The acquisition of knowledge through mergers and acquisition (M&A) may not create value—usually because the knowledge may not be transferred, or transferred but not integrated. The purpose of this paper to develop and test a theoretical model of knowledge and performance in the M&A process.
Theory, model and case analysis.
The literature review led us to distinguish between three main categories of knowledge along the different stages of the M&A process: acquired knowledge in the pre-merger stage; and transferred knowledge and integrated knowledge in the post-merger stage. The application of the model is illustrated in a case study of technology M&A, which includes data collected from annual reports before and after the merger.
The model recommends acknowledging the differences between the acquired knowledge, transferred knowledge and integrated knowledge when examining the relationship between knowledge and performance in M&As. In addition, the model suggests considering several factors that influence future knowledge integration in the pre-merger stage. Ignoring the three categories and the factors may be the reason for the reports of previous studied stating that the acquisition of knowledge-based resources is associated with negative announcement returns to the acquiring firm.
The paper presents new procedures to measure knowledge, collecting data on R&D employees by using annual reports. In addition, the paper suggests adding “in-process R&D” as an “Acquired Knowledge” measure.
Calipha, R., Brock, D., Rosenfeld, A. and Dvir, D. (2018), "Acquired, transferred and integrated knowledge: a study of M&A knowledge performance", Journal of Strategy and Management, Vol. 11 No. 3, pp. 282-305. https://doi.org/10.1108/JSMA-07-2017-0049Download as .RIS
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Corporate mergers and acquisitions (M&As) significantly influence the world economy by generating market growth, international employment (World Investment Report, 2017); and knowledge is a key strategic resource in these contexts (Grant, 1996; Pablo and Javidan, 2004). However, successful implementation of knowledge-driven M&A remains a challenge to many firms (Birkinshaw et al., 2010; Das and Kapil, 2012; Gerbaud et al., 2007; Pentland, et al., 2014; Qin et al., 2017). This stream of literature has helped us understand the processes (and patterns) of knowledge transfer; and also identified several facilitators of international knowledge transfer – like communication, visits, meetings – as well as factors relevant to knowledge transfer – like time, articulability, governance form and nature of knowledge (Ambrosini et al., 2009; Bresman et al., 1999; Fang, et al., 2007). However, we still see much evidence of unsuccessful mergers (Andersson, et al., 2015; Wiersema and Bowen, 2011).
In this paper we thus focus on the question “Why does knowledge not contribute to a firm’s value in an acquisition, while it does in the individual firm?” We focus on the inability of merger partners to integrate knowledge acquired in cross border M&A. First, the paper examines the known knowledge measures in the literature. The findings lead us to suggest a theoretical model which presents knowledge measures in various stages of the M&A process. In addition, the literature discusses pre-merger facilitators of knowledge transfer. We add these factors to the model and suggest to further examine the relationship between these factors and knowledge transfer in the future. Second, to test the model, we analyze a knowledge-driven acquisition. The distinction between the knowledge measures in the different stages of the M&A enables us to examine each measure separately. The findings of the case study show how the knowledge is acquired, transferred, and integrated. In addition, the financial and operational performances of the acquisition are positive. These findings strengthen the model and support the previous studies that knowledge contributes to a firm’s value. In addition, we find in the analysis an additional knowledge measure, “in-process R&D,” and we use a new procedure to measure knowledge: collecting data on R&D employees by using text analysis of annual reports. In summary, the purpose of this paper is to raise awareness in distinguishing knowledge measures in M&As and to suggest examining the acquirer’s performance after verifying the integration of knowledge.
Literature review and conceptual framework
Knowledge, firm value and M&A
Prior studies show that knowledge contributes to a firm’s value (Ambrosini, et al., 2009). For example, Villalonga (2004) found that the greater the intangibility of a firm’s resources as measured by Tobin’s q, the greater the sustainability of its competitive advantage. Decarolis and Deeds (1999) found that the relationship between number of new products and organizational performance is positive. Hall et al. (2005) found that citation-weighted patent is correlated with market value. Chan et al. (2001) found that companies with high R&D to equity market value earn large excess. Carmeli and Tishler (2004) found that local authorities with educated and trained employees outperform those that do not have these characteristics.
Several studies examined the relationship between knowledge-driven acquisitions and value. For example, Gerbaud et al. (2007) compare knowledge-motivated acquisitions to property-motivated acquisitions. The motive for the acquisitions was defined by content analysis of the press release. They created two lists of resource categories, distinguishing between property- and knowledge-based resources. They found that short-term market performance of property-motivated acquisitions was higher than that of the knowledge-motivated acquisitions.
Arikan (2004) compared intangible and tangible acquisitions and defined intangible assets. She measured highly intangible targets by q’>1 and found that firms that buy highly intangible targets lose, on average, 12 percent of their value over the 60 months following the M&A activity. On the other hand, firms that buy highly tangible targets, on average, break even in the long run. In summary, these studies show that knowledge-based acquisitions do not contribute to a firm’s performance. These findings raise the question “Why does knowledge not contribute to a firm’s value in acquisition while it does in the individual firm?” We suggest that since in those studies knowledge has been measured before the acquisition, the knowledge was only acquired but was not integrated in the acquiring firm. This finding led us to look for knowledge transfer measures in the literature.
Knowledge and knowledge measures
Knowledge is defined in several ways; this study uses that of Grant (1996) that distinguished between tacit and explicit knowledge. The critical distinction between tacit and explicit knowledge is the mechanism of transfer as Grant (1996, p. 111) says: “Tacit knowledge is revealed through its application. Explicit knowledge is revealed by its communication.” Lai (2013) emphasizes the importance of distinguishing between implicit and explicit knowledge when examining the impact on knowledge accumulation. This study focuses on explicit and implicit technological knowledge. Tacit knowledge has the greatest potential for creating competitive advantage since it is very difficult to imitate (Barney, 1991).
Since knowledge is an intangible asset it cannot be measured directly. The common measure for valuing intangible assets is Tobin’s q, the ratio of a firm’s market value to the replacement cost of its assets (Arikan, 2004; Hall et al., 2005; Lev, 2001; Simon and Sullivan, 1993; Villalonga, 2004). Other measures of explicit knowledge can be technological knowledge-based resources such as R&D expenditures (Chan et al., 1990; Chauvin and Hirschey, 1993; Decarolis and Deeds, 1999; Hirschey, 1985; Lustgarten and Tomadaki, 1987; Morck et al., 1988; Morck and Yeung, 1991), patents (Griliches et al.,1987; Ahuja and Katila 2001; Hall et al., 2005; Shane and Klock, 1997), new products (Carmeli and Tishler, 2004) and implicit knowledge as human capital (Coff, 2002; Hatch and Dyer, 2004; Hitt et al., 2001; Pennings et al., 1998). According to Becker (1975), “[People] are called human capital because [they] cannot be separated from their knowledge, skills, health, or values in the way they can be separated from their financial and physical assets.” He argues that education and training are the most important investments in human capital that influence economic growth. Human capital is measured for example by education, work experience, and competence of a firm’s members (Carmeli and Tishler, 2004).
First it should be noted that knowledge transfer in the context of acquisitions has been examined mostly in terms of “resource redeployment” (Capron et al., 1998; Capron and Mitchell, 1998; Capron, 1999; Capron and Pistre, 2002). Capron and Mitchell (1998) define resource redeployment as “The use by a target or acquiring business of the other firm’s resources; redeployment may involve transferring resources to new locations or sharing resources without physical transfer” (p. 453). They measure redeployment of ten resources including two types of technical resources (product innovation, manufacturing), three types of commercial resources (sales networks, brand names, marketing experience), four types of administrative resources (supplier relationship, logistics expertise, managerial capabilities, staff personal) and financial resources. Most of these resources are tacit resources. Also, Capron, et al. (1998) and Capron (1999) measure redeployment focusing only on five organizational functions from Capron and Mitchell (1998) study. Bresman et al. (1999, p. 444) contend that “Knowledge may be transferred in either or both of the following directions: from the acquiring unit to the acquired unit: from the acquired unit to acquiring unit.” They measured knowledge transfer via two items related to the transfer of technological know-how between the R&D units, as well as the number of patents generated by the acquired unit after five years. Varra et al. (2010) also use the term knowledge transfer, using the measure of Capron et al. (1998). Other measures of knowledge transfer taken from the accounting literature are change in the R&D expenditures or change in the total number of employees between years before the merger and years after the merger (Healy et al., 1992).
The above studies generally discuss knowledge transfer without further investigating whether or how knowledge was used by the acquiring firm (Pentland, et al., 2014). We now consider work dealing with issues like knowledge combination, knowledge creation, learning, assimilation and integration. Greenberg and Guinan (2004, p. 137) define knowledge transfer as “an organization’s ability to assimilate, adapt, and improve on another organization’s existing technological processes and products.” This definition assumes that modification and further innovation of the existing technology is a component of the knowledge transfer process. In this case, the common measure for innovation output is patents (such as presented by Ahuja and Katila, 2001; Makri et al., 2010).
According to Grant (1996), knowledge transfer does not necessarily indicate knowledge integration. The organization should have the capability to integrate the knowledge. Following the above review (see Table I) we suggest to distinguish between “Acquired Knowledge”, “Transferred Knowledge”, and “Integrated Knowledge” in technology M&A process. “Acquired knowledge” refers to the value of the acquired knowledge assessed by the acquiring firm before the merger. The acquired knowledge might be explicit or implicit knowledge. “Transferred Knowledge” refers to the level of knowledge that is transferred from the acquired firm to the acquiring firm after the merger. “Integrated knowledge” refers to the application of the knowledge.
Other aspects that have been discussed in the literature are mechanisms and factors that influence knowledge transfer or knowledge integration. Mechanisms facilitating knowledge transfer include communication, visits, and meetings (Bresman, et al., 1999; Huysman et al., 2002). Lai (2013) finds that interactive coordination mechanism and systemization mechanism help the accumulation of explicit knowledge.
A recent paper of Huang et al. (2012) suggests six key factors that influence the success of knowledge transfer, including prior-gained knowledge of merging companies (large comprehensive knowledge base, high quality employees, advanced products, clear organization policies and knowledge network), prior-gained knowledge of merged companies, motivation for M&A, levels of knowledge to be transferred, investments in transfer and the implementation process. This study focuses on the following factors: knowledge similarity, organizational culture, national culture, and degree of integration. While the relationship between these factors and performance has been examined broadly in the literature, research on the relationship between these factors and knowledge transfer is relatively unexplored—hence our focus on them in this study.
There is a growing body of research on the subject of knowledge similarity (e.g. Singh and Montgomery, 1987; Mowery et al., 1998; Ahuja and Katila, 2001). The relationship between knowledge similarity and innovation output, which we can refer to as knowledge integration, was examined by Ahuja and Katila (2001), Cloodt et al. (2006) and Makri et al. (2010). Ahuja and Katila (2001) distinguish between technological and non-technological acquisitions. They compared the knowledge bases of acquired and acquiring companies according to three measures: absolute size, relative size and relatedness as measured by patents. Cloodt, et al. (2006) replicated the study of Ahuja and Katila and extended it to four sectors in the high-tech industry. Makri, et al. (2010), different from previous studies, discuss two kinds of knowledge, science knowledge and technology knowledge, and combine this notion with similarity and complementarity, creating four variables: science similarity, science complementarity, technology similarity and technology complementarity.
National culture and organizational culture
There is extensive research about the relationship between national and organizational cultures and performance (see review by Teerikangas and Very, 2006) but our knowledge of the relationship between national and organizational cultures and knowledge transfer is quite limited (Qin, et al., 2017). Several studies have examined this recently, such as Sarala and Vaara (2009) and Varra et al. (2010). The organizational culture was measured using survey questions; and national culture was measured using the GLOBE dimensions.
Degree of integration
Haspeslagh and Jemison (1991) argue that mangers can improve performance by developing better understanding of the integration process. They suggest four types of integration approaches: preservation, symbiosis, holding and absorption. Pablo (1994) also argues that the level of integration is critical for M&A outcomes. It has been found that integration can cause a negative effect on acquired firm inventors (Paruchuri et al., 2006). Integration mechanisms enable coordination but also destroy processes by reduction in autonomy. Puranam and Srikanth (2007) suggested understanding this integration paradox by distinguishing two ways. Integration can help leverage what the acquired firm “knows” (existing knowledge) but avoid the ability to leverage what the acquired firm “does” (innovation capability). Puranam et al. (2009) suggested that preexisting common ground can be alternative to achieve coordination and can be less destructive than integration. A recent study by Varra et al. (2010) finds that operational integration is positively associated with knowledge transfer. They measure integration using a questionnaire measuring operational integration activities. For example, questions about elimination of overlaps between the units or tendency to standardize practices.
Model of M&A knowledge-performance
We are working toward understanding why knowledge often seems to have value in a given firm, but does not contribute to performance in an acquisition. Based on the above review of knowledge measures in different stages of the M&A and the factors influencing future knowledge integration, we suggest the following theoretical model of knowledge and performance in the M&A process (Figure 1). The pre-merger stage includes the explicit and implicit acquired knowledge and the factors’ influence on future knowledge integration. The measures for acquired knowledge are Tobin’s q and knowledge-based resources such R&D expenditures, patents and human capital. The factors influencing future knowledge integration include knowledge similarity, national and organizational cultures, and level of integration. The post-merger factors include the transferred knowledge and integrated knowledge. The measures for transferred knowledge are change in R&D expenditures, change in total employees and change in patents. The measures for integrated knowledge are new products and patents. The integrated knowledge may influence the acquiring firm’s performance. The performance is measured by stock market reaction and operational measures. The consideration of knowledge in different times of the M&A process supports Jemison and Sitkin’s (1986) findings recognizing the importance of the acquisition process itself.
We conducted an in-depth analysis of a knowledge-driven acquisition. In the following sections we describe the method, findings and discussion of the extent to knowledge transfer, integration and performance in the research setting we chose. Our case is a technology-driven acquisition by SanDisk Corporation (a producer of flash memory) of M-Systems Ltd (an Israeli producer of portable memory devices) during 2006. Table II provides details of the case. This M&A case was chosen for the following reasons: SanDisk is the world’s largest supplier of flash memory data storage products (see Industry Analysis and SanDisk competitive advantages, Appendix 1); SanDisk was one of few companies in the flash memory market that survived the subprime crisis; this is an international acquisition; both the acquirer and acquired are public companies; and the acquisition’s motive is technology/knowledge transfer.
The data for the case study were collected from three sources: interviews, documents and archival records. The interviews took place in SanDisk’s branches in Israel (Omer and Kfar Saba). The interviewees are former M-Systems employees. At the time of the interviews (post-merger), their positions are Sandisk’s Israel CEO, senior vice president of corporate engineering, R&D center’s manger, business development manager and director of business affairs. The CEO discusses more in detail about the motive, emphasizing the advantages of the acquisition and the similarities between the acquirer and the target firm, as high-tech firms. The senior vice president of corporate engineering and the R&D center’s manger, who are a part of the development team, give us more detailed information about the products, the technology, and the firm’s existing knowledge. The director of business affairs discusses more in detail the acquirer’s new organizational structure following the merger. The business development manager discusses more in detail the relationship between the acquirer and the target’s employees post-merger.
The interviews are in-depth, semi-structured interviews of about 90 minutes in length, as suggested by Shkedi (2003). The questions of the interviews can be found in Appendix 2. Each interview is tape-recorded, transcribed and translated.
The documents include news reports from websites, newspaper reports, investment companies’ reports and relevant articles. These reports include additional quotations of the CEOs, Dr Eli Harari and Dov Moran, which give us further relevant information. Archival records include organizational records, such as annual reports and Board of Directors’ reports, which are taken from Lexis-Nexis or from the acquirer’s website. Financial data are taken from the Center for Research in the Security Prices (CRSP) database. The patents data of the acquirer and the target firm are taken from the website https://iu.app.box.com/v/patents. Information about the patent citations is taken from the website www.google.com/patents.
The data analysis include both qualitative and quantitative analyses.
The qualitative analyses include content analysis of the interviews and newspaper reports, which are mentioned above. In the first phase, called “initial analysis” or “open coding”, we identify each category and the theme it represents. The themes are pre-merger and post-merger, and the categories are knowledge measures and knowledge transfer measures. In the second phase, called “mapping analysis”, we look for associations between the M&A phases and knowledge/knowledge transfer measures. In the third and final phase, called “focus analysis”, we look for central categories, which include the M&A phases and the knowledge/knowledge transfer measures.
The quantitative analysis includes measuring knowledge, using Tobin’s q, patents, R&D expenditures, total number of employees, number of R&D employees and in-process R&D; and performance parameters, using stock market reaction and operating performance.
Tobin’s q is used to measure acquired knowledge. It is calculated by the simple method (Chung and Pruitt, 1994) of market value (number of common shares times price of share), plus the liquidating value of the firm’s outstanding preferred stock, plus debt, divided by total asset.
The acquired knowledge is measured by the amount of R&D expenditures of the target firm before the merger. Transferred knowledge is measured by the change in R&D expenditures pre- and post-merger. To calculate the change in R&D expenditures, we first sum the R&D expenditures of the acquirer with the acquired firm’s R&D expenditures for each of the pre-merger years. In the post-merger, we use the acquirer’s consolidated data. Then, we calculate the difference of R&D expenditures before and after the merger.
The acquired knowledge is measured by the number of patents that the target firm has before the merger.
Transferred knowledge is measured by the number of the target firm’s patents that remain in the list of the acquirer firm after the acquisition.
The integrated knowledge is measured by citations of the acquired patents by SanDisk’s new patents after the acquisition and it is measured by new products.
Total number employees and R&D employees
Data on the total number of employees and number of R&D employees are collected from the annual report in the business section. The acquired knowledge is measured by total number of employees and R&D employees of the target firm before the merger. Transferred knowledge is measured by change in total number of employees and R&D employees pre- and post-merger. To calculate the change, we first sum the total number of the acquirer’s employees with the acquired firm’s employees for each of the pre-merger years. In the post-merger, we use the acquirer’s consolidated data. Then, we calculate the difference of total number of employees and number of R&D employees before and after the merger.
In process R&D
We identify a new knowledge measure, as the acquired knowledge, in the target firm’s valuation: “in-process R&D” or IPRD. Before the acquisition occurs, the acquirer estimates the target’s value, which is usually revealed in the text of the M&A announcement. The actual price and its composition are written in the annual report of the acquirer in the M&A year. The purchase price includes net tangible assets, goodwill, and other identifiable intangible assets (see Table III). The intangible assets include the estimation of in-process R&D (in this case in-process technology), which indicates knowledge and can be used as a knowledge measure.
We combine two measures, stock market reaction and operating performance, as suggested by Das and Kapil (2012).
Stock market reaction is measured by using event-study methodology. The following equation (Brown and Warner, 1985) is used:
In our calculations, we use daily data from August 1, 2005 to May 22, 2006 as the estimation period; there are 204 daily observations in this period. The market index is the S&P 500.
Operating performance is measured as done in the Healy et al. (1992) study. We use operating cash flow return on assets, calculated with operating cash flow scaled by market value of assets. We define operating cash flow as sales minus cost of goods sold and sales and administrative expenses, plus depreciation and goodwill expenses. The market value of assets, measured at the beginning of the year, is the market value of equity plus the book values of preferred stock and net debt. The market value of assets in the pre-merger years is the sum of the acquirer and acquired firm’s values. The market value of assets in the post-merger years is the assets of the combined firm. We exclude the change in equity values of the target and acquiring firms at merger announcement from the asset base in the post-merger years.
In order to calculate the change in the operating cash flow return, we calculate the differences of operating cash flow return before and the after the merger.
Pre-merger: acquired knowledge
First, we identify the “Acquired Knowledge” in this acquisition, using Tobin’s q, patents, products, number of total employees, specifically number of R&D employees of M-Systems and in-process R&D.
The approximate q of M-Systems is 2.6. The following data of M-Systems, at the end of the year (December 31, 2005), are taken from the annual report. The share price is taken from CRSP database:
|No. of common sharesa||Price of share (December 30, 2005)||Total assets (in thousands)||Liabilities (in thousands)|
Note: aAssuming no preferred stock (not mentioned in reports).
M-Systems has 42 patents before the acquisition.
SanDisk Inc. is motivated by a desire to have the acquired new knowledge-based resources: X4 technology (a 4-bit technology, which can hold four times the information as the old technology) and a product, Solid State Disk (SSD), along with the R&D employees’ expertise.
M-Systems invests in R&D expenditures pre-merger, in 2005, the company invests $38,417 thousands in R&D.
Number of total employees and specifically number of R&D employees
M-Systems has 822 employees a year before the acquisition, 371 of them are R&D employees.
Acquired in-process R&D
The target price indicates that in-process R&D is $186,000.
Pre-merger factors influence knowledge transfer
At the beginning of the qualitative analysis we looked at the statements by the main players and were reminded of the essential product, technological and human aspects of a knowledge-driven M&A. A senior executive of SanDisk Israel says, “the acquisition motive is to have an M-System team, ×4 technology and products.” In the words of Dr Eli Harari, Chairman and CEO of SanDisk, “M-Systems is a leader in flash memory systems addressing mobile, portable and embedded markets and they have a strong team, significant IP and important OEM customers.”
Dov Moran, President and CEO of M-Systems stated, “from mDOC to megaSIM, from U3 to ×4, M-Systems is creating new markets through innovation. We are truly proud of our achievements to date. This strategic deal will enable us to continue supporting our OEM customers, to whom we remain fully committed, and strengthen our innovation and product offering with SanDisk’s leading edge, low cost fab capacity. This deal has synergy at its core, encompassing people, technology, products and customers. Based on our shared vision, as well as our teams’ history of successful and fruitful cooperation, I am confident we can succeed in achieving the goals we set for ourselves. I also believe that SanDisk’s extensive silicon expertise will prove itself as a strong catalyst to productizing our revolutionary ×4 technology as well as other future innovations.”
Harari continued, “ultimately the success of everything we do depends on the people, and both Dov (Moran) and I have been exceptionally fortunate to recruit the best people and talent in the business. Dov and I are committed to meet the challenge going forward of integrating the two teams into a unified, effective organization that is totally focused on our customers.”
So from the start we see how the senior executives on both sides identified the tangible as well as the intangible aspects of the transaction, and were preparing to deal with aspects of integration as the deal moved forward.
In the acquisition described here, both the acquirer and the acquired have similar knowledge bases before the acquisition, their R&D employees have similar expertise and are Application-Specific Integrated Circuit and system engineers, and both use the same technology, NAND (Negated AND or NOT AND) flash memory. Thus, they have some similar products, such as USB drives and memory cards. This situation is reflected in the following quotation taken from the announcement and the annual report: “SanDisk, the world’s largest supplier of flash memory data storage products, designs, develops manufactures and markets flash storage products for a wide variety of electronic systems and digital devices […] M-systems designs, develops and markets innovative flash data storage solutions for consumer electronics markets.”
In 2005, SanDisk focused on developing the following products: Compact Flash, SD Card, miniSD Card, microSD, Memory Stick PRO/Memory Stick PRO Duo, Cruzer USB Flash drives, XD-Picture Card, Sansa Digital Audio Music Player and Gaming Card. In 2005, M-Systems developed the following products: mDrive, mDoc, mSIM, MegaSIM, mCard, mSSD and mModule. In addition, while SanDisk Corp. has new IP and flash knowledge, which M-Systems is exposed to for the first time, M-Systems has better knowledge about the controller. The Senior Vice President of Corporate Engineering explains, “the development process includes flash memory development, controller development and system level integration.” R&D Center Manager says, “Our [M-System’s] strength is our knowledge in controller development. SanDisk develops controllers too, but we are better at ‘mistake fixing’.” Furthermore, the Business Development Manager says, “We have the know-how knowledge, the capabilities, and experience in developing and manufacturing of the controller.” The two companies’ knowledge and strengths complete each other. The Senior Vice President of Corporate Engineering explains, the development process includes flash memory development, controller development and system level integration.
National and organizational culture
The next set of factors influencing knowledge transfer are the differences in national or organizational cultures. SanDisk is an American company and M-Systems is an Israeli company. Both CEOs, who founded the companies, are Israeli. SanDisk Corp. is founded in 1988 by Dr Eli Harari in California. M-Systems is founded in 1982 by Dov Moran in Israel. They have a close relationship before the merger. Dr Harari states: “Every time I visit Israel, I invite Dov to cheesecake and coffee, and we talk in riddles.” The geographic distance between the USA and Israel creates difficulties—for example, difference in work hours, and therefore, limited time for consulting. Israeli high-tech workers frequently work over-time, while American high-tech workers generally stick to their shift hours.
The organizational culture, described by the employees who are interviewed, is influenced by the industry and by the firms’ founders’ personality. In this case, the industries of the acquirer and the acquired are similar and the employees have similar orientations. For example, both acquirer and acquired firms are innovation oriented (similar dimension of organizational culture), they cooperate and enable knowledge transfer in order to develop new products. Both the acquirer and acquired firms, as high-tech firms, are characterized by informal procedures (another dimension of organizational culture), relying heavily on brain-storming sessions, as Israel SanDisk CEO says, “We work together as a team. We have meetings in small groups in which we share ideas, suggestions, and knowledge.” These kinds of sessions enable implicit knowledge transfer.
In addition, Dr Harari defines his organizational culture as “[…] innovation, speed, setting standards, and long term partnering.” Regarding the powerful synergy with M-Systems he stated: “we both have a similar culture: a passion for creating markets and building businesses through innovation.” He added about the founder of M-Systems, who influenced his firm’s culture, “Dov is a very serious, competitive guy with a lot of integrity. People in and outside M-Systems say that success is a natural by-product of Moran’s hard work and high ambition.”
In summary, similar organizational cultures seem to have facilitated knowledge transfer in this case.
Level of integration
Prior to the acquisition, SanDisk has a functional structure and M-Systems had a divisional structure. At the beginning of the integration process, the acquirer used an external consulting firm that recommends maintaining M-Systems’ autonomy. Both CEOs reject the recommendation and decide on full integration; they changed the organizational structure to a matrix structure, as described by the Director of Business Affairs. Some of M-Systems’ divisional managers relocated to California to SanDisk as senior managers and others resigned, including the COO and development managers. Some of SanDisk’s mangers also left. The main reason for these changes was culture clashes.
For example, Shai Adar, the M-Systems Business Development manager, states: “A key person, the R&D senior manager of M-Systems, who was promoted to the position of vice-president of one of the divisions of SanDisk, straightforwardly criticized the American employees [an approach that is considered acceptable in the Israeli business culture, but not in the American culture]. This style caused several employees to leave the company, including SanDisk’s R&D manager. His behavior led to his dismissal, and a great deal of knowledge was lost as a result.” In summary, full integration can lead to culture clashes, which in the case of different cultures can be an obstacle to knowledge transfer.
Post-merger: transferred and integrated knowledge
In the post-merger stage, we identify “Transferred knowledge” and “Integrated knowledge” measures as follows: change in number of patents and patent’s citations, change in the number of total employees, and specifically change in the number of R&D employees, change in R&D expenditures and new products.
SanDisk transfers 41 of M-System’s patents. One of the patents changes ownership to Ramot, Tel-Aviv University’s implementation authority.
After the acquisitions, SanDisk used almost half of the acquired M-Systems’ patents as a reference for the combined company’s new patents (see Table A1).
In addition, the manager of R&D center says, “The knowledge transfer was conducted by transferring the IP from M-Systems to SanDisk. The IP included algorithms (methods of flash memory management developed by M-Systems’ engineers). M-Systems’ engineers were experts at error-correction at the control development stage, which contributed to SanDisk’s knowledge. But after the acquisition, the acquirer stopped producing products similar to those of the target and the target began working on new projects based on similar knowledge.”
Total number of employees and specifically R&D employees
Table IV reveals that there is an increase in the number of total employees after the merger, with the exception of 2009 (a fact that can be attributed to the world-wide recession).
Since R&D employees are described as the most valuable resource of the companies, one of the integration objectives is “maintaining employee morale and retaining key employees.” The manager of the R&D center states: “SanDisk doesn’t cut duplicate R&D centers. SanDisk has R&D centers in the US, India, Ireland, and Israel (Tefen industrial zone). M-Systems had R&D centers in Israel (in Omer and Kfar-Saba) and Spain. The number of employees at the lower level was retained with no changes.”
Data on the R&D employees, summarized in Table V, reveals that there is an increase in the number of R&D employees immediately after the merger. There is also an increase during the three years following the merger, with the exception of 2009 (a fact that can be attributed to the world-wide recession).
Data on the R&D expenditures, summarized in Table V, reveals that there is an increase in R&D expenditures after the merger (except 2009).
Three years after the merger, on October 13, 2009, SanDisk announces on the company website that it has begun production and shipment of flash memory cards based on the company’s advanced X4 flash memory technology. “The development and commercialization of ‘X4’ technology represent an important milestone for the flash storage industry,” says Sanjay Mehrotra, President and Chief Operating Officer, SanDisk. “Our challenge with X4 technology was to not only deliver the lower costs inherent to 4-bits-per-cell, but to do so while meeting the reliability and performance requirements of industry standard cards that employ MLC NAND. Our world-class design and engineering team has applied its deep experience with high speed 2- and 3-bits-per-cell flash chip designs and collaborated closely with our leading design partners to develop and perfect new and powerful error correction algorithms to assure reliable operation. This intensive multi-year effort has generated powerful new patents and know-how, and demonstrates SanDisk’s relentless drive for innovations that result in the ever-expanding use of flash storage in consumer applications such as music, videos, photos, games, and numerous third-party applications.”
The new product, based on M-Systems technology, indicates “Knowledge integration” in this acquisition.
In addition, only M-Systems develops SSD products, which also embeds new knowledge. M-Systems’ Business Development manager states: “We have the know-how, knowledge, the capabilities, and experience in developing and manufacturing controllers for SSD products.” After the merger, SanDisk enables M-Systems to continue working on SSDs.
In summary, all knowledge measures discussed in the literature review are identified in the acquisition of M-Systems by SanDisk (see Table VI).
Stock market reaction
The stock market reaction in the short-term shows that SanDisk’s cumulative abnormal return (CAR) −10 and +10 days relative to the announcement date (see Figure 2) was positive on the fourth day before the announcement. This indicates that information was leaked to the market, which anticipated an increase in profitability for the bidders.
The stock market reaction in the long-term shows that SanDisk’s CAR over the two-year period following the announcement was negative, as shown in Figure 3.
The operating cash flow increased throughout the years, with the exception of 2008 (see Table VII). The operating cash flow return increased post-merger in 2007. In 2008, operating cash flow return decreased because the operating cash flow and the market value both decreased. This decrease is explained by the early influence of the subprime crisis on the electronics industry, when there was a surplus of products and a decrease in sales.
Reviewing the literature on knowledge measures reveals common measures in past studies, including Tobin’s q as a measure of intangible assets; and measures of technological knowledge-based resources such as R&D expenditures, patents and human capital. Knowledge transfer in the context of acquisitions has been examined mostly in terms of “resource redeployment.” Some who use the term “knowledge transfer” define it to include knowledge transfer between both sides – from the acquired unit to acquiring unit, without referring to knowledge combination, knowledge creation, or learning; these measures include patent counts post-merger or using tacit knowledge, such as technical know-how. Other measures include assimilation in their definition. It is argued that knowledge transfer does not necessarily indicate knowledge integration.
Our review leads us to distinguish between the three categories of knowledge measures: “Acquired Knowledge,” “Transferred Knowledge,” and “Integrated Knowledge” in different stages of the M&A process. In addition, we review mechanisms and factors that influence future knowledge integration. Mechanisms that facilitate knowledge transfer may include communication, visits, and meetings. There are several factors influencing knowledge transfer; this study focuses on knowledge similarity, degree of integration, and organizational and national cultures. The relationship between these factors and knowledge transfer only begins to be investigated recently and there is a need to expand on it in future research.
The distinction between those types of measures emphasizes the importance of considering knowledge at different stages in the M&A process. When researchers define knowledge-based acquisition, they should examine whether the knowledge is transferred and integrated. This is important since acquisition failure can be a result of failing to transfer the knowledge between the two firms and not a failure in assessing the value of the knowledge itself. The review is summarized in a theoretical model of knowledge-performance in an M&A process (see Figure 2).
The model is examined by an in-depth analysis of a single case study of M-Systems’ acquisition by SanDisk. Findings of the case study strengthen the model. First, the acquisition is knowledge acquisition; Tobin’s q is greater than 1, it indicates the company has intangible assets. The target firm has patents, technological based products and R&D expertise. The percentage of the R&D employees is 45 percent from the total employees and the company invests in R&D. These knowledge-based measures are known already in the literature (e.g. Villalonga, 2004; Hall, et al., 2005; Chan, et al., 2001; Carmeli and Tishler, 2004) and specifically in context of M&A (e.g. Arikan, 2004; Ahuja and Katila, 2001; Healy et al., 1992).
We also identify new technological knowledge measure, IPR&D. To date, this issue has been examined mainly in the accounting literature. It is defined as “the fair [value] assigned to acquired, though incomplete R&D projects that are typically purchased in a business combination” (Clem et al., 2004; Deng and Lev, 2006; Mulford and Yang, 2008, p. 4). It has been found that high-tech and pharmaceutical companies in the U.S. have written-off IPRD upon completion of M&As. This behavior led the SEC to follow firms’ write-offs and the Financial Accounting Standards Board to issue new guidelines (Financial Accounting Standards Board, 2007). Yu Hsu et al. (2009) investigated the causes of IPRD write-offs in an attempt to determine whether they arise from overpayments for the target at the time of the acquisition, or whether they are mainly earnings management tools. The results support the first argument, but not the latter.
In examining the factors that influence knowledge transfer, it is first found, that the SanDisk and M-Systems has similar knowledge. The knowledge embedded on R&D expertise, enables knowledge assimilation that improves the IP and creates new products, but, on the other hand, diminished the development of similar products. Knowledge base similarity and its influence on output innovation has been examined in previous studies (Ahuja and Katila, 2001; Cloodt et al., 2006; Makri et al., 2010) and is measured by patents. Ahuja and Katila (2001) find that the relatedness of acquired and acquiring knowledge bases has a curvilinear impact on innovation output.
Second, the similar country origin of the acquirer and acquired firm CEOs, their good relationship, and the mutual appreciation, all create a friendly acquisition with a positive atmosphere. This kind of atmosphere enables capability transfer (Haspeslagh and Jemison, 1991). In addition, SanDisk and M-Systems has joint projects before the merger and SanDisk has R&D centers in Kfar-Saba and Petach-Tekv. This enables the management to already know the Israeli market and enables trust development, which facilitates future knowledge transfer. This finding supports Stahl and Sitkin (2005). But on the other hand, there were difficulties from the USA and Israel’s geographic distance, such as limited time for consulting. This finding supports Mayer and Kenny (2004) who conduct an intensive study on Cisco and suggest the importance of geographic proximity for M&A’s success.
Third, both the acquirer and acquired firms, as high-tech firms are characterized by informal procedures, similar dimension of organizational culture, usually arrange a brain-storming meeting. This kind of meeting enables implicit knowledge transfer. This mechanism is also discussed in the literature as a facilitator factor (Huysman et al., 2002; Bresman, et al., 1999; Bjorkman et al., 2007).
Fourth, the acquisition was a complete integration accompanied by a change in the organizational structure. This change causes seniors’ employees to resign mainly because of culture differences. This supports the findings regarding the critical effect of different cultures on integration (Cartwright and Cooper, 1996; Lodorfos and Boateng, 2006; Weber, 1996; Badrtalei and Bates, 2007; Schraeder and Self, 2003). In addition, the leave of the employee’s causes a loss of knowledge. There are few papers that examine the relationship between culture and knowledge transfer. For example, Varra et al. (2010) examine the relationship between organizational culture/national culture and social conflict. They find that organizational cultural difference, but not national cultural differences, can be a root cause of social conflicts.
In referring to transferred knowledge, we find that the numbers of R&D employees continue to increase post-merger, indicating knowledge transfer. This finding supports Ranft and Lord (2000), who find that R&D personnel are cited as the most important source of knowledge, the main reason why the firm is acquired. The research on R&D employees in the context of M&As is limited. Most of the studies focus on other kinds of key employees, managers for example (Hambrick and Cannella, 1993; Walsh, 1988, 1989; Walsh and Ellwood, 1991). Other studies measure knowledge transfer by using surveys on R&D, but do not use more objective data from annual reports (Capron, 1999; Capron and Pistre, 2002).
R&D expenditures increased post-merger, indicating knowledge transfer by continued investment in R&D. This finding is inconsistent with that of Hitt et al. (1991), who find negative effects of acquisitions on R&D investments; and with Ruckman (2009) who finds negative effects in technology acquisition with related research line; but it is consistent with Healy et al. (1992), who find no decrease in R&D expenditures after mergers, a fact indicating that merged firms do not reduce their long-term investment.
Finally, the integrated knowledge; the acquirer uses half of the target firm’s patents as reference and the company develops new products based on the target’s technology. These findings support Makri et al. (2010) who find that acquisitions produce greater innovation.
In summary, our findings identify pre- and post-merger knowledge measures (see Table VI), supporting the prior literature, and identify a new measure – “in-process technology” – and a new method to assess knowledge transfer, using the number of R&D employees from annual reports. In addition, it is found that the acquired knowledge is transferred and integrated.
Another interesting finding regarding this technology-driven acquisition is that the performance, measured by stock market reaction in the short term, shows a positive abnormal return. The financial performance’s finding is inconsistent with previous empirical studies’ predictions in the finance literature about what happens to the CARs of an average firm following a representative acquisition in a large sample (Andrade et al., 2001; Jarrel et al., 1988; Jensen and Ruback, 1983), with the exception of Bena and Li (2010). In the strategy literature, Capron and Pistre (2002) examine resources (innovation, marketing and management), transfer, and value creation. They find that acquirers do not earn abnormal returns when they only receive resources from the target. But when they transfer their own resources to the target, there is a positive relationship.
In contrast, the stock market reaction in the long term shows negative CAR. This finding is consistent with the finance literature (Andrade et al., 2001; Fuller et al., 2002; Loughran and Vijh, 1997; Mitchell and Stafford, 2000; Moeller et al., 2005; Rau and Vermaelen, 1998; Savor, 2006).
Operational performance shows improvement in the year following the merger. The literature shows mixed results; Healy et al. (1992) and Heron and Lie (2002) find an operational improvement after the merger, in contrast to Ravenscraft and Scherer (1989) and Ghosh (2001). It can thus be seen that when knowledge is transferred and integrated, the company has a positive performance.
This paper implies that when choosing a company to acquire, whose knowledge is desired, the acquirer’s management must examine the similarities in knowledge and culture. Furthermore, once a firm is acquired, the acquiring company must ensure that the level of integration will enable knowledge transfer. When the acquirer is interested in knowledge, different measures can be used to evaluate the knowledge. Moreover, once the acquirer decides to acquire the knowledge, it must follow it and verify its transfer and integration.
Conclusions and implications for future research
This paper begins with the question “Why does knowledge not contribute to a firm’s value in an acquisition while it does in an individual firm?” The review of the literature leads us to distinguish between three knowledge measures: acquired knowledge, transferred knowledge and integrated knowledge. Our conclusion seems to be that the failure of knowledge acquisitions might be due to knowledge not being transferred, or transferred but not integrated. Following this realization, we propose a model of knowledge transfer in M&A process.
We must mention, at this point, that our approach of categorizing is based on the assumption that the knowledge acquired is considered transferred if there is no change or if there is a positive change in the measure of knowledge after the acquisition. Decline in the measure might indicate that no knowledge is transferred. For example, no change or positive change in R&D employees indicates the company maintains the acquired employees – which implies knowledge retention. Others might interpret a decline in R&D employees as some redundant knowledge that is transferred and hence some R&D employees can be dismissed. Alternately, substantial knowledge may have been transferred, but the R&D capacity of the acquiring firm cannot maintain it; or a decline in R&D expenditures might indicate that some valuable knowledge is transferred, yet the R&D process has become more productive and effective – and hence the acquiring firm needs to spend less money for the same amount of R&D output. These relationships between knowledge transfer and measures of knowledge are certainly a subject for future research. Another fascinating area to be explored would be the involvement of professional advisers in acquisition processes (Pickering, 2017). These issues are especially relevant in a world of increasingly transnational knowledge (Seabrooke, 2014).
Finally, we should mention the limitation that our model was tested using a single case study. Our results should be consider with this limitation in mind, as well as in light of the many assumptions made throughout the process.
Knowledge measures in the literature organized by suggested categories
|Kind of knowledge||Measure||Papers|
|Acquired knowledge||Intangible asset: Tobin’s q||Villalonga (2004), Arikan (2004)|
|Griliches et al. (1987), Shane and Klock (1997), Hall et al. (2005), Ahuja and Katila (2001), Decarolis and Deeds (1999)|
|R&D expenditures||Chan et al. (1990), Chan et al. (2001)|
|Carmeli and Tishler (2004)|
|Transferred knowledge||Number of patents||Bresman et al. (1999)|
|Technological know-how (practical skills and expertise, two questions)||Bresman et al. (1999)|
|“Resource redeployment” (questionnaire)
|Capron et al. (1998), Capron and Mitchell (1998), Capron (1999), Capron and Pistre (2002)|
|Integrated knowledge (innovation output)||Patent count and patent citations
|Ahuja and Katila (2001), Makri et al. (2010)|
Note: aThe ten resources include product innovation, manufacturing, sales networks, brand names, marketing expertise, supplier relationships, logistics expertise, staff personal, managerial capabilities and financial resources
SanDisk and M-Systems merger data
|Bidder||SanDisk, based in USA|
|Target||M-Systems, based in Israel|
|Announcement date||July 31, 2006|
|Completion of the merger||November 19, 2006|
|Value of acquisition||$1.5 bn|
|Form of payment||Stock. SanDisk issued approximately 29.4 million shares based on an exchange ratio of 0.76368 shares of the company’s common stock for each outstanding share of M-Systems common stock. The average price per share of SanDisk common stock of $46.48 was based on the average of the closing prices for a range of trading days around the announcement date of the proposed transaction|
|Type of acquisition||Horizontal|
|Multiple/single bidders||Single bidder|
|Attitude of the target||Friendly|
|Number of interviews||7|
M-Systems’ total purchase price (in $000)
|Net tangible assets acquired||$ 213,988|
|Other identifiable intangible assets|
|Total other identifiable assets||312,500|
|Acquired in-process technology||186,000|
|Deferred tax liability||(31,339)|
|Assumed unvested stock-based awards to be expensed||55,339|
|Total preliminary estimated purchase price||$1,495,738|
|M-Systems: total number of employees||394||513||822|
|SanDisk: total number of employees||805||876||1,083||2,586||3,172||3,565||3,267|
|Combined firm: total number of employees||1,199||1,389||1,905||2,586||3,172||3,565||3,267|
R&D employees and R&D expenditures, three years pre-merger and three years after the merger (in $000)
|Total number of R&D employees||378||530||821||1,081||1,255||1,341||1,236|
|Total R&D expenditures||10,006||150,889||233,227||306,866||418,066||429,949||384,158|
Knowledge measures identified in acquisition of M-Systems by SanDisk
|Kind of knowledge||Measures|
|Acquired knowledge||Intangible asset
Number of total employees
Number of RD employees
|Factors influencing knowledge integration||Knowledge similarity
Same R&D employee expertise
|Differences in national culture
Israeli – American
Both high tech companies
|Degree of integration
|Transferred knowledge||Acquisition of all target’s patents
Positive change in number of R&D employees
Positive change in R&D expenditures
Keep technology and SSD
|Integrated knowledge||Citations of acquired patents by new patents
New product based on acquired technology
Operating cash flow/market value (in $’000)
|Operating cash flow||461,609||781,624||1,102,023||1,215,223||1,276,953||115,711|
|Market value of assets||3,589,096||11,246,413||5,669,582||13,339,022||9,859,717||7,920,537|
|Operating cash flow/market value of assets||0.12||0.06||0.19||0.09||0.12||0.014|
The acquired patent citations until 2017 and specifically citations by SanDisk post-acquisition
|Patent number||Referenced by all companies (until 2017)||Referenced by SanDisk July 2006-2010|
|6591330||73||None before 2011|
|6691205||37||None before 2011|
|6760805||58||None before 2011|
|6883114||57||None before 2011|
|6986030||153||None before 2011|
|7003620||66||None before 2011|
|7058818||78||Not before 2011|
Source: Available at: www.google.com/patents
Appendix 1. Industry background
The flash memory market is based on two basic technologies: NOR and NAND. NOR is traditionally used for code storage and is characterized by fast read speeds and generally higher costs per megabyte and lower storage capacities than NAND. NAND flash memory is traditionally used for embedded and transportable data storage, and is characterized by fast write speeds, high capacity, and lower manufacturing cost than NOR flash memories. SanDisk focuses on NAND-based products.
Digital computing products include traditional computers and consumer electronics, communications devices and industrial computerized products. SanDisk focuses on digital consumer devices such as digital cameras, mobile phones, gaming devices, personal computers and portable digital audio players. SanDisk produces flash memory products for these devices. Flash memory products are composed of flash memory and a controller. Flash memory is non-volatile and can be manufactured in large volumes. The intelligent controller technology includes advanced defect management systems that allow the flash storage products to achieve a high level of reliability and longevity. Each flash chip contains millions of flash memory cells. A failure in any one of the cells or in a group or block of cells can result in loss of data such as picture files, and this can occur several years into the life of a flash storage device. The controller inside the device is designed to detect such defects and recover data under most standard conditions.
SanDisk faces competition from semiconductor manufacturers, flash memory cards, USB drive manufacturers, digital audio player manufacturers and other storage technologies (for details see Table AI).
The interview began with a few broad questions:
Tell me about SanDisk/M-Systems.
Tell me about the acquisition.
What was your involvement in the acquisition?
In order to lead the conversation toward the main topic, directing follow-up questions were asked:
What was the motive for the acquisition?
What were the criteria for the M&A?
What is technological knowledge?
What are the acquirer and the target’s products pre-merger?
What was the relationship between the two companies before the merger?
What were the similarities between the two companies before the merger?
How does the knowledge transfer?
Are there any new products based on the acquired firm’s technology?
What happened to the employees after the acquisitions?
What was the level of integration?
What were some of the changes that were made to the companies throughout the acquisition process?
What were the organization structures of the companies before and after the merger?
What were some of the difficulties that were faced during the acquisition process?
What were the differences in the culture?
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