypotheses we formulated out of the conceptual model and

the testing of whole model represented by the equation .

Firstly, we compared the results for the first four hypotheses. The R square of is only

2.5%, which means only 2.5% of variance in the absorptive capacity could be explained by the independent variable in-house R&D activities (IRD). The significant level of the model is much bigger than 5%, so the result of first hypothesis testing is invalid. When testing the

relationship between IRD and IS ( ), we filtered out one outlier (represented by Philips)

because of its significant influence on the general trend of the rest of data. Both in-house R&D expenditure and sales generated from new products of Philips is at one million euros level while the rest of other Dutch manufacturing firms are at 100 000 euros level. When going to the second hypothesis, the result shows totally different situation. The second

equation is fit with R square at 67.3%. The strong positive relationship between IRD and

IS could be explained by the coefficient 0.820 at 1% significant level. As we took the firms’ average R&D expenditure to measure the level of in-house R&D activities of firms, the positive coefficient (0.820) between IRD and IS means there will be 0.820 euro more sales generated by firms’ new products as the in-house R&D spending increased by 1 euro. As to

which is to test the relationship between firms’ absorptive capacity and innovatory

success, only 1.2% of variance in firms’ innovatory success (IS) was found to be explained by the variable absorptive capacity (ACAP). The model is invalid and the linear relationship

between IS and ACAP is not significant. The last Univariate regression analysis was to

test the relationship between technological opportunities (d_TO) and innovatory success (IS). The results show that 28.2% of dependent variances can be explained by the independent variable d_TO. The coefficient of d_TO (0.531) shows that there is a significant positive relationship between firms’ technological opportunities and innovatory success.

The fifth hypothesis is to ascertain the enhancing effect of firms’ absorptive capacity



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over the relationship of technological opportunities and firms’ innovatory success, which was formulated to ascertain the relationship between the interaction of firms’ absorptive capacity, external technological opportunities (ACAP*d_TO) and firms’ innovatory success (IS). The

equation of can explained 30% of variances in the dependent variable IS. The result

of shows when firms’ absorptive capacity interacts with external technological

opportunities its integrated effect strongly and positively enhances firms’ innovatory successes, which is demonstrated by a significant and positive coefficient 0.548. In other words, firms’ absorptive capacity helps firms to achieve a good strategic position in the

external dynamics. As to the internal dynamics of firms’ innovatory success , we found the

interaction of firms’ absorptive capacity and in-house R&D activities (ACAP*IRD) has a significant and positive relationship with firms’ innovatory success (IS), which implies that firm’s absorptive capacity also exercises an enhancing effect through firm’s in-house R&D

activities on innovatory success. The hypothesis excludes the variable ACAP manually to

test the independent effects of in-house R&D activities (IRD) and external technological opportunities (d_TO) on firms’ innovatory success. The result of equation testing is valid and fit with R square at 70.2%. We found that when the external technological opportunities and in-house R&D activities are both readily available, the input of in-house R&D activities is beneficial for firms’ innovatory success, which is shown by the positive and significant coefficient of IRD (0.910), while there is no contribution of external technological opportunities to firms’ innovatory successes found.

The was not only designed to ascertain all the relationships of the conceptual model but

also to comprehensively ascertain the dynamics of ACAP playing in the process of firms transferring in-house R&D activities and the external technological opportunities into innovatory success. The R square of the final comprehensive model (99.7%) turned out to be the highest one among all the regression analyses. When performing the regression analysis

of at the first time, the R square was even at 100% and one independent variable IRD has

been excluded out of the equation testing by SPSS. We estimated the problem is still caused by Philips which has far higher level of in-house R&D spending and sales generated by new products (both all at 1 million euros level.) than the rest of respondents. Hence, we set a condition to filter Philips (respondent 35) out of our testing dataset. At the second time of multi-regression testing, the R square has been reduced to 99.7% and IRD was included in the equation testing. However when we tried to reduce R square further by filtering out the lower

extreme of outliers, this manipulating action did not affect the results of that we’ve got

when R square was calculated as 99.7% at the second time of testing. Hence we just took the result of hypothesis testing at the second time (R square=99.7%) as the final results to be interpreted. The 99.7% of variance of dependent variable innovatory success can be explained



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by the equation , which is very fit. The relationship between IRD and IS is still found to be

significant and positive, which is consistent with the result of but the coefficient of IRD

is higher than 1. There is still no significant relationship found directly between firms’

absorptive capacity and innovatory success, the same result as we’ve got from . The

dummy variable technological opportunities (d_TO) appear significantly but negatively related to the dependent variable firms’ innovatory success (IS), which is inconsistent with the

testing result of . Only one interaction of ACAP with d_TO showed the significant and

positive relationship with firms’ innovatory success, which is not only consistent with the

testing result of but the effect of interaction of ACAP*d_TO on firms’ innovatory

success becomes even more strong when comparing the new coefficient of 20.590 to 0.531

that we’ve got from . However, the other interaction of ACAP with in-house R&D

activities (IRD) shows no significant relationship with firms’ innovatory success, which is

inconsistent with the result of .

6. Conclusion

At the Univariate-analysis level (From to ), we did not find the significant causal

relationships either between firms’ in-house R&D activities (IRD) and absorptive capacity (ACAP) or between absorptive capacity and firms’ innovatory success (IS). This is inconsistent with our original hypotheses; especially the causal relationship between IRD and ACAP, which was originally found by Cohen and Levinthal in their breakthrough works (1989 and 1999). The explanation could be that Cohen and Levinthal had a much bigger sample size (318 firms out of 151 lines of business) and they have developed a more precise econometric model to justify the relationship between IRD and ACAP. However as the focus of our research is not on the origin of firms’ absorptive capacity, we will not analyze further to diagnose the relationship between IRD and ACAP. As the former literature indicated and the testing results showed, firms’ in-house R&D activities is directly contributing to firms’ innovatory success. Although the direct effect of ACAP on firms’ innovatory success is not significant, it plays an assisting role in triggering firms’ capability to absorb new technological opportunities from the environment. The first significant finding of the results is that the external technological opportunities (d_TO) is significantly but negatively related to firms’ innovatory success (indicated by the negative coefficient -20.121), while the interaction of firms’ absorptive capacity with external technological opportunities is significantly and positively related to firms’ innovatory success (indicated by the positive coefficient 20.590). The testing result between d_TO and firms’ IS



17

in the final comprehensive equation testing is counter-intuitive against the finding of our

previous testing result of . This counter-intuitive result can be explained by the

co-existence of interaction of ACAP*d_TO in the final equation. As from the testing result of

, we saw the more technological opportunities being available for firms will help to

increase firms’ innovatory success, but when adding the interaction term ACAP*d_TO, the more technological opportunities (d_TO) decreases firms’ chance to achieve innovatory success. The interaction term ACAP*d_TO implies that firms apply their absorptive capacity in the process of absorbing the external technological opportunities. The absorptive capacity in the interaction term represents a kind of firms’ tacit innovation efforts already exercised in assimilating the external technological opportunities, which means firms have already purposely chosen the right and beneficial technological opportunities to develop further, such as having already made a good business plan based on such right technological opportunities, build pilot plant and launch the extensive market test. Thus the rest of technological opportunities represented by another variable d_TO become obsolete and even costly for firms to invest. This conclusion could be explained by the product development methodologies which have historically been used to manage the process of reducing 1,000s of ideas to 100s that are feasible, 10s that are developable, and only one or two that are ever scaled up to production. According to a study of the Product Development and Management Association, 68% of American firms (including IBM, Procter & Gamble, 3M, General Motors, and Corning) and 56% of European firms (Roberts, 2001) use some type of stage-gate process to manage their new product development process. The stage-gate model, developed by Robert Cooper, provides a linear template or roadmap for driving new product projects from idea to launch and beyond. At the idea and initial several stages, the process asks firms firstly to either generate new ideas internally or scanning the external environment to get potential profitable technological opportunities. In a research on the development time and cost by each stage of new product development, Buggie (2002) found an exponential increasing relationship between time and cost. That means when the new product development process goes further, more towards the final commercialization such as extensive plant production, promotion and launch to the markets, the opportunity cost of every stage gets larger and larger. This finding of Buggie implies that the more opportunities of new product development of firms turn out to be obsolete the more opportunities costs will incur for firms, thus the failed ‘technological opportunities’ hinders the profits achieved by firms from exploiting more market favorable technological opportunities. The second significant finding of the results is that the effect of interaction of ACAP with in-house R&D activities becomes insignificant on firms’ innovatory success. This finding could be explained by the result concluded by Nieto and Quevedo (2005) in their research. They found when a firm has a higher level of absorptive capacity it is more able to apply the knowledge generated by other firms (external technological opportunities), thus has a greater ability to obtain profits. In this case, when firms are readily available to invest both in internal R&D activities and the external technological opportunities, the firm equipped with higher absorptive capacity will be more inclined to invest in external technological opportunities in



18

order to get more innovatory success. Thus this insignificant interaction term of ACAP with in-house R&D activities implies that firms’ absorptive capacity is more effective on absorbing external technological opportunities and transferring them into firms’ real profit. And from the aspect of coefficient, we found that only the significant coefficient of IRD and

the interaction term of ACAP*d_TO in the final comprehensive model are higher than 1.

The rest of significant coefficients are either negative or less than 1. As we took the numeric figure of sales generated by new products to measure the dependent variable innovatory success (IS), all the significant coefficients represent the absolute increment of sales generated by new product while there is 1 unit of money input on every independent variable. Thus the significant coefficients which do not exceed 1 implies that technological firms can not offset their cost of increasing 1 unit of money on every independent variable by generating less than 1 unit of money increments on sales of new products. However the significant coefficients of IRD (1.128) and ACAP*d_TO (20.590) confirm that while firms increase their efforts on in-house R&D activities and applying absorptive capacity to effectively assimilate the external technological opportunities, the technological firms indeed will make profits, especially from applying absorptive capacity to exploit and commercialize the right external technological opportunities. Due to the small sample size and partial non-response problem among the dataset, the conclusion of this research is not representative for the whole Dutch machine-manufacturing industry. They could only be considered as the insights drawn out of the exploratory research. More extensive and intensive researches need to be done in order to get more generalized and representative results for Dutch machine-manufacturing industry.



19

Appendix A: Previous Researches on Absorptive Capacity (Nieto and Quevedo, 2005) Author Sample Measure Basic Relationship Results

Cohen and Levinthal (1989, 1990)

Thousand three hundred and two business units in 297 industrial companies in the USA.

Impact on R&D expenditure of certain characteristics of the learning environment

Relates R&D spending/sales with absorptive capacity.

Factors affecting ease of learning impact on the R&D spending as a proportion of sales, hence absorptive capacity exists and is relevant.

Atuahene-Gima (1992)

Theoretical analysis Relates adoption of internal technology licenses to absorptive capacity and to internal capacity to develop new products

The existence of absorptive capacity is a basic condition for adoption of internal technology licenses.

Nicholls-Nixon (1993)

Multinational pharmaceutical companies

Patents. Development of new products Reputation

Relates absorptive capacity to the advantage taken (level of learning) of research alliances

Companies with greater absorptive capacity invest more in R&D, cooperate more on R&D and get more out of alliances.

Mowery, Oxley and Silverman (1996)

Bilateral alliances established between 1985 and 1986 in which one of the firms is

Patents of Firm A cited in patents of

Relates overlaps in the technological interests of those cooperating to several variables such

Absorptive capacity is important in allowing the co-operating parties to get technological capabilities out of an agreement.



20

from the USA

Firm B/Total citations presents in Firm B’s patents before the agreement

as nationality of participants, structure of the agreement, investment on R&D and Absorptive Capacity

Szulanski (1996)

One hundred and twenty two transfers of 38 management practices involving 8 originating firms

Set of items rated on a scale from 1 to 5

Analyzes factors hindering knowledge transfer between different organizational units of a single firm and groups them in four sets: characteristics of the knowledge, characteristics of the context, characteristics of provider and characteristics of the receiver (Absorptive Capacity seen as part of last)

Absorptive capacity of the receiver is one of the principal factors explaining rigidities in companies (firm stickiness) over transfer of knowledge between their organizational units.

Veugelers (1997)

Two hundred and ninety firms with outlays on R&D in the Netherlands between 1992

Links with basic research; Presence of R&D

Relates R&D spending to absorptive capacity

Co-operation on R&D has positive effects on investment in own R&D only if there is absorptive capacity.



21

and 1993 department. Number of Ph.D.s in the R&D area

Luo (1997) Joint ventures established in China between local firms and multinationals between 1988 and 1991

Technology staff/Total staff

Impact of given characteristics of local firms (absorptive capacity, market strength, size, etc.) on the success of co-operation agreements

The absorptive capacity of the local associate is vital for good running of any joint venture.

Cockburn and Henderson (1998)

Ten large pharmaceutical firms

Total publications per dollar spent on R&D per year

Examines the relationship between public R&D, private R&D and absorptive capacity

Only firms with absorptive capacity are able to have access to or connect with basic research carried out by public laboratories. The degree to which private companies tap into the work of public laboratories is correlated to their absorptive capacity

Koza and Lewin (1998)

Theoretical analysis Relates the aims of alliances (exploratory/exploitation) to the form of the co-operation agreement(absorptive



22

capacity of participants, systems of control and identification)

Kumar and Nti (1998)

Theoretical analysis Relates the stability and evolution of an alliance to conflicts relating to the ability of those co-operating to attain their learning objectives (linked to their absorptive capacity)

Lane and Lubatking (1998)

International co-operation agreements for R&D set up between pharmaceuticals firms involved in developing therapeutic products between 1985 and 1993

Overlap of product characteristics. Formalization of management practices. Degree of centralization of decision taking. Similarities in pay and benefit packages

Relates absorptive capacity with success within the firms in the alliance (in learning organizational skills)

The factors determining success in the relationship are the following: (1) relevance of the learning firm’s basic knowledge to the teaching firm’s; (2)similarity in pay and benefits practices; (3) similarity in areas of research; (4) similarity between organizational structures.



23

Shenkar and Li (1999)

Ninety Chinese firms seeking partners for co-operation agreements.

Knowledge brought by local associate (Binary variables according to whether or not the local contact brings various specific types of knowledge)

Relates the type of associate that the local firm will seek to the knowledge that it possesses; a partner complementing knowledge it already has or aiding it to expand this knowledge.

Firms seek knowledge in areas complementary to their own rather than in their own area of specialization.

Mangematin and Nesta (1999)

Four hundred R&D contracts drawn up between the French National Centre for Scientific Research and firms located in the area of Grenoble

R&D Spending. Number of researchers. Number of R&D laboratories. Permanence of R&D activity. Relations with public research institutes. Number of publications of patents

Analyzes the relationship among three features: the tacit or codified nature of knowledge, its basic or applied status and the firms absorptive capacity

The presence of considerable absorptive capacity inhibits cooperation on R&D. Moreover, given this circumstance it is possible to absorb all sorts of knowledge, both basic and, through a whole range of vehicles (doctoral students, machinery, and scientific staff). There is also a diversification of the mechanisms by which such absorption can occur.

Becker and Peters

Two thousand and nine hundred innovative

Existence of permanent R&D

The relation between the level of technological

Regressions not including absorptive capacity indicate that sources linked to scientific



24

(2000)

manufacturing firms (data from the Mannheim Innovation Panel [MIP] gathered in Germany in 1993)

departments. R&D activities carried out. continuously

opportunity in a sector and innovative activity by firms (investments made and results obtained) and how this relationship is influenced by the presence of Absorptive Capacity.

knowledge have a very strong influence on the innovative activity of German manufacturing companies. When absorptive capacity is included there is an increased probability that the firm will carry out R&D actions. There is a positive relation between absorptive capacity and output of innovations

Stock, et al., (2001)

Firms that between 1976 and 1993 developed modems and brought them onto the market

R&D Spending/Sales

The relationship between absorptive capacity in a company and its efficiency in the process of developing new products

The relationship between absorptive capacity and efficiency in developing new products is not linear. An inverted U curve is found, suggesting diminishing returns for absorptive capacity.

Nieto and Quevedo (2005)

Survey of 2030 Spanish industrial firms which received the financing from Spanish CDTI (Center for the Development of Industrial Technology)

Spending on R&D/Volume of sales; The artificial variable (TO) was created, adopting a value of 1 for firms belonging to the first group (high level of technological opportunity) and

Firms operating in technological and scientific environments with a high level of technological opportunities put more effort into innovation and with a high level of spillovers put less effort into innovation. Companies with a greater absorptive capacity put more effort into innovation. Absorptive capacity

A positive relationship was proved to exist between technological opportunity and innovative effort, which means that in those science-related research environments presenting the biggest potential for advances, company commitment to innovation is greater. The effect of disincentive for R&D investment by innovating firms (or the effect of replacing internal R&D with external R&D in firms adopting these innovations) is indeed greater than the incentive effect arising from companies’ desire to increase their absorptive capacity when faced with the existence of a



25

of 0 for those in the second (low level of technological opportunity); Total R&D spending in the industry minors the amount of investment conducted by firm itself. Index of absorptive capacity generated by survey results.

exercises moderating effect both over the impact of technological opportunity and of the stock of firm’s spillovers on firm’s innovative effort.

larger amount of ‘freely available’ knowledge. A positive and significant relationship was proved between the variables absorptive capacity and innovative effort. Those firms presenting a higher level of absorptive capacity are more able to use knowledge generated by other companies and so should have a greater ability to obtain profits (since their starting point is more favorable). Thus absorptive capacity is more important in determining the efforts put in innovation by firms. No moderation effects were observed by absorptive capacity on the impact of the stock of firm’s spillovers. Although such effects were observed on the impact of technological opportunities on firm’s innovative effort, it has a negative sign, which means higher levels of absorptive capacity will lead companies to put more effort into innovation, relying on their own resources and to some extent doing without the greater or smaller range of opportunities offered by the environment.



26

Appendix B: Questionnaire Applied in the Empirical Study

Questionnaire: The Organizational Learning Capabilities (The information provided by you will be kept strictly confidential!)

Part I Measurement of Organizational Learning Capabilities Point-scale explanation: 1= “Completely disagree”, 2= “Disagree”, 3= “Moderately disagree”, 4= “Neutral”, 5= “Moderately agree”, 6= “Agree”, 7= “Completely agree”.

A) Links between firm and surrounding environment 1. We often obtain the technology by getting external licenses.

Completely disagree 1 2 3 4 5 6 7 Completely agree

2. We have developed new products and/or processes in collaboration with other firms.


3. We are well aware of the new technologies presently developed by our competitors.


4. We are a technology supplier to other firms/organizations.


5. We periodically approach other sources (consultants, customers, universities, trade-fairs and etc.) to look for new opportunities for developing new products.


B) Level of knowledge and experience of the organization. 6. Organizational training is conducted frequently.


7. We are able to adapt to the external technologies.


8. We develop our new products based on the existing competitors’ products and processes.


9. Most of the time we are ahead of our competitors in developing and launching new products.


10. We are able to develop the novel technology completely by ourselves.


11. We are able to successfully introduce the novel technology to the market.



28


C) Diversity and overlapping of knowledge structures. 12. Our organization has a flat organizational structure.


13. The level of co-ordination between the various departments in our firm is very low.


14. Most of our employees have a wide range of working experience.


15. Most of our employees have a wide range of professional education background.


16. We specialize in a small number of technologies.


17. Most of the projects for new product development are carried out by the employees from different departments working as a team.


D) Strategic posture Point-scale explanation: 1= “Completely unimportant”, 2= “Unimportant”, 3= “Moderately unimportant”, 4= “Neither unimportant nor important”, 5= “Moderately important”, 6= “Important”, 7= “Completely important”.

18. Achieving maximum product quality.

Completely unimportant 1 2 3 4 5 6 7 Completely important

19. Efforts aimed at developing new products


20. Efforts aimed at reducing costs.


21. Improving existing products.


22. Efforts to maintain & improve the firm’s image.


23. Maximizing market share.


Part II the Details of Operational Figures



29

24. What is the name of the firm you are currently working for?

25. What is the average annual sales of your firm (In Euro) in the past 3 years?

26. What is the average annual number of the employees of your firm in the past 3

years?

27. What is the average expenditure of your firm on Research and Development in

the past 3 years?

28. What is your general percentage of sales generated by new products developed

in the past 5 years?

Part III the Characteristics of the Respondents

29. What is your job position within the firm?

30. We would kindly like to share your research results with you. Would you please

provide us your email address for the feedback? If yes, your email address:

Appendix C: BIK company code of Dutch machine-manufacturing

Industries 2875 Manufacture of general metal products 29 MANUFACTURE OF MACHINERY AND EQUIPMENT 291 Manufacture of machinery for the production and use of mechanical power 2911 Manufacture of engines and turbines 2912 Manufacture of pumps and compressors 2913 Manufacture of taps 2914 Manufacture of bearings 292 Manufacture of other general purpose machinery 2921 Manufacture of furnaces and furnace burners 2922 Manufacture of lifting and handling equipment 2923 Manufacture of non-domestic cooling and ventilation equipment 2924 Manufacture of other general purpose machinery incl. their parts n.e.c. 293 Manufacture of agricultural and forestry machinery



30

2931 Manufacture of agricultural tractors 2932 Manufacture of other agricultural and forestry machinery 294 Manufacture of machine-tools

2941 Manufacture of electrical tools 2942 Manufacture of machine-tools for metal working 2943 Manufacture of machine-tools (not for metal working)

295 Manufacture of other special purpose machinery 2951 Manufacture of machinery for metallurgy 2952 Manufacture of machinery for mining 2953 Manufacture of machinery for food 2954 Manufacture of machinery for textile 2955 Manufacture of machinery for paper and paperboard production 2956 Manufacture of other special purpose machinery n.e.c. 296 Manufacture of weapons and ammunition 2960 Manufacture of weapons and ammunition 297 Manufacture of domestic appliances 2971 Manufacture of electric domestic appliances

2972 Manufacture of non-electric domestic appliances 3330 Manufacture of equipment for guarding and controlling industrial processes

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Enclosed the SPSS Processing Results One-way Anova: processing for Technological Opportunities

Means Plots



35

Regression processing for ACAP 1



36



37

Charts

Regression processing for ACAP 2



38



39

Charts

1. Linear Regression for H1: ACAP and IRD

Scatter Plot



40

Regression

Charts



41

2. Linear Regression for H2: IS and IRD

Scatter Plot 1

Regression



42



43

Charts

Scatter Plot 2: after filtering one outlier at IS <=8 000 000

Regression 2



44



45

Charts

3. Regression for the final H3: IS and ACAP

Scatter Plot



46

Regression



47

Charts

4. Regression for the final H4: IS and d_TO

Scatter Plot



48

Regression



49

Charts

5. Linear Regression for H5: IS an ACAP*d_TO

Scatter Plot



50

Regression



51

Charts

6. Linear Regression for the final H6: IS and acap*ird

Scatter Plot 1



52

Regression 1



53

Charts

Scatter Plot 2: after filtering out one outlier IS <= 8 000

000



54

Regression 2



55

Charts

7. Multi-regression for H7: IS and IRD, d_TO

Regression 1



56



57

Charts



58

Regression 2: after filtering out one outlier IS<6 000 000



59

Charts



60



61

8. Multi Regression for H8: the whole model

Regression 1



62

§

Charts



63



64



65

Regression 2: after filtering out one outlier (Philips) at

IS<= 8 000000



66



67

Charts



68



69



70

Absorptive capacity

Documents

technological firms

rd spillovers

success of innovation

generated knowledge

external environment

higher absorptive capacity

firm size

external factors