Mergers and Acquisitions: The Influence of Distance on Innovation Performance of Companies in High-Tech Industries

Mergers and Acquisitions:

The Influence of Distance on Innovation

Performance of Companies in High-Tech Industries

Author: Cees Leijten

Student Number: S2041995

E-mail: c.p.leijten@student.rug.nl

University: University of Groningen

Faculty: Economics and Business

Master: Strategic Innovation Management

Supervisor: K.J. McCarthy

Second supervisor: F. Noseleit

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Mergers and Acquisitions:

The Influence of Distance on Innovation Performance of Companies in

High-Tech Industries

Cees Leijten

Dr. K.J. McCarthy

, Dr. F. Noseleit

S2041995; Masters student MSC BA Strategic Innovation Management b

First Supervisor c

Second Supervisor

Abstract

Mergers and acquisitions (M&As) took a glance in the last decades and became an important tool for high-tech firms to gain external knowledge in order to improve innovation performance. However a M&A is not without risks, uncertainties and challenges and these dark sides increases when the distance increases, however distance could also enhance organizational learning and innovation. This study examines the influence of four distant perspectives on innovation performance for post M&A firms. These distant perspectives are cultural, institutional, technological and geographical. The hypotheses are tested with an event study of 1820 global M&A deals from 2003 till 2008 whereby the number of granted patents indicate the innovation performance of the firms. The results in this research show that technological distance is positive related to innovation performance. There is a negative relation between geographical distance and innovation performance by a domestic deal, this relation is not significant at a cross border deal. Cultural distance has a negative influence when both firms are active in different industries. There is no significant result for the influence of institutional distance on innovation performance. An important implication for manager in order to improve innovative performance is to acquire a (domestic) firm with a different technological specialization that is not located too far away.

Keywords: merger and acquisitions, innovation performance, distance, culture, institutional,

technology, geographical, high-tech firms, event study

1. Introduction

A M&A is a complicated event whereby only half of the firms succeed in reaching their objectives (Aon Hewitt, 2011). To improve M&A outcomes, firms should focus on coordination, communication and bonding between both firms, these dimensions are called alliance management capabilities (Schreiner, Kale, Corsten, 2009). It is likely that these capabilities become more challenging when the distance and differences between both firms is larger. For example, it is likely that large differences in economical and cultural structures between the parent country and the target country are hard to manage (Filipovic, Prodrug, and Prester, 2012; Aguilera and Dencker, 2004). Furthermore also in a domestic setting it is likely that for example geographical or technological distance could have an impact on innovation performance.

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and Duyster, 2002). These firms operate in industries that are primarily knowledge driven and characterized by a rapidly changing environment with a high speed of new product developments (OECD, 1997, Cloodt, Hagedoorn, and Van Kranenburg, 2006; Makri, et al, 2010). The OECD (1997) identified four groups of industries with a high degree of technological intensity, these industries are (1) Aerospace, (2) Computers and office machinery, (3) electronics-communications, and (4) pharmaceuticals. Aim of this study is to examine the influences of distance and differences on post-M&A innovative performance of acquiring firms in these high tech industries.

M&A’s became a popular research topic and is often part of the strategic alliances research topic (Villalonga and McGahan, 2005). Research on strategic alliances focus mainly on the motivation to form alliances (i.e. Villalonga and McGahan, 2005; Hitt, Dacin, Levitas, Edhec, and Borza, 2000; Lavie and Rosenkopf, 2006) or the performances of such alliances (i.e. Afuah, 2000; Faems, Janssens, Madhok, and van Looy, 2008; Lahiri and Norayanan, 2013). However, not so much research is done about the influence of distance and differences on innovation performance. This research contributes to the literature in several ways. First, we take four different kinds of distance into account. These four distant perspectives are geographical, technological, cultural, and institutional. Although some researcher focused on single distance perspective (i.e. Sampson, 2007, Grote and Umber, 2006), this research is the first who takes all these distant perspectives together into account. Second, this research takes the interaction effect of a domestic deal into account for the geographical and technological distance. Since the results of an international deal can be blurred by exploiting motives from the acquiring firm (Hitt, et al. 2000), the results of this interaction effect could give us a better understanding about the effects of technological and geographical distance. Third, patent applications for both the acquiring as well as the target firm are used to define the innovation performance. Finally, to have more viable results, this research looks at M&As that took place from 2003 till 2008. More years in our sample reduces the chance of having a bias. This research helps us understanding what a M&A deal influences, so that managers can make better decisions in order to improve their innovation performance. Furthermore, this research will also provide new starting points for researcher to do research about the influence of distance on post M&A firms’ performance.

The distance between the acquiring company and the target company will be measured in four ways. First the geographical distance, measured by GPS coordinates. Second is the cultural distance, this is measured by the six dimensions of Hofstede (2011). Next is the institutional distance and at last is the technological distance. Contribution to the literature is the link between the different dimensions of distance to innovation performance. Innovation performance is measured via an event study based on the number of patent applications per year. We test our hypotheses by using a sample of 1820 M&A deals in the high-tech industry between 2003 and 2008.

2. Theoretical Framework and Hypothesis

a. Mergers and Acquisitions

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success of a M&A can be defined. Next we formulate the hypothesizes by relating the different distance dimensions and innovation performance.

b. M&A motives

M&As are often an important part of firms’ growth strategy, especially for firms which have enough financial resources and which perform above average (Filipovic, et al, 2012, Dutta, 2011). Koza and Lewin (1998) summarizes several reasons to form alliances, these are explorative or exploitative by nature. The difference between this distinction is that the former is focused on knowledge-generating R&D activities and that the latter one is focused on knowledge-leveraging marketing activities. Lavie and Rosenkopf (2006) argue that firms try to balance between both options in their alliance formation strategy, because both short-term productivity as well long-term innovativeness are essential for organizational success and survival. Since companies in high-tech industries operate in rapidly changing environments that is driven by knowledge, they focus more on R&D (Makri, et al. 2006). These companies with a high R&D focus are explorative by nature and their motivation to acquire a firm is not only to eliminate competition but also to stay innovative by acquiring new knowledge and capabilities (Dutta, 2011; Cummings and Holmberg, 2012).

To make use of this external knowledge, firms have to absorb, diffuse, and exploit this knowledge. This is called the absorptive capacity of a firm and is positively related to a higher level of R&D intensity (Cohen and Levinthal, 1990), a higher level of openness (Laursen and Salter, 2004) and these firms have twice as much R&D agreements than other firms (Segarra-Blasco, Aurauzo-Carod, 2005). Although these agreements lead to access to more and external resources, it has also a downside, because partnerships can potentially damage their own technologies or resources (Katila, Rosenberger and Eisenhardt, 2008) To prevent this, firms make use of informal defence mechanisms, like time to market and secrecy, but prevent their technologies also by formal defence mechanisms. Patents are part of the latter mechanisms and are also a proper measure to define the innovation performance of a company or to define the success of an alliance in high tech industries (Lahiri and Narayanan, 2013; Sampson, 2007).

Firms have to make use of their alliance management capabilities when they will make use of the knowledge, technologies, and capabilities of the target firm. Firms with better capabilities have a higher M&A performance (Schreiner, et al., 2009), however it is also likely that distance and differences between both firms will have influence on the alliance management capabilities and therefore also on innovation performance.

c. On the Role of Distance in Innovative Performance

c.i. Cultural Distance

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creativity and organizational learning (Morosini, Shane, and Singh 1998; Larsson and Risberg 1998; Vaara, et al, 2012).

Since knowledge sharing is a main condition for improving the innovation performance, it is important that those who are involved bond, collaborate, and communicate (Schreiner, et al. 2009). However differences in culture between both firms create the potential for social conflict and social identity building that could lead to ‘us versus them’ thinking (Vaara, et al., 2012). The acquiring firm first has to resolve these social conflicts and develop mutual trust before they can take advantage from the differences between both firms (Monin, Noorderhaven, Vaara, and Kroon, 2013). In this research we look at the influences of distance on the M&A post performance only in the first four years. Because we assume that firms first have to focus on developing mutual trust and improve the relationship before they can take advantage from their differences, we hypothesize that:

H1 The cultural distance between the target firm and the acquiring firm has a

negative effect on the innovation performance.

c.ii. Institutional Distance

Institutional distance is a concept for understanding differences between markets (Jansson, Hilmersson, and Sandberg, 2009). These differences can become obstacles in the internationalization process (Jannson and Hilmersson, 2012) and have negative influences on the financial performance of alliances (Pattnaik and Choe 2007). Kostova (1999) argues that this is because institutional distance hinders the transfer of strategic organizational practice and knowledge and a higher distance increases the likelihood of a misfit between the practices and environments, furthermore employees will be less reluctant to implement other practices or use each other knowledge. Therefore, we hypothesize:

H2 The Institutional distance between the target firm and the acquiring firm has a

negative effect on the innovation performance.

c.iii Technological Distance

The target company’s technological experience has influence on the technological performance of the acquiring company (Hagedoorn and Duysters, 2002). For instance, more similarities in the technological experience facilitate incremental innovations, because it is easier to assimilate the knowledge and exploit it commercially (Cohen and Levinthal, 1990). On the other side, when there are greater differences, but more complementarities it is more likely that a disruptive innovation will occur, because dissimilarities can be complementary and this leads to more incentives to explore new opportunities (Makri et al., 2006). This is also in line with Cloodt, et al (2006), they argue that acquiring a diverse and complementary external knowledge base contributes to the innovative performance of the acquiring firm.

Therefore we hypothesize.

H3 The technological distance between a target and an acquirer is positively related

to the innovation performance

c.iv Geographical Distance

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information asymmetry costs will increase as well. Furthermore, it is more difficult to implement the new business philosophy and transfer tacit knowledge (Coval and Moskowitz, 1999). Acquiring firms will also face these challenges at their target company, since both companies are not located on the same spot. There is more evidence that the geographical distance has effect on the performance of firms. Grote and Umber (2003) argue that firms prefer a proximate target, because distance should have a negative effect on the deal.

The success or failure of a M&A is not directly related to the innovation performance or vice versa. As already mentioned, a firm can acquire another firm for several reasons, like knowledge, capabilities, and resources. An advantage of a large geographical distance could be that it leads to more incentives to employ opportunities. Firms have a lot of knowledge about their home market, therefore it could be that acquiring firm’s aim is to explore the ideas and knowledge of the target firm at a cross-border acquisition (Hitt, et al., 2000; Lavie and Rosenkopf, 2006). However, it is likely that most long distance acquisitions take place to gain efficiency in production and/or to have excess in new market. In this point of view, the exploitation of knowledge and capabilities will be more important than invest in them (Lavie and Rosenkopf, 2006). Therefore, we hypothesize:

H4 The physical distance between a target and an acquirer has a negative effect on

the innovation performance

d. Distance in a Domestic Setting

The step to do an acquisition aboard is for firms more risky than to do a domestic deal. Cultural and institutional differences could make the deal more challenging, which makes it more difficult to take advantage from each other’s knowledge and capabilities. Therefore, we suggest that the effect of technological distance and geographical distance on innovation performance is stronger in a domestic deal.

H5a The effect of technological distance on innovation performance is stronger and more

positive in a domestic setting than in an international setting

H5b The effect of geographical distance on innovation performance is stronger and more

negative in a domestic setting than in an international setting.

3. Methodology

The phenomenon, the influence of distance on innovation performance for post M&A firms, is not fully addressed by the academic literature. As you can see in the literature review part, some research is done on the different dimension but not together and performance is often measured by financial aspects.

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Figure 1. Conceptual model

The conceptual model includes the four distance dimensions that could affect innovation performance. The technological and geographical distance effects are interacted by a domestic deal to get a better understanding of the influence of distance in a domestic setting.

Sample:

Step three is the data collection and data analysis. For this research, we used a database which consist out of 1820 M&A deals between 2003 and 2008 and whereby the target is acquired for 100 percent. Since patents are an important measure for innovation performance, only companies in high tech sectors are taken into account.

Table 1. Distribution of the sample

Ye ar o f t h e M& A d eal N u m b er o f M& A d eals Patent applications SU M To ta l p at en ts 4 y ear s b ef o re M& A 3 y ear s b ef o re M& A 2 y ear s b ef o re M& A 1 y ear b ef o re M& A M & A 1 y ear a fter M& A 2 y ear s after M& A 3 y ear s after M& A 4 y ear s after M& A 2003 218 7409 8948 10294 10040 10361 9633 9780 9108 8684 84257 2004 256 9579 10840 10396 8703 8402 8991 10288 10797 11040 89036 2005 339 14221 14370 14405 13826 12669 11694 10669 9836 9111 110801 2006 349 16671 15230 14186 13179 12668 11103 10195 9920 8805 111957 2007 368 14068 12976 13570 12902 12445 11176 9265 7304 6140 99846 2008 290 15069 15153 14219 12990 13237 12504 9979 6720 3169 103040 Total 1820 77017 77517 77070 71640 69782 65101 60176 53685 46949 598937 Dependent Variable.

The innovation performance of a firm is the dependent variable. The number of patents a firm applies per year indicates the innovation performance of a firm (Acs and Audretsch, 1989). Companies can apply for their patents at the United States Patent and Trademark Office (USPTO)1, this database is used to collect the data about the patents. The data about

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the patents is collected in March and April 2014. The number of patents of the acquiring firm as well as from the target firm from four years before the deal, the year the deal was completed, and four years after the deal are taken into account.

To define the impact of the M&As, we analyzed the results with the event study method. This method is developed to measure the effect of an unanticipated event on stock prices, but it can also be used for other purposes (McWilliams and Siegel, 1997). In this study, we use a standard approach whereby we estimate a model for each firm in the sample based on the patent applications in the four years before the deal. This model gives an estimation about the patent applications in the four years after the deal. The rate is expressed as:

Ra = (Total_Min4 + ... + Total_Min1) / Total_Years

Where:

Ra = the rate of innovation performance of firm a,

Total_Min4 = the number of patent applications four years before the deal Total_Min3 = the number of patent applications three years before the deal Total_Min2 = the number of patent applications two years before the deal Total_Min1 = the number of patent applications one years before the deal Total_Years = total number of years taken into account, in this case: 4.

The above equation gives us an idea how many patents a firm applies each year. The estimation for the following years is expressed as:

Forecast_(x) = (Total_Min4 + … + Total_Min1 + Total_Min0) + (x)*Ra

Where:

Forecast_(x) = the number of expected patent applications (x) years after the deal Total_Min0 is the year of the deal.

With the forecast variable and the actual facts, it is possible to calculate the abnormal results using the following equations:

PERFORM_(x) = Total_Plus(x) – Forecast_(x)

Where:

PERFORM(x) = The innovation performance of the firm (x) years after the deal. Total_Plus(x) = the number of actual patent applications (x) years after the deal. NB: maximum of four years after the deal.

A positive value of the abnormal result, PEFORM_(x), represents a better performance than expected and vice versa. PERFORM_(x) is the dependent variable in this research. In order to get the results, the distant measures of the events are regressed with the dependent variable.

Independent Variables

8 1. Cultural distance

The cultural dimensions of Hofstede (2011) indicate the cultural differences between countries. These dimensions are used independently, however they are also grouped in two ways. The first group includes the “original” four dimensions, (power distance, individualism, masculinity and uncertainty avoidance) this is labeled as distance_culture4. In the other group (distance_culture6) are the four original dimensions supplemented by two new dimensions; Long or short term orientation and indulgence versus Restraint. These latter two dimensions are new dimensions in the Hofstede theory (Hofstede, 2011). The headquarters of both the target company as well the target company are starting points for the Hofstede’s dimensions as well as for defining the geographical distance.

2. Geographical distance.

The geographical distance is measured by the distance in kilometers. To define this distance, we first used the SDC to identify the city in which the acquiring and target firms’ headquarters are based. For each of the 1.820 deals, we then identify the GPS coordinates of both the target and the acquiring firm, using GPS Visualizer2. After this step we used the Havershine formula to calculate the kilometer distances between the target and the acquirer. The Havershine calculates the greater-circle distance between two points, in kilometers, over the earth’s surface3.

3. The institutional distance

This is based upon the Institutional Profiles Database (IPD) 20094. This database covers 123 countries and measures countries’ institutional characteristics. IPD’s structural framework has nine institutional functions and four institutional sectors. In this research we use the values of the four institutional sectors; A, Public institutions and civil society. B, Market for goods and services. C, Capital market. D. Labor market.

4. Technological distance

The Standard Industrial Classification Codes (SIC) indicate the type of business were a firm is active in5. This SIC code consist about five numbers. The first two digits indicate the major industry group, the third digit indicates the industry, and the last two digits indicate the specific category where the industry is active in. In this research we measured the technological distance by the first and the second digit. In other words, is there a difference in innovation performance if both firms are active in the same industry (group) or if they are active in different industry(groups).

Control Variables

We also control for multiple variables that may influence innovation performance, these variables are collected via Thomson Reuters DataStream Professional in May 2014. The SEDOL code is a unique number for each firm, this is used as the input. Since DataStream has not every firm in their database, the sample decreased in size. Only data from the acquiring firm in the year that the deal was completed is used for the control variables.

3 _{http://wordpress.mrreid.org/2011/12/20/haversine-formula/} 4 _{http://www.cepii.fr/institutions/EN/ipd.asp}

9 a. Employees

The number of employees indicates the size of the firm. Larger firms can take advantages from their scale that could influence their performance. In this research we control for the size of the acquiring firm on innovation performance

b. Free Cash Flow

Another way to measure the financial performance is by subtracting capital expenditures form the operation cash flows. The free cash flow represents the cash that a company has to spend on for example R&D, therefore it is used to control innovation performance.

c. Return on Assets

The return on assets is an indicator for the efficiency and profitability of a firm. This is an important success indicator for firms and could also affect innovation performance.

d. R&D

A higher focus on R&D influences the innovation performance of the firm (Cohen and Levinthal, 1990). The costs of R&D as a percentage from the total sales indicate the R&D intensity and is one of the control variables in this research.

e. Market/Book Ratio

Companies can be evaluated and compared with other companies by financial ratios. The market/book ratio measures the relative value of company compared to its the stock prices. Most important assets for firms in the high tech industries are human’s capabilities and physical assets. Therefore, these companies have mostly a higher market to book ratio and these capabilities have a large influence on the innovation performance.

f. A_SIC_2d / A_SIC_1d

This is the industry or industry group where the acquiring company is in. Some industries or industry groups are more innovative than the others.

g. T_SIC_2d / T_SIC_1d

This is the industry or industry group where the target company is in.

Analysis

At last we will analysis the results to test the hypothesis. We test for linear relationship between performance and distance. In this research we used the ordinary least squares (OLS) regression to analyze these relationships. Furthermore, we used interaction models to test the domestic effects of distance on innovation performance.

4. Results

Table 2 provides descriptive analyses of all the variables used in this research. Table 3 till 12 display the results of models predicting the influence of distance on innovation performance.

Table 2. Descriptive statistics

Variable Obs Mean Std, Dev, Min Max

10 diff_ltowvs 1803 0,977 1,912 0 1,158 diff_ivr 1760 1,035 2,326 0 2,835 distance_c~6 1734 1,031 1,556 0 9,881 distance_c~4 1779 1,085 1,766 0 14,649 diff_public 1755 0,042 0,091 0 1,664 diff_goods 1755 0,043 0,107 0 1,113 diff_capital 1755 0,059 0,146 0 2,245 diff_labor 1755 0,132 0,379 0 3,102 distance_i~l 1755 0,069 0,154 0 1,726 DUMMY_SAM~2d 1818 0,502 0,500 0 1 DUMMY_SAM~1d 1818 0,396 0,489 0 1 dif_SIC_2d 1818 1,203 1,724 0 61 dif_SIC_1d 1818 1,272 1,840 0 6 Distance_g~c 1807 3058286 4056025 0 18572,0 Interaction domestic 1820 0,475 0 0 1 Control EMPLOYEES 761 37343,920 90945,970 2 475000

Free Cash Flow 605 -2,897 0,966 -6,540 4,595

RETURN ON ASSETS 808 -3,559 75,513 -1326,470 413,040 R&D 662 881707 1868276 -118 9014635 T_SIC_1d 1818 3,751 2 0 8 A_SIC_1d 1820 2,548 0,498 2 3 T_SIC_2d 1818 4,339 18,645 2 89 A_SIC_2d 1820 32,297 3,924 28 37 a. cultural distance

Table 3, table 4, and table 5 show the regression results of cultural distance. As you can see in table 3, only the masculinity dimension shows significant results in all the years (p<0.01/p<0.05), the effect of masculinity distance is negative related to performance. Some of the other dimensions are positively related and some are negatively related to innovation performance. Although the results are not significant, it is remarkable that the performance increases at most dimensions. Only the first year (PERFORM_1) and fourth year (PERFORM_4) after the deal is shown in this table, the other two years show comparable results and the performance develops equally between the first and fourth year.

There is a significant result (p<0.1) if we interact the combination of all the six cultural dimensions or the four ‘original’ cultural dimensions with technological differences on industry level (DUMMY_SAME_TECH_1d). Table 4 and table 5 show that there is a significant (p<0.1) and negative relation with innovation performance in the first year after the deal (PERFORM_1) if we interact cultural distance with technological distance. The results are still negative in the second till the fourth year after the deal (PERORM_2 till PERFORM_4), however they are not significant. Taken together the results of the direct regression analysis and interaction analysis, we can say that hypothesis one is partial supported.

b. institutional distance

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Table 3. Regression analysis Hofstede’s cultural dimensions, first and fourth year after M&A deal

1 2 3 4 5 6 7 8 9 10 11 12

VARIABLES PERFORM_1 PERFORM_4 PERFORM_1 PERFORM_4 PERFORM_1 PERFORM_4 PERFORM_1 PERFORM_4 PERFORM_1 PERFORM_4 PERFORM_1 PERFORM_4

diff_pdi 0.134 2.643 (1.747) (4.730) diff_idv -0.134 -0.116 (2.167) (5.868) diff_mas -3.395*** -8.041** (1.151) (3.169) diff_uai -0.330 4.040 (1.743) (4.716) diff_ltowvs 0.640 4.251 (1.894) (5.134) diff_ivr -0.363 3.425 (1.732) (4.617) EMPLOYEES 0.000117 0.000319 0.000118 0.000334* 0.000109 0.000319 0.000119 0.000328 0.000118 0.000338* 0.000119 0.000336*

(7.48e-05) (0.000203) (7.41e-05) (0.000201) (7.34e-05) (0.000200) (7.41e-05) (0.000201) (7.34e-05) (0.000199) (7.50e-05) (0.000200) Free Cash Flow 11.30* 37.99** 11.38* 39.01** 12.92** 42.92** 11.45* 37.68** 11.06* 37.48** 11.57* 39.57**

(6.588) (17.84) (6.580) (17.83) (6.515) (17.70) (6.579) (17.81) (6.482) (17.58) (6.616) (17.64) Return on

Assets 0.186 -0.354 0.182 -0.409 0.120 -0.541 0.180 -0.361 0.205 -0.357 0.204 -0.343

(0.658) (1.783) (0.658) (1.783) (0.651) (1.769) (0.658) (1.780) (0.650) (1.764) (0.664) (1.770) R&D -1.07e-05*** -2.18e-05** -1.08e-05*** -2.22e-05** -9.66e-06** -1.99e-05* -1.08e-05*** -2.19e-05** -1.07e-05*** -2.18e-05** -1.09e-05*** -2.35e-05**

(3.81e-06) (1.03e-05) (3.80e-06) (1.03e-05) (3.78e-06) (1.03e-05) (3.80e-06) (1.03e-05) (3.75e-06) (1.02e-05) (3.83e-06) (1.02e-05) A_SIC_1d -19.25* -59.29** -19.31* -61.13** -16.93 -55.78* -19.34* -61.23** -19.36* -62.10** -20.13* -64.80** (10.76) (29.15) (10.71) (29.00) (10.62) (28.84) (10.69) (28.94) (10.48) (28.41) (10.76) (28.68) T_SIC_1d 3.699 -0.388 3.685 -0.617 3.680 -0.833 3.694 -0.696 3.859 0.0189 4.050 1.138 (2.451) (6.653) (2.447) (6.644) (2.423) (6.596) (2.447) (6.638) (2.424) (6.586) (2.516) (6.725) Constant 65.35* 239.8** 66.15* 252.3** 70.17* 263.9*** 66.75* 242.6** 63.36* 240.7** 67.75* 251.1** (38.36) (103.9) (37.60) (101.9) (37.16) (101.0) (37.71) (102.1) (36.50) (99.02) (37.82) (100.9) Observations 458 457 458 457 458 457 458 457 466 465 451 450 R-squared 0.036 0.029 0.036 0.029 0.054 0.042 0.036 0.030 0.036 0.030 0.037 0.035

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Table 4. Results regression analysis distance_culture6

13 14 15 16

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

distance_culture6 1.937 2.879 2.681 3.253 (1.529) (2.290) (2.059) (3.333) c.distance_culture6#c.DUMMY_SAME_TECH_1d -14.72* -16.93 -17.71 -14.91 (6.668) (10.39) (18.10) (21.98) DUMMY_SAME_TECH_1d 32.29* 47.15* 59.24 62.26 (12.75) (19.40) (34.20) (35.97) EMPLOYEES 0.000128 0.000273 0.000357 0.000350 (0.000124) (0.000192) (0.000251) (0.000308)

Free Cash Flow 14.21 22.85 36.43* 43.18**

(12.30) (18.02) (17.91) (14.98)

RETURN ON ASSETS 0.137 0.122 -0.311 -0.458

(0.672) (1.080) (1.158) (1.122)

R&D -1.12e-05 -1.89e-05 -2.46e-05 -2.46e-05

(6.35e-06) (1.08e-05) (1.56e-05) (1.95e-05)

A_SIC_1d -18.26** -32.62** -43.66** -60.46** (5.548) (10.06) (14.09) (16.80) T_SIC_1d 1.071 -2.676 -7.476 -8.791 (4.400) (8.408) (12.67) (15.89) Constant 74.28* 139.1 217.8** 273.3** (34.25) (69.42) (84.70) (98.12) Observations 443 443 443 442 R-squared 0.055 0.050 0.045 0.038

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 5. Results regression analysis distance_culture4

17 18 19 20

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

distance_culture4 1.332 1.893 1.582 1.896 (1.733) (2.378) (2.376) (3.518) c.distance_culture4#c.DUMMY_SAME_TECH_1d -14.23* -16.44 -20.62 -20.49 (7.071) (10.81) (18.48) (21.77) DUMMY_SAME_TECH_1d 32.12* 59.05** 82.99* 95.42** (12.75) (21.79) (35.63) (36.57) EMPLOYEES 0.000128 0.000268 0.000352 0.000347 (0.000122) (0.000194) (0.000251) (0.000315)

Free Cash Flow 14.03 22.03 35.98* 43.21**

(12.32) (17.93) (17.71) (14.94)

RETURN ON ASSETS 0.0978 0.0889 -0.374 -0.502

(0.688) (1.154) (1.242) (1.221)

R&D -1.06e-05 -1.77e-05 -2.29e-05 -2.24e-05

(6.19e-06) (1.10e-05) (1.59e-05) (2.07e-05)

A_SIC_1d -17.11** -27.77** -36.04** -49.63** (5.083) (9.740) (13.77) (16.98) T_SIC_1d 0.634 -6.066 -12.57 -15.67 (4.101) (8.074) (11.89) (14.84) Observations 458 458 458 457 R-Squared 0.059 0.051 0.048 0.040

13

Table 6. Results regression analysis distance institutional factors, in the first, third and fourth year after the M&A.

21 22 23 24 25 26 27 28 29 30 31 32

VARIABLES PERFORM_1 PERFORM_3 PERFORM_4 PERFORM_1 PERFORM_3 PERFORM_4 PERFORM_1 PERFORM_3 PERFORM_4 PERFORM_1 PERFORM_3 PERFORM_4

diff_public 1.322 -23.45 20.65 (57.38) (123.7) (154.0) diff_goods -0.164 14.98 70.65 (42.70) (92.05) (114.5) diff_capital 7.501 1.064 46.01 (34.22) (73.77) (91.82) diff_labor 4.110 11.34 23.22 (11.81) (25.47) (31.69) EMPLOYEES 0.000115 0.000321* 0.000296 0.000115 0.000318* 0.000293 0.000116 0.000319* 0.000300 0.000112 0.000311* 0.000281

(7.59e-05) (0.000164) (0.000204) (7.58e-05) (0.000163) (0.000204) (7.57e-05) (0.000163) (0.000204) (7.62e-05) (0.000164) (0.000205) Free Cash Flow 10.66 29.39** 34.20* 10.68 28.84* 33.41* 10.48 29.04** 33.32* 10.55 28.71* 33.76*

(6.834) (14.73) (18.35) (6.820) (14.70) (18.30) (6.844) (14.75) (18.37) (6.798) (14.65) (18.24) Return on

Assets 0.219 -0.107 -0.221 0.220 -0.116 -0.182 0.226 -0.121 -0.171 0.218 -0.126 -0.218

(0.671) (1.447) (1.802) (0.671) (1.446) (1.799) (0.671) (1.446) (1.801) (0.670) (1.445) (1.799) R&D -1.05e-05*** -2.21e-05*** -2.02e-05* -1.05e-05*** -2.20e-05*** -2.03e-05* -1.06e-05*** -2.20e-05*** -2.05e-05* -1.04e-05*** -2.17e-05*** -1.97e-05*

(3.88e-06) (8.37e-06) (1.04e-05) (3.88e-06) (8.37e-06) (1.04e-05) (3.88e-06) (8.37e-06) (1.04e-05) (3.89e-06) (8.39e-06) (1.05e-05) A_SIC_1d -19.72* -42.14* -56.36* -19.71* -42.12* -54.44* -19.63* -42.46* -55.64* -19.49* -41.87* -54.87* (11.13) (23.99) (29.87) (11.14) (24.02) (29.91) (11.10) (23.94) (29.80) (11.12) (23.96) (29.83) T_SIC_1d 3.932 -0.700 -0.747 3.931 -0.679 -0.680 3.927 -0.695 -0.784 3.975 -0.575 -0.510 (2.526) (5.446) (6.798) (2.527) (5.447) (6.796) (2.526) (5.447) (6.796) (2.529) (5.452) (6.802) Constant 63.80 185.7** 225.7** 63.89 182.3** 215.0** 62.62 184.5** 219.0** 62.16 180.0** 217.0** (38.93) (83.93) (104.6) (39.47) (85.09) (106.0) (39.26) (84.63) (105.4) (39.15) (84.39) (105.1) Observations 442 442 441 442 442 441 442 442 441 442 442 441 R-squared 0.034 0.029 0.024 0.034 0.029 0.024 0.034 0.029 0.024 0.034 0.029 0.025

14 TECHNOLOGICAL DISTANCE

There are two ways to interpret the technological distance. The first option is by defining if there is a difference or not. This is measured by DUMMY_SAME_TECH_1d for the first digit (difference in industry group) and by DUMMY_SAME_TECH_2d for a difference in the first two digits (difference in industry). If both companies are active in the same industry(group), than the value of the variables is zero, otherwise it is one. It is also possible to measure the difference between the technology by subtracting the SIC codes of both firms from each other. Although the SIC codes are nominal and an individual SIC code doesn’t have a value, it is possible to measure a distance between them. The SIC codes are categorized in a logical sequence, so how larger the difference how more technological distance there is. This is the other option to define the technological distance.

Table 7 shows the results of the regression analyses with the dummy variable of a difference between industries. There is direct positive and significant (p<0.05) result in the third and fourth year (PEFORM_2 and PERFORM_4) after the deal. This indicates that a difference in industry between the acquiring firm and the target firm improves the innovation performance. Table 8 shows the regression effect if there is difference in technology on a two-digit level. These results are not significant.

Table 7 Regression analyses difference on the first digit level

33 34 35 36

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

DUMMY_SAME_TECH_1d -4.975 23.35 48.81* 72.68* (6.820) (15.23) (23.72) (31.43) c.DUMMY_SAME_TECH_1d#c.domestic 46.30** 32.31 9.467 -23.59 (13.43) (19.42) (33.45) (54.75) domestic -12.43 -7.438 -4.476 6.320 (6.723) (15.10) (17.77) (27.85) EMPLOYEES 0.000120 0.000257 0.000334 0.000323 (0.000127) (0.000197) (0.000255) (0.000316)

Free Cash Flow 14.12 20.81 31.24 36.59**

(11.18) (16.13) (15.56) (12.15)

RETURN ON ASSETS 0.172 0.206 -0.167 -0.259

(0.683) (1.115) (1.233) (1.165)

R&D -1.04e-05 -1.75e-05 -2.29e-05 -2.21e-05

(6.30e-06) (1.11e-05) (1.61e-05) (2.10e-05)

A_SIC_1d -14.54** -25.86** -35.86** -50.95** (4.163) (8460) (13.33) (17.38) T_SIC_1d 1.792 -4.840 -11.27 -14.06 (3.606) (7.782) (11.55) (14.74) Constant 69.14* 125.8* 194.7** 241.8** (29.15) (57.33) (67.87) (82.58) Observations 469 469 469 468 R-squared 0.048 0.041 0.038 0.033

15

Table 8 Regression analyses difference on the second digit level

37 38 39 40

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

DUMMY_SAME_TECH_2d 0.403 4.963 14.32 16.68 (11.57) (16.09) (18.60) (33.49) c.DUMMY_SAME_TECH_2d#c.domestic 27.09 26.87 23.90 8.309 (23.77) (26.77) (31.36) (44.02) domestic -10.92 -10.97 -14.80 -7.301 (14.95) (26.42) (28.53) (36.07) EMPLOYEES 0.000111 0.000247 0.000321 0.000316 (0.000135) (0.000205) (0.000260) (0.000316)

Free Cash Flow 12.98 20.42 32.05 38.41**

(11.46) (16.95) (16.11) (12.80)

RETURN ON ASSETS 0.161 0.157 -0.272 -0.381

(0.690) (1.157) (1.279) (1.255)

R&D -1.03e-05 -1.71e-05 -2.21e-05 -2.12e-05

(6.51e-06) (1.12e-05) (1.61e-05) (2.07e-05)

A_SIC_1d -18.90*** -33.88*** -46.41** -62.11*** (4.024) (8.010) (11.92) (15.08) T_SIC_1d 2.544 0.367 -3.439 -2.981 (3.778) (7.422) (10.61) (14.47) Constant 72.96* 133.2* 207.6** 254.6** (33.90) (65.68) (74.14) (87.38) Observations 469 469 469 468 R-squared 0.042 0.037 0.035 0.029

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 9. Regression analyses technological distance on the first digit level

41 42 43 44

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

dif_SIC_1d 0.544 18.80* 33.20** 44.78** (6.951) (7.369) (12.45) (15.51) c.dif_SIC_1d#c.domestic 12.48** 9.166 1.130 -8.379 (4.471) (6.957) (11.17) (17.12) domestic -10.12 -6.940 -3.590 5.754 (6.178) (13.95) (16.84) (25.68) EMPLOYEES 0.000114 0.000249 0.000327 0.000319 (0.000128) (0.000198) (0.000255) (0.000317)

Free Cash Flow 14.11 20.53 30.31 35.29**

(10.96) (15.78) (15.24) (11.94)

RETURN ON ASSETS 0.169 0.208 -0.156 -0.244

(0.669) (1.078) (1.177) (1.100)

R&D -1.01e-05 -1.70e-05 -2.25e-05 -2.18e-05

(6.20e-06) (1.09e-05) (1.58e-05) (2.07e-05)

A_SIC_1d -13.64 -13.38 -15.54 -24.40 (7.314) (12.00) (17.25) (21.75) T_SIC_1d -0.0988 -18.15* -32.23** -40.58** (4.143) (7.378) (12.15) (15.10) Constant 71.51* 128.9* 196.6** 242.4** (28.41) (56.23) (66.64) (78.65) Observations 469 469 469 468 R-squared 0.047 0.040 0.037 0.033

16

Table 10. Regression analyses technological distance on the first and second digit level

45 46 47 48

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

dif_SIC_2d 0.518 1.860** 3.137** 4.041** (0.610) (0.685) (1.163) (1.445) c.dif_SIC_2d#c.domestic 1.337** 0.993 0.105 -0.937 (0.460) (0.769) (1.279) (1.937) domestic -10.27 -6.797 -2.809 7.097 (6.056) (13.95) (16.46) (25.02) EMPLOYEES 0.000111 0.000247 0.000326 0.000318 (0.000128) (0.000199) (0.000257) (0.000319)

Free Cash Flow 13.53 19.81 29.49 34.53**

(10.87) (15.61) (14.93) (11.58)

RETURN ON ASSETS 0.176 0.214 -0.150 -0.239

(0.654) (1.067) (1.159) (1.078)

R&D -1.01e-05 -1.71e-05 -2.27e-05 -2.20e-05

(6.17e-06) (1.10e-05) (1.59e-05) (2.08e-05)

A_SIC_1d -11.14 -16.84 -21.81 -33.64 (5.737) (10.79) (16.79) (20.80) T_SIC_1d -4.017 -16.53** -28.16** -33.47* (3.146) (6.026) (10.18) (13.19) Constant 73.04** 131.4* 199.6** 245.4** (27.89) (55.41) (65.75) (78.18) Observations 469 469 469 468 R-squared 0.048 0.040 0.036 0.032

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Table 9 and table 10 show the regression analysis with the distance between technology on the first digit level (dif_SIC_1d) and the second digit level (dif_SIC_1d). These results show positive and significant (p<0.05) results in the second, third and fourth year (PERFORM_2, PERFORM_3, and PERFORM_4) after the deal was completed. These results confirm hypothesis 3. The interaction effect with a domestic deal is significant in the first year, this confirms hypothesis 5b partially.

GEOGRAPHICAL DISTANCE

17

Table 11 Regression analyses geographical distance

49 50 51 52

VARIABLES PERFORM_1 PERFORM_2 PERFORM_3 PERFORM_4

Distance_geographic 0.000369 0.000768 0.000800 0.000882 (0.00202) (0.00291) (0.00398) (0.00500) c.Distance_geographic#c.domestic -0.0116** -0.00362 -0.000392 0.0172 (0.00374) (0.00493) (0.00637) (0.00907) domestic 27.49* 38.24 40.64 39.43 (12.08) (24.21) (28.72) (36.63) EMPLOYEES 6.40e-05 0.000194 0.000259 0.000199 (0.000114) (0.000196) (0.000305) (0.000364)

Free Cash Flow 19.02 25.05 39.05 46.13

(12.22) (23.99) (25.49) (30.11)

RETURN ON ASSETS -0.105 -0.417 -0.660 -0.259

(1.605) (2.033) (2.867) (3.009)

R&D -4.17e-06 -9.07e-06 -1.25e-05 -7.66e-06

(7.68e-06) (1.25e-05) (1.97e-05) (2.37e-05)

Market/Book Ratio -0.000880 -0.00368 -0.00193 0.00723 (0.0119) (0.0236) (0.0342) (0.0417) A_SIC_2d -1.126 -2.884 -4.432* -6.293** (1.043) (1.720) (1.796) (2.192) T_SIC_2d 0.399 0.524 0.649 1.075 (0.625) (1.108) (1.508) (1.736) Constant 65.62 127.3 207.6** 249.6* (33.44) (65.42) (77.27) (111.5) Observations 276 276 276 276 R-squared 0.032 0.021 0.020 0.024

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

5. Discussion

This study offers several insights for the influence of distance on innovation performance for post M&A firms. The results indicate that distance could have influence on the innovation performance in the first four years after a M&A, however it is not always the case.

a. cultural distance

18

alliance management capabilities before they can take advantage of the cultural distance is also supported by these results, because the innovation improves each year for most dimensions, however please note that these results are not significant.

The interaction model shows that culture is negatively related to innovation performance if a firm acquires a firm that operates in another industry. We assume that it becomes too complex to handle both the cultural differences as well as the technological distance in order to improve the innovation performance. These results indicate that managers should take care of cultural distance if they acquire a firm that is active in another industry and should avoid a large difference between masculinity.

b. institutional distance

Innovation performance is not influenced by the distance between institutional factors. We expected that firms should have more difficulties in order to share knowledge and explore their activities if there are more differences between the home-based country and target firm, however this is not approved by our research. Since institutional distance has a negative influence on financial performance (Jansson and Hilmersson, 2012), we assume that firms with market leveraging M&A motivations face more challenges in order to improve their performance than firms with explorative M&A motivations.

c.Technological distance

As expected, the distance between the technologies has a positive influence on the innovation performance. However it is only in the second, third and fourth year after the deal. This could indicate that firms first have to reorganize their activities after a M&A. Firms have to use their alliance management capabilities to integrate and make use of the activities and knowledge from the other firm (Schreiner, et al., 2009). However the technological distance shows a positive and significant result at a domestic deal in the first year after the deal. This supports our assumption that firms first have to focus on their management skills and that this is easier in a domestic setting, because of the absence of language barriers and cultural differences.

d. Geographical distance

Geographical distance has only negative influence on the innovation performance at a domestic deal. This result supports our hypothesis that the effect of geographical distance is stronger in a domestic setting and it partially supports our hypothesis that geographical distance has a negative influence on innovation performance. It is partially supported, because the results are not significant when the deal is international. This result indicates that it doesn’t matter if for instance a Dutch company acquires a company in Spain or a company in Asia, because both companies are too far away to visit those companies as frequently and easy as a domestic alliance partner. The geographical distances makes it thus more challenging to communicate, coordinate and bond between both firms. Based on this finding, we assume that physical contact and visits are an important factor for the success of an alliance.

6. Limitations & future research

19

First, although researchers argued that patents are a good indicator for innovative performance (Acs and Audretsch, 1989), there are some limitations about this dependent variable. First, firms can use different names or use division’s names for their patent application. For example, the holding of the firm acquires the target firm and the acquiring firm integrates the components of the target firm in one or more division, which apply patents under their own name. This problem is also recognized by Lahiri and Naraynana (2013), they covered this problem with the “who owns whom” approach to fully cover all the patents from all the subsidiaries, divisions and joint-ventures that belong to the acquiring firm. We checked as much as possible to search for the right names, however at a sample of more than 1800 firms and the limited time it was not possible to fully cover this.

A second limitations covers the motivations to do a M&A. The innovation performance is an appropriate success factor when a firm is interested in the target’s technology or knowledge. However technology is not the only motivation for M&As in high tech industries, also market exploitative motives could be important for high tech firms (Hagedoorn, 1993). In this latter case, firms will use external resources and external knowledge to leverage their own capabilities in order to exploit their activities (Koza and Lewin, 1998). The financial performance or market shares are more appropriate success indicators for these kinds of M&As. Furthermore, Hagedoorn and Duysters (2002) state that firms prefer M&A’s for exploitative motivations, and that strategic alliances - like joint ventures - are preferred for knowledge generating R&D activities. Since it is not defined in our sample what the aim of the deal is, the results can be more specific if our sample only consist about M&A deals with knowledge driven motives. Furthermore, since many firms have exploitative motivation to acquire another firm, it could also be interesting to have a better understanding about the influence of distance on the financial performance.

Finally, we only looked at the patent applications till four years after the deal and we regressed this with a linear model. Although, the high-tech industry is characterized by a rapidly changing environment (OECD, 1997, Cloodt, et al, 2006; Makri, et al, 2010), it could also be interesting to take a longer period of time to find out if there are concave relationships between distance and performance.

7. Conclusion

The primary insight from this study is that most of the distance dimensions have influence on innovation performance. This study helps managers and academics in understanding these influences. In order to improve innovation performance, managers should prefer domestic and nearby firms, but from another industry to acquire. This research provides not only implications for managers, it is also a foundation for future work on the topic of influence of distance on performance of post M&A firms.

8. Acknowledge

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