To diversify or multiply. A study into the influence of acquisition program breadth and depth on innovation performance.

To diversify or multiply.

A study into the influence of acquisition program

breadth and depth on innovation performance.

By examining 522 acquisitions part of 123 acquisition programs the objective of this paper was to find out if a relationship between acquisition programs and innovation performance exists. Particularly, we were interested in the dimensions breadth and depth of the acquisition program resting on theories for knowledge recombination and organizational learning. We propose opposite effects hypothesis for acquisition program breadth and a positive relationship between knowledge depth and innovation performance. We find that, however, not enough evidence exist to infer this relationship exists. This research concludes with recommendations for further research.

Student: Willem Reinders

Student number: S3856445

Master program: MSc BA Strategic Innovation Management

Supervisor: P. Kuusela

Second assessor: P. Steinberg

Data: 21 August 2020

2

INTRODUCTION

“The real leaders of tomorrow are already working on the next big idea, the one that will drive growth into future.” – Roger Enrico, CEO PepsiCo

Market leaders often produce incremental improvements and stick to core technologies while new entrants are able to introduce radical innovation and become the new leaders in the industry (Mitchell, 1989; Vermeulen & Barkema, 2001). The question arises how firms keep control over their competitive advantage in an industry where (radical) change is required due to pressures outside of the firms control. Moreover, how do incumbent firms survive a transitioning industry? Incumbent firms use acquisitions to reconfigure resources, renew business models, create new capabilities and thereby ultimately innovate (Deeds & Hill, 1996; Park & Meglio, 2019; Stuart, 2000). Acquisitions can revitalize organizations that have the tendency to gradually become rigid (Vermeulen & Barkema, 2001). In recent years, it became clear that single acquisitions did not provide the acquirers with the innovation output it expected (Ahuja & Katila, 2001; Ernst & Vitt, 2000; Hitt et al., 1991). Increasing attention has been dedicated to acquisition programs (Park & Meglio, 2019). An acquisition program can be defined as multiple acquisitions that serve a common goal (Faulkner et al., 2012). Acquisition programs often include acquisitions with significant interdependencies (Chatterjee, 2009). Research indicates that acquisition programs that are heterogeneous and therefore include a lot of different acquisition targets are difficult to manage (Laamanen & Keil, 2008).They argue that the more an acquisition program is focused on familiar industries, the easier the acquisition is managed and therefore more likely to have a positive impact on a firm’s financial performance. The results give great promise to an emerging field of

3 builds on two theories. First, some authors argue that diversity (breadth) increases the ability to access novel and unfamiliar knowledge that can be recombined (Nooteboom et al., 2007). Others argue that a large knowledge pool with a narrow scope (depth) increases the likelihood that a firm can use one or more knowledge components to innovate (Schilling & Phelps, 2007). In addition, organizational learning literature (Laamanen & Keil, 2008) and integration and coordination issues influences the effectiveness of above mentioned theories (Ahuja & Katila, 2001; Cohen & Levinthal, 1989; Kusewitt, 1985; Park & Meglio, 2019).

Laamanen and Keil (2008) state the following:

A failed acquisition may create valuable learning effects that can enhance the overall program-level performance through improved acquisition capabilities more than its direct negative influence. (p. 664)

In previous studies, acquisitions are examined as single events (Ahuja & Katila, 2001). This research aims to contribute to this field of research by examining the innovation performance implications of multiple acquisitions as interrelated events. As stated before, acquisitions are often used to reconfigure resources, renew business models, create new capabilities and therefore innovate (Park & Meglio, 2019). This research will examine the structural dimensions of the acquisition program as a predictor of innovation performance. This research will answer the question;

How can firms benefit their innovative performance by acquisition programs?

This main research question is based on the following sub-questions;

1. How does acquisition program breadth influence innovation performance? 2. How does acquisition program depth influence innovation performance?

4 innovate to sustain their market presence. By examining the performance implications of breadth and depth of acquisition programs, this papers aims to explain how firms can cope with the changing business environment. More precisely, this paper studies how acquisition programs can support an innovation strategy and provide managers with a reason to emphasize either similar or different acquisition targets. Taking a network (or program) perspective is stressed by previous research as it does not only explain more about differential success of firms, it also changes our understanding of sources of competitive advantage (Gulati et al., 2000). The hypotheses are tested using a sample of 522 single acquisitions executed between 2000 and 2018 by 15 firms. The acquisitions are part of 123 programs executed in individual years. Innovation performance is measured based on patents granted and pending applications following previous research (Ahuja & Katila, 2001; Nooteboom et al., 2007). Due to the exploratory nature of the literature we develop opposite effects hypothesis to test our propositions.

Unfortunately we find no evidence to suspect a relationship between breadth and depth of an acquisition program with innovation performance. This research shows that acquisition program success is highly contexts specific and does not show similar results with previous research when subject to different industry characteristics.

LITERATURE REVIEW

Incumbent firms use acquisitions to reconfigure resources, renew business models, create new capabilities and ultimately innovate (Deeds & Hill, 1996; Park & Meglio, 2019; Stuart, 2000).

Acquisitions and innovation

Complementary knowledge

5 when the technological knowledge is sufficiently similar to provide opportunities for learning, but different enough to expose the acquirer to new and divers knowledge” (p. 1762).

Knowledge recombination

Acquisition programs can affect the innovative output through economies of scale and scope. Previous research on post-acquisition innovation output found a positive relationship between absolute size of acquired knowledge base and innovation (Ahuja & Katila, 2001). Ahuja and Katila (2001) stated that: “the number of direct combinations that a firm can create from its own knowledge elements increases with the size of the acquired knowledge base” (p. 200).

It became clear that single acquisitions did not provide the acquirers with the innovation output it expected (Ahuja & Katila, 2001; Ernst & Vitt, 2000; Hitt et al., 1991; Park & Meglio, 2019). Previous research has viewed acquisitions as single entities instead of interrelated events (Ahuja & Katila, 2001). In recent years, increasing attention has been dedicated to acquisition programs (Park & Meglio, 2019).

Acquisition programs and innovation

An acquisition program can provide a solution to the limitations a single acquisition proposes to innovation. Although research on this topic remains scarce, previous research suggests solutions come from the ability to better coordinate the acquisitions (Ahuja & Katila, 2001; Laamanen & Keil, 2008; Park & Meglio, 2019), integrate the acquisitions (Laamanen & Keil, 2008; Meglio et al., 2015; Park & Meglio, 2019) and learning experiences from prior acquisitions (Ahuja & Katila, 2001; Cohen & Levinthal, 1989; Hayward, 2002; Kusewitt, 1985; Park & Meglio, 2019).

Coordination

6 Integration

That acquisition programs can provide the firms with better acquisition outcomes is related to the ability to efficiently coordinate and integrate the acquisition. Although many actors are involved in an integration process, top managers and integration managers are found to be important determinants of acquisition integration success (Meglio et al., 2015). In addition, others found that similarity between acquisition targets can facilitate integration and the assessment of strategic and organizational fit (Laamanen & Keil, 2008).

Organizational learning

Acquisition programs can not only provide the firm with knowledge, it can also enhance the capabilities to absorb the given knowledge (Ahuja & Katila, 2001). Acquiring a larger knowledge base should enhance the firm’s absorptive capacity, which is defined as the firm’s ability to identify, assimilate and

exploit knowledge from their environment (Cohen & Levinthal, 1989). Absorptive capacity represents the learning a firm does by doing (Cohen & Levinthal, 1989). The firm that acquires a knowledge base simultaneously acquires the knowledge that is needed to understand and use the knowledge to innovate (Ahuja & Katila, 2001). Acquisition program experience can enhance the capabilities to execute a program that can subsequently be used to improve the productivity and efficiency of acquired firms (Chatterjee, 2009). In addition, Vermeulen and Barkema (2001) state in their research on organizational learning through acquisitions that acquisitions help the firm restructuring their way of doing things and providing the firm with “a healthy level of doubt” (p. 460). Experiences with varying acquisitions should

make the organizations more flexible and better able to handle changing environments (Vermeulen & Barkema, 2001). Based on the above mentioned mechanisms the following hypothesis is formulated:

H1a: Acquisition program breadth is positively related to firm innovation performance.

7 (Ahuja & Katila, 2001; Vermeulen & Barkema, 2001). High management intensity leads to a lower commitment to innovation (Hitt et al., 1991, 1990). In addition, these managers are considered important determinants of acquisition integration success (Meglio et al., 2015). Therefore, the following opposite effects hypothesis:

H1b: Acquisition program breadth is negatively related to firm innovation performance.

To understand the relationship between the acquisition program depth and innovation we draw on the theory that innovation relies on the recombination of knowledge that is available in the firm (Schumpeter, 1934). Increasing the size of the acquisition program depth by acquiring more similar firms will increase the number of elements with which the firm is able to innovate (Laamanen et al., 2014). A large knowledge pool (depth) increases the likelihood that a firm can use one or more knowledge components to innovate (Schilling & Phelps, 2007). They also argued that similarity between partners increases the information transmission sustaining knowledge transfer. In other research on the behaviour of key inventors of acquired companies, Ernst and Vitt (2000) found that key inventors are less likely to leave when high degrees of technological proximity exists between acquirer and target company, therefore keeping valuable knowledge in the firm. Therefore, the following hypothesis is proposed:

H2: Acquisition program depth is positively related to firm innovation performance.

METHODOLOGY

DATA

To test our hypothesis we use a dataset retrieved from SDC Platinum and Compustat North America, database for financial, statistical and market data. The SDC Platinum database provides easy access to the acquisition deals executed by the firm and Compustat North American company info (e.g. size measures). To identify the acquisition programs we used LexisNexis. LexisNexis provides official press-releases with rationale behind an acquisition deal. If not available other press releases and the firm’s annual reports were used. We used Orbis – Bureau van Dijk to find information on intellectual

8 because the energy industry is currently facing a transitioning period. The Paris Agreement, at the moment of writing signed by 189 parties, is a landmark agreement to minimize the use of high carbon fossil energy systems and to accelerate the use of sustainable, renewable energy systems that produce low carbon. The acceleration of renewable energy systems is generally referred to as the energy transition. The actions taken by the parties to the convention forces firms dependent on fossil fuel systems to renew their business models. In addition, examining a single industry controls for industry effects (Ahuja & Katila, 2001). Acquisition behaviour over a 18-year period is collected, completed between 2000 and 2018. Some firms were very active acquirers while others conducted less acquisitions. The final sample includes 15 publicly listed firms active in the energy industry. The firms in the sample have a total of 522 single acquisitions with a total of 123 (n = 104 since we averaged multiple programs performed in the same fiscal year) acquisition programs. Differences in firm characteristics are controlled for by including these in the model as control variables. Since we are interested in the breadth and depth of acquisition programs, single acquisition are excluded.

Acquisition programs

As stated before takes this research an acquisition program perspective. The importance of acquisitions to sustain innovation is a intensively researched topic. However, the research on acquisition programs is not yet as mature. The process of identifying acquisition programs is done using LexisNexis, press releases and annual reports. Acquisition programs are identified based on similar rationale behind the acquisition or the ‘core logic’ behind the acquisition (Chatterjee, 2009). Quotes from the acquirer and target company were collected from press releases to find the rationale behind the acquisition. This information was complemented with annual reports where the acquirer firms explained the motivation behind certain acquisitions or their short- and long-term goals that explained a certain acquisition activity. To give an example of rationale as stated in the press releases:

"Solar is a natural extension of our business," Paul T. Hanrahan, president and chief executive of AES.

9 "This acquisition continues Constellation's strategy to extend our participation along the energy value chain and expand our business into attractive North American markets," said Clem Palevich, president of Constellation NewEnergy.

Based on the quote above we were able to classify this acquisition part of an acquisition program with the objective of geographical expansion. The acquisitions were categorized based on (1) geographical expansion, (2) growth, (3) complementary business, and (4) diversification. Geographical expansion is defined as acquisitions used to secure presence in a foreign market (or in a geographical location where the firm was not yet present). Growth acquisitions are to expand the presence in a market where the firm is already present. Complementary business is defined as acquisitions that are not similar but related to the firm’s core business. Diversification are acquisitions that were into a completely different market to

grow a new business. Since the completion of an acquisition can take up to several year we used the date of completion to identify in which fiscal year the program took place. Because our analysis is on year-level we calculated the average program scope of the acquisitions executed in the given year.

MEASURES

Dependent variable

Innovation performance. Innovation performance is measured based on the patents granted or pending to the firms during the period of observation. Patent count is a measure used by previous researchers and is a good indicator of innovation output (Ahuja & Katila, 2001; Desyllas & Hughes, 2010; Sampson, 2007; Schilling & Phelps, 2007; Stuart, 2000). Including pending patent applications gave us more variation and less missing values for firm years in which the firm did apply for patents but did not get patents granted. The measure provides an objective measurement since we are focusing on one specific industry in which the patenting opportunities for firms are assumed to be equal. Since patent applications can take up to several years to be granted we used the patent application date to measure the innovativeness of the firm as close to the actual innovative activities.

10 Acquisition program breadth. To measure acquisition program breadth, BREADTHit, we use differences in target four digit industry code (SIC). SIC data is used in number of studies to explain the differences between industry characteristics (Fan & Lang, 2000; T. Laamanen & Keil, 2008; Tomi Laamanen et al., 2014). The four digit codes can be different on a scale from 0 to 4 where 0 means that there are no differences between acquirer and target company, 1 means that one digit is different, 2 means two digits are different, 3 means three digits are different and 4 means all digits are different. This process is done from left to right meaning if the base number is different the other digits are considered different as well (e.g. 4511 is 5511 is different on a 4 level scale). This is because the base number classifies the main industry and specifies with the second, third and fourth number. The breadth of the acquisition program is calculated as the average difference of all the single acquisitions in the program. To control for misinterpretation of patenting activities related to acquisition programs we calculated the average of the acquisitions programs in the same year. This proposes limitations to our results, this will be discussed in the limitations section of the paper.

Acquisition program depth. Acquisition program depth, DEPTHit, is defined as the amount of similar knowledge a firm acquires in an acquisition program. Similar to the acquisition program breadth we use SIC data to calculate the depth of an acquisition program. The ultimate depth of the program is calculated as the percentage of similar firms in an acquisition program (Hayward, 2002), this number is then multiplied with the number of acquisitions in the program. This gives us a high number when more similar knowledge exists in the program. Again, to control for misinterpretation of patenting activities related to acquisition programs we calculated the average depth of the acquisitions programs in the same year. The limitations this proposes will be discussed in a later chapter.

Control variables

11 measured in the total assets (Dang et al., 2018). Total assets is an appropriate measure for size in research where it represents the number of resources from which a firm can create profits (Dang et al., 2018).

Cumulative patent count. Firms build on prior knowledge in their knowledge base (Ahuja & Katila, 2001). To control for a firm’s existing knowledge base we included cumulative patent count prior to the focal year.

Target to target similarity. Similarity between acquisition targets is expected to enhance the success of the post-acquisition integration. In addition, similarity between targets is expected to provide the firm with capabilities to recognize and assimilate valuable knowledge (Ahuja & Katila, 2001; Cohen & Levinthal, 1989). Target to target similarity is measured by comparing the SIC codes (Hayward, 2002; Laamanen & Keil, 2008). The similarity between targets is calculated as the percentage of similar SIC codes in the same acquisition program. For example, in a program where 5 of the 10 acquisitions executed in the same year had similar SIC codes, similarity between targets in that year was 50 percent.

MODEL SPECIFICATION

The sample consists of an unbalanced panel data set of 15 firms. To decide if a random or fixed effects model is more suitable a Hausman test was conducted. The results (Prob>chi2=0.0933) showed us that we cannot reject the null hypothesis. Based on this we decided to use a random effects model. Following previous research the model is specified as follows;

Innovative performanceit = β0 + β1 BREADTHit + β2 DEPTHit + γ Controls β0k + β0t+ ui + εit

where i represents the firm, t is the fiscal year, β0k is the industry fixed effects, β0t is the year fixed effects, and ui + εit is the composite panel error term, including a time-invariant and firm variant error term ui and a fully random error term εit (Estrada & Dong, 2020).

DISCUSSION OF VALIDITY AND RELIABILITY MODELS

12 Mean Std. Dev. (1) (2) (3) (4) (5) (6) Patents (1) 410.7596 1075.758 1.0000 Breadth (2) 2.884409 .7581184 0.2027** 1.0000 Depth (3) 194.1466 263.7998 -0.0833 -0.5788*** 1.0000 Size (4) 9228.649 15302.55 0.4076*** 0.3376*** -0.2593*** 1.0000 Target to target similarity (5) 29.56497 35.61952 -0.2203** -0.4351*** 0.4285*** -0.1930* 1.0000 Cumulative patent count (6) 2257.423 6451.22 0.7494*** 0.2645*** -0.1087 0.4511*** -0.2094* 1.0000

Table 1: Descriptive statistics and correlations. N=104. ***significant at 1%; **significant at 5%; *significant at 10%.

RESULTS

Table 2 reports the random effects regression results. Model (1) is the baseline model. The results show that cumulative patent count have statistically significant and positive effects on firm innovation performance. These results indicate that prior patents can provide the firm with knowledge to build on and increase innovation momentum. Model (2) reports the results including the independent variables acquisition program breadth and acquisition program depth. The results regarding the relationship between acquisition program breadth and innovation performance are negative and insignificant (p = 0.726). Therefore hypothesis 1a, where we expected a positive relationship is not supported. Although being negative there is not enough evidence to suspect our hypothesis 1b, which expects a negative relationship, to be true. Therefore, also hypothesis 1b is not supported.

(1) (2)

Firm size .0056361(.0051935) .6191956(.0378874)

13

Cumulative patent count .1169065(.0123615)* -.000052(1.76e-06)*

Program breadth -43.11398(122.8602)

Program depth .146026 (.3435587)

Constant 146.6215(107.6838) 256.1778(405.8167)

R 0.5708 0.5733

Wald Chi-sq. 133.01 131.66

Table 2: Random effects regression results. N=104. * p<0.01. Standard errors are in parentheses. Dependent variable is firm innovation performance.

For acquisition program depth, we hypothesized a positive relationship with innovation performance. Although being positive, the results for this relationship are also insignificant (p = 0.671) and do therefore not provide enough evidence to reject the null hypothesis. Therefore, hypothesis 2 is also not supported. In both cases our standard error is high relative to the coefficient indicating that our sample is not representing the population very well. A discussion of the results will follow in the next chapter.

Robustness checks

A series of robustness checks are done to help interpret the results. First, a fixed effects model is conducted, the test shows similar results.Second, we tested for the influence of outliers using a robust regression. Although being greater, our hypotheses are not confirmed in this robust model. The results show that size is significant and positively related to firm innovation performance. To test for a non-linear increase in our dependent variable, we tested different functional forms of our independent variables. The results show qualitatively similar results.

(1)

(2)

Fixed effects

Robust

Program breadth

-0.504

-43.11

(47.74)

(59.12)

Program depth

0.0485

0.146

(0.0972)

(0.286)

Size

0.00655

0.00666***

(0.00401)

(0.000947)

14

(0.690)

(3.141)

Cumulative patent count

-0.101***

0.117***

(0.00682)

(0.00488)

Constant

557.5***

256.2

(144.6)

(307.1)

Observations

104

104

R-squared

0.761

F/ Wald chi2

53.41

2515.92

n

15

15

Table 3: Robustness checks. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

Development of propositions

Unfortunately, based on these results our conclusion about the influence of acquisition program dimensions on innovation performance remains limited. Based on this sample there is not enough evidence found to conclude that either breadth or depth of knowledge in an acquisition program can provide the firm with an improved innovation performance or coordination and integration efforts might hamper innovation. The cumulative patent count in found to be significantly related to patents, confirming findings from previous research that prior patents provide the firm with knowledge to build on (Ahuja & Katila, 2001; Cohen & Levinthal, 1989).

DISCUSSION

15 performance of the firms in our sample. Although small, we find that cumulative patents count can enhance innovative performance. Possible explanation for the insignificant results might be found in our sample variation or sample size.

16 Theoretical implications

Although interesting results found in the chemical industry (Ahuja & Katila, 2001), these results seem not to be generalizable for the energy industry based on this research. This research implicates that the energy industry might have different laws for the recombination or complementarity of knowledge to sustain innovation. Acquisition success seems to be highly context specific and subject to influences from their environment not yet examined by current research.

Managerial implications

By highlighting the difficulties of measuring the success of acquisition programs, this research did prove one thing. Acquisition (programs) remain to be context, firm, industry and even time specific phenomena. It is difficult to assess if breadth or depth of acquisition programs can contribute to a firms innovative performance without being able to take the firm specific context into account.

Limitations

As with all research, this paper has its limitations. Given the sample readers should be critical on how to interpret the results. Although being a good indicator of innovation and widely available in open source databases, our dependent variable ‘granted and pending patent count’ does not provide a complete picture of the innovation activities of the firms in the sample. First, patenting strategies of one firm can be different from the other and it is difficult to assign value to a particular patent. One way to overcome this limitation in future research is to take patent citation count into account. Patent citation count should provide more information on the value of the patent to a company. Since we were interested in the innovation performance in a quantitative way we calculated the number of patents granted and applications. In a future study where quality of the innovation is important as well, number of citations can provide a good measure. Second, because of limited data on granted patents, in this research granted patents is complemented with pending patent applications. However, it is difficult to assess if the pending patent applications are indeed successful innovations. Using granted patents only should overcome the issue of firms in sample with an ‘aggressive patenting strategy’ which can be interpreted

17 Regarding our independent variables, the calculation of program breadth and depth is done based on SIC industry identifiers. Although being an easily available method for large scale data analyses, it comes with its limitations. The industry identifiers do explain differences between firms based on the market they are serving. These identifiers do not take into account that firms may be familiar with firms from outside of their industry but part of their supply chain. Contrary, SIC codes do not take into account that firms that serve the same market might be different on a cultural or structural basis. In a later study, the quantitative data could be complemented with a qualitative study that can tell more about the influence of these aspects. Second, our measure for acquisition program breadth does not take access to a variety of knowledge into account, firms may be able to access knowledge needed to innovate without owning this knowledge (and therefore not being part of their industry code). Lastly, since our data analysis is done on year-level. The sample included multiple missing values. These values were dropped and because of this the sample was unbalanced.

Further research

18 single industry. This research results is limited to industries where patents provide a meaningful indicator of innovation. To find out the effects of breadth and depth of acquisition programs on innovation performance in other industries, other research should be conducted in this specific context.

CONCLUSION

This research was motivated by the idea that market leaders of today, with the resources and expertise they own, can be valuable players in a transitioning industry. This research hoped to find evidence for companies to make informed decisions regarding the dimensions of their acquisition programs to sustain their competitive advantage in a new industry. Unfortunately we must conclude that this research cannot provide the evidence to show that knowledge recombination, complementary knowledge bases or by efficient coordination and integration firms can benefit their innovation performance. We can conclude that acquisition performance and subsequent innovation appear to be very context specific phenomena.

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