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High Technology Semiconductor Company Acquisitions

The fast rate of technological change was one of the most important trends in the 1990s and this brought an increasing complexity and cost to the development of new technologies. Companies used their innovative assets as a major source of competitive advantage to quickly introduce new products and adopt new processes (Sen and Egelhoff, 2000). Acquisitions are completed in many industries for reasons that are aligned with the dominant competitive driving forces for that industry. In the area of high technology and seminconductors, the competitive drivers are short product life cycles and process advancement. Process advances are required to both support the incremental changes to existing products and to allow the creation of radically new one. The number of acquisitions rapidly increased through the decade for several reasons: the product life cycle was getting shorter; participating in the creation of industry and product standards was crucial; early entry helped capture market share; and R & D risk could be reduced. Hagedoorn (1993) found the reduction in innovation time and acquisition of needed technologies as the most important reasons for one company to pursue another. Several researchers have written about the radical and incremental innovation capabilities, their distinguishing factors and the important consequences to the corporation. It has also been argued that large firms are effective with incremental innovations and small firms are better at radical innovations. (Ettlie, Bridges, and O’Keefe, 1984; Dewar and Dutton, 1986; Christensen, 1992).

Corporate decision to acquire or not acquire another company embodies a high level, serious management strategy decision toward repositioning a company in the competitive landscape. The decade from 1990 to 2000 was chosen as an important time for acquisition activity. There was frequent activity in acquisitions during a time of stable economic conditions creating good conditions for analysis. In 1990, the dollar value of all acquisitions and mergers in the United States was two percent of the Gross Domestic Product (GDP). In 2000, the value reached over 15% of the GDP (Mergerstat, 2003). In the first 10 months of 2000, in the technology sector alone, there were 2,019 acquisition and merger deals worth $573 billion (Reason, 2000). This occurred despite studies done in the 1980’s and 1990’s that found little positive effect financially for the acquiring company. The magnitude of the activity strongly suggests that some positive relationship could be found if examined in a different way or using new metrics. This research uses a different methodology by exploring a single industry, selecting profitability growth as the metric from theoretical industry driving forces and analyzing profitability over time as a statistical repeated measures model using SPSS software. The results from this work may have strategic implications for remaining competitive in high technology, high-velocity industries.

It should be noted here that the term acquisition, mergers and acquisitions and M & A will be used interchangeably in this research and are defined in Appendix A along with other important terms.

In high technology industries, such as semiconductors, a firm interested in new product innovation must aggressively invest to stay at the leading edge. Creating or acquiring new offerings can be dependent on a combination of efforts directed either internal or external to the company. Internal efforts include primarily Research and Development (R & D) or newly formed affiliates, termed ‘greenfields’ (Vermeulen & Barkema, 2001; Sonenclar, 1984; Bradley & Korn, 1981). External efforts can take the form of acquisition or mergers to best capture the intellectual property (IP) that is maintained in the categories of trade secret and proprietary know-how. Acquisitions, when done well, appear to have the advantage of capturing this kind of IP as compared to the other forms of external efforts. Acquisitions also potentially offer faster repositioning with less risk and lower cost than pursuing internal company endeavors (Singh & Montgomery, 1987). A high technology company’s success hinges on creation of innovative ideas, availability of creative personnel, speed of new product execution and cost effectiveness.

Mergers and acquisitions are a highly favored management avenue for growth and competitive positioning. The importance of this management consideration and the impact of mergers and acquisitions continue to expand with billions of dollars involved. The importance in the technology sector becomes apparent when looking at the 724 firms that made their initial public offering (IPO) in 1992, but were not acquired or merged. Of these companies, 58% were selling at less than their IPO price six years later (Small Business Statistics, 2000).

Product and service offering must constantly evolve and change (Thompson & Strickland, 2001). High velocity innovation is fundamental to the growth and survival of high technology businesses. Organizations that are successful have a regular stream of unique products and services. Hewlett-Packard had over 50% of revenue in 1999 coming from products introduced in the previous two to four years. In high technology companies, the highest profit levels come from the newest products. Consequently, it is imperative to accelerate the innovation cycle, often through mergers and acquisitions, and this is critically important to remaining competitive. Entrepreneurial firms consistently outperform larger firms in both market and earnings growth on the Inc. 500 and Forbes 200 lists (Imparato & Harari, 1994).

There are several potential reasons for making an acquisition that have been identified and studied in the literature. In addition to the reasons for actually acquiring, there are a number of factors following the event that will influence the degree of success or failure that these efforts may experience. These elements that play a part in determining the outcome have been the focus of studies that are summarized in the Literature Review.


Both the popular business press as well as recent academic research seems to uniformly accept the unique nature of high-tech stocks. Kohers and Kohers (2000) state: ‘The high-growth nature of technology-based industries distinguishes them from other types of industries. In addition to their high-growth potential, however, another distinctive feature of high-tech industries is the inherent uncertainty associated with companies whose values rely on future outcomes or developments is unproven, uncharted fields (p. 40). In fact, many ‘pure’ technology stocks are young companies, underfunded and without prospects for generating any cash flows in the near future. Nevertheless, despite the inherent uncertainty of high-tech industries, investors seemed to disregard most equity fundamentals when valuing technology stocks, especially during the market upturn in the late 1990s. As a result, even though high-tech stocks were in general extremely volatile, many of them were trading at remarkable premiums. The exploding rate of growth in M & A activity that involved high-tech industries can be partly attributed to those overly optimistic valuations. Puranam (2001) argues: ‘On the acquirer’s side, booming stock market valuations have made acquisitions for stock feasible for several relatively small (revenue wise) firms, as well as the more established larger ones. On the target’s side, an increasing preference for the ready liquidity offered, by an acquisition, as opposed to the paper profits from an IPO have created an environment conducive to acquisitions of small start-ups’. At the same time many of these acquisitions were also motivated by the acquirer’s need to obtain critical technologies and expertise in order to quickly enhance their own technological competence.

Despite the burst of the high-tech market bubble and the failure of most of these acquisitions, investors continue to show an extreme faith on these stocks. ‘Americans still believe that technology can create a better world. Each time the U.S. tech sector falls into a trough, new technologies and companies emerge to lead it forward again’ (Business Week, August 27, 2001).


This research effort seeks to understand the relationship between acquisitions and profitability by looking at the industrial sector for high technology semiconductor companies. Many prior studies have shown little financial benefit to the acquiring company in research conducted beginning in the 1980s and extending to today using a variety of variables, measures and company sample selection. These studies will be discussed in more detail in the Literature Review. The researchers Rumelt (1984), Ravenscraft and Scherer (1987), Porter (1987) and Kaplan and Weisbach (1990) separately found that acquisitions that could be categorized as unrelated, or diversifications, did not lead to profitability improvements, but most of these studies obviously included a cross-section of divergent industries. The importance of innovation and new products in high velocity, competitive environments is discussed in literature and high velocity innovation is fundamental to the growth, profitability and survival of these businesses (Thompson and Strickland, 1999; Betz, 2001; Burgelman, Christensen and Wheelwright, 2004). The competitive advantage of capturing intellectual property through acquisition has also been discussed more recently. More clear evidence is beginning to emerge concerning the drive to acquire technology and the unique features of doing so (Prentice & Fox, 2002). This research examines the correlation between the event of acquisition and subsequent company performance and growth of profitability in the decade of 1990-2000.

Practicing managers in the area of management of technology are faced with the challenge of high velocity innovation being a requirement to maintain competitive positioning (Thompson & Strickland, 2001). Two methods for constant innovation include internal efforts, such as Research & Development (R & D), and external efforts, such as acquisitions, on which this paper focuses.

Prior studies have been cross-sectional across different industries and analyzed the benefits gained in terms of patents and R & D (Bettis 1981), stock price (Matsusaka, 1990; Schleifer and Vishny, 1990; and Lubatkin, 1982) or increase in company size versus the cost of acquisitions. These studies have not captured one of the most unique features of the high technology industry where innovation and new products are dependent on intellectual property (IP) that is maintained in the categories of trade secret and proprietary know-how. Because of this characteristic, the high technology industry would be expected to yield different results. The importance of IP and know-how has been an area of academic focus working to clarify the concept of ‘absorptive capacity’ in the 1990s, but empirical work to tie these concepts to firm performance was not pursued (Cohen and Levinthal, 1990; Barney, 1991; Prahalad and Hamel, 1990). The use of patents as a measure, as used in prior research (Acs and Audretsch, 1988; Pakes and Griliches, 1980; Hitt, Hoskisson, Ireland and Harrison, 1991), does not capture the IP benefits in these categories or measure the success resulting from these external efforts. Acquisitions, when done well, should be expected to have an advantage on capturing this kind of IP. Acquisitions potentially offer faster positioning with less risk and lower cost than internal company endeavors which include primarily Research and Development (R & D) (Gulati, 1995; Singh & Montgomery, 1987).


This research effort focuses on one high technology industrial sector of semiconductors and studies the correlation between acquisitions, profitability, survivability and R&D intensity over time. Many prior studies (Rumelt, 1984; Ravenscraft and Scherer, 1987; Porter, 1987; and Kaplan and Weisbach, 1990) have shown little financial benefit to the acquiring company, but most of these studies included a cross-section of divergent industries. The importance of innovation and new products in high velocity, competitive environments is widely discussed in literature. High velocity innovation is fundamental for the theory of growth, profitability and survival of these businesses. The competitive advantage of capturing intellectual property through acquisition has also been discussed more recently. More clear evidence is beginning to emerge concerning the drive to acquire technology and the unique features of doing so (Prentice & Fox, 2002). This paper researches the correlation between the event of acquisition and subsequent company performance, survivability, the growth of profitability and R & D spending.



Most research on high-tech companies is relatively recent and has its origin in various business fields. Chaudhuri and Tabrizi (1999) study the practices of 24 high-tech companies involved in acquisitions, and try to identify the key factors in capturing the real value in high-tech acquisitions. They conclude that in order to make a successful acquisition managers need to move beyond the traditional model of acquisitions where the people acquired are secondary to physical assets and brands. High-tech acquisitions need to focus on the people since technological capabilities tied to skilled people are the key to long-term success in these industries.

Arora, Fosfuri and Gambardella (2000) examine how the growth of markets for technology affected the corporate strategies of the leading companies, which can now sell technologies that they do not use in-house and increase their potential returns to R & D. They argue that globalization, along with the low transportation costs of technologies, has made large R & D intensive companies realize that they have the potential to exploit their technology on a very large scale by licensing. However, in deciding how to exploit their technology small firms and technology-based startups face a different set of challenges. According to the authors they need to trade off the costs of acquiring capital and building in-house production, distribution and marketing capability against the rents that would be lost or shared with their partners in a licensing deal. Also, the authors argue that integration may reduce the innovative potential of the firm, because the acquisition of the complementary assets inevitably increases the size of firms and induces important changes in the culture of the firm and in the speed and fluidity of information flows’. Finally, they claim that evaluating technologies and being able to use them requires substantial in-house scientific and technological expertise and therefore internal and external R & D can be reviewed as complements and not substitutes.

Liu (2000) focuses on a different issue by examining the markets’ reaction to innovation news announcement made by the U.S. biotech firms during the 1983-1992 period. He finds that the average AR to the announcements is as high as 3.98 percent for a three-day event window and biotech stocks trading volumes almost double on the day of the news announcement. The announcement period ARs are negatively related to firm-size and underwriter reputation, while positively related to the firm’s technology depth as measured by R & D intensity. However, during the months following the announcement the average three-month post announcement AR is 2.73 percent. The negative drift in stock prices appears to be mainly driven by the firm’s weak science and technology (less R & D intensive), firms with high Book to Market (B/M) ratios and large firms. In explaining his findings the author proposes an ‘expectation error hypothesis’. According to this hypothesis it is hard for investors or even managers to precisely evaluate the economic value of innovations which in turn leads to the possibility of forming erroneous expectations. In high-tech industries the erroneous expectation is reflected in the investor’s over-optimism towards high-tech firm’s innovation news. Eventually, the stock prices adjust itself to reflect the firm’s fundamentals, especially its technology depth. The author attributes the observed evidence to the costly information required to value a high-tech firm’s innovation.

Prentice and Fox (2002) provide a comprehensive review of the merger and acquisition process while focusing on the distinctive characteristics of high-tech companies. They argue that technology mergers are different from traditional mergers because of the importance that must be placed on people and their ability to innovate. Targets must be evaluated on intangible assets such as intellectual property and human capital. At the same time managers need to consider the issues of retention, culture and integration strategy from the beginning of the merger process to ensure success. There are two studies that are most relevant to this research. The first one is by Kohers and Kohers (2000) who examine the value creation potential of 1,634 mergers in the various high-tech areas between 1987 and 1996. They find that acquirers of high-tech targets experience significantly positive Ars at the time of the merger announcement, regardless of whether the merger is financed with cash or stock. Other factors influencing bidder returns are the time period in which the merger occurs, the ownership structure of the acquirer, the ownership status of the target and the high-tech affiliation of acquirers. They conclude that the market appears to be optimistic about such mergers and expects that acquiring companies will enjoy future growth benefits.

The second related study is also by Kohers and Kohers (2001) who examine the post-merger performance of acquirers that purchase high-tech targets in order to determine whether the high expectations regarding the future merits of these investments are actually justified. Their results indicate that compared to non acquirers, acquirers perform poorly over the three-year period following the high-tech takeover announcement. Furthermore, glamour bidders show significantly lower long-run ARs, while value bidders do not experience significant post-merger ARs. Also, glamour bidders with a higher risk of agency problems show even worse post-merger performance while institutional ownership in the acquiring firm has a positive influence on acquirer long run ARs. Overall, the authors conclude that the market tends to exhibit excessive enthusiasm toward the expected benefits of high-tech mergers but many of these benefits do not materialize.




Previous research in the literature has generally found little financial benefit for the acquiring companies that were associated with occurrence of the acquisition activity (Rumelt, 1974; Ravenscraft and Scherer, 1987; Porter, 1987; and Kaplan and Weisbach, 1990). Consequently, the first and second questions for this study are focused using the single industry of semiconductors, are stated in the null hypothesis format. First, firm profitability growth rates are compared in two groups, one that does acquire and one that does not. Secondly, individual firm profitability growth is examined before and after an acquisition event looking for a change in growth rate that is significant.

Hypothesis 1 (H1): There will be no significant difference in profitability growth when firms making acquisitions are compared to firms not making acquisitions in the high-tech sector.

Hypothesis 2 (H2): Acquiring firms making acquisitions are expected to have no significant change in profitability growth before and after the acquisition event.

The literature yields less empirical work in analyzing the relationship between merger and acquisition actions and the longevity of a corporation. Theory certainly recognizes the close link between competitive capability and company survival. For the high technology industry of semiconductors, high velocity innovation is a requirement for remaining competitive. Research questions three and four are also stated in the null hypothesis format. Company longevity, or survival rate in number of year, is compared in two groups also, where one group does acquire and one does not. Lastly, an individual firm’s spending rate on R & D is examined before and after an acquisition event looking for a significant change in the rate compared to the trend for the company.

Hypothesis 3 (H3): Firms making acquisitions are expected to have no difference in survivability in this industry than firms who do not make acquisitions.

Hypothesis 4 (H4): A company’s R & D intensity will show no significant change following the event of acquisition within this industry.


This research was conducted in a concentric approach by starting with one independent and one dependent variable initially to define the relationship and guide the next treatment in the study. As work continued, variables were selected and the methodology expanded to assess both within-subject and between-subject effects.

The variables used in this study for Hypothesis 1 (H1) include profitability growth rate and a dummy variable to represent the presence or absence of the event of acquisition. The event of acquisition is represented by a dummy variable with a zero (0) representing no acquisition and with a one (1) representing an acquisition event. An acquisition event is identified by using a firm’s reported cash flows attributed to acquisition as stated in the Compustat database. The profitability growth rate is calculated from the total gross profit margin reported by year and cumulated over three years, then averaged to reduce fluctuations and facilitate identification of trends.

The variables used for H2 analysis of profitability growth rate before and after an acquisition were the dummy variable for the presence of acquisition, the gross profit margin percentage (GPM %) calculated as a three (3) year cumulative average growth rate (CAGR) to smooth fluctuations and better identify a trend. This relationship was studied for three (3) years prior to the actual acquisition and five (5) years following the action. As the study progressed, a second dummy variable was used for company size to separate the effect of this independent variable as well. A repeated measures matrix was designed with two dummy independent variable as well. A repeated measures matrix was designed with two dummy independent variables, each with two levels and one dependent variable with repeated measures over nine years for a 2 x 2 x 9 repeated measures analysis using the SPPS software.

The variables used for H3 analysis of acquisition relation to firm longevity were the acquisition dummy variable and the data from Compustat for the number of years that the company did financial reporting during the period of this study.

H4 looks for the effects between acquisition and R&D spending or intensity by using the acquisition dummy independent variable and R & D intensity as the dependent variable. R & D intensity is calculated using the R & D expense reported as such by the companies and in the Compustat database. This Compustat item represents all costs incurred during the year that relate to the development of new products or services. This amount is only the company`s contribution and includes software and amortization of software costs and complies with Financial Accounting Standard Board (FASB) standards. This item excludes customer or government-sponsored research and development (including reimbursable indirect costs) and ordinary engineering expenses for routine, ongoing efforts to define, enrich, or improve the qualities of existing products.


This study encompasses the time period of ten years from 1990-2000, inclusive. Semiconductor companies were selected as an entire group according to their NAICS/SIC codes. Using the Standard & Poor’s Compustat database, there are 153 semiconductor companies included that were identified as ‘active’ companies at the end of the calendar year 2000 by Compustat. These companies are listed in Appendix B. Active reporting for one year. Companies are designated as ‘inactive’ and reclassified in the Compustat database when it is no longer actively traded on a stock market exchange due to bankruptcy, becoming a private company, leveraged buyout or merging.

The research effort started with analysis one independent variable and one dependent variable in order to initially establish what the relationship was that existed, if it was significant and how to proceed with analysis. Exploratory work on Hypothesis 1 showed that there was a statistically significant and positive correlation between acquisitions and gross profit margin (GMP) growth broadly over the decade which differs from prior research. Hypothesis 2 moves toward a more detailed analysis of this finding. Consequently, in this chronology of discovery, the next step presented in Section 4.2 look at one dependent variable of profit margin growth and two independent variables of company size and acquisition activity. 3-way ANNOVA and regression treatments of the data are conducted using the data analysis tool available under Microsoft Excel Software looking at individual years in the ten year study period. The results show significance again and suggest that other interactions between variables would yield additional understanding. The next step in the research was set up to look at one dependent variable, again gross margin (GPM) growth, repeatedly measured over time for each subject or company was entered for the nine (9) years 1995-2000 inclusive to capture acquisition effects giving 2 x 2 x 9 repeated measures design. The two independent variables were used in the dummy format with non-acquires given a code zero ‘0’ and acquires assigned at one (1). Company size was the second dummy variable with firms less than $100M in sales per year coded zero (0) and if greater than $100M in sales, assigned a one (1). The statistical analysis using a repeated measures design analyzed the variable interactions and their relationship to GPM growth using the SPSS software. These results are presented in Section 4.5 Repeated Measures Analysis that was done using SPSS software.

Descriptive statistics were an important first treatment of the data sets created. This includes the values for the following parameters: mean, median, range variance, standard deviation, kurtosis, and skewness. This treatment looks at characteristics of the data and the degree of normal distribution. The 3-way ANOVA investigations and regression treatment of the data were initially done using the data analysis tool software available in Microsoft Excel. Generally, the data sets for this study vary somewhat from the classical normal distribution, but ANOVA and MANOVA (multivariate ANOVA) within a repeated measures analysis are considered robust to violations of the normal distribution assumption (Maxwell & Dealney, 1990; Stevens, 1996)

SPSS Advanced Models 11.0 software was used to create general linear models of the data and conduct analysis of variance (ANOVA), regression, and analysis of covariance (ANCOVA) for the multiple variables in this model with repeated measures. The factors or independent variables were used to divide the population of 153 active semiconductor companies into groups. There were two independent variables used that were designated as dummy variables. The first variable of acquisition separated companies that did complete acquisitions from those that did not complete acquisitions during the decade of study. The second variable grouped the companies by size of sales at the end of the decade by either greater than $100 million or less than $100 million. Then the general linear model procedure was used to test the four null hypotheses, as stated above, regarding the effects of the independent variables on the dependent variable of gross profit margin growth as a repeated measure over the period 1992-2000. The investigation included looking at interactions between factors as well as the individual factors and the effects and interactions of covariates. This model specifies the independent variables as covariates for regression analysis.

The SPSS repeated measures model creates a matrix for the sums of squares due to the model effects, gives the approximate F statistics and estimates parameters in addition to testing hypotheses. When an F test shows significance, SPSS performs post hoc tests to evaluate the differences between the means. This yields a predicted mean value for the cells of the model.

Analysis of variance (ANOVA) was applied to named variables to study the portion of variance in the each variable that could be identified as explained and unexpected with regard to the event of acquisition.

A covariance tool was also used when looking at the variables described above such as acquisition occurrence, company size and profitability growth changes. This compares whether the two ranges of data move together – that is, whether large values of one set were associated with large values of the other (positive covariance), whether small values of one set were associated with large values of the other (negative covariance), or whether values in both sets were unrelated (covariance near zero).


Standard & Poor’s Compustat database was used for data collection in this research. The database contains fundamental financial, statistical and market data derived from publicity traded companies trading on the NYSE, NASDAQ, AMEX, OTC and Canadian stock exchanges.

The calendar year for a company is the year in which the fiscal year ends and is the time period used as standard in this research. Companies with fiscal years ending in January through May are assigned by Compustant into the year in which the fiscal year begins. Companies with fiscal years that end in June through December are assigned to the year in which the fiscal year ends.

The EDGAR (Electronic Data Gathering, Analysis and Retrieval) System database maintained by the United Stated Security and Exchange Commission (SEC) was also used. The EDGAR data is also collected from the same sources that are used to generate the Compustat database. Data from these controlled and verifiable sources were corroborated and augmented with information collected from semiconductor trade journals, company annual reports and the Mergers & Acquisitions Journal that tracks statistics in this area.




A strong positive relationship was found to exist between the presence of acquisition activity and the growth in gross profit margin (GPM) by the end of the ten year study period. The statistical analysis is detailed below and is a departure from previous findings. This finding addresses the central question of this research endeavor to look for a relationship between acquisition events and profitability growth within the one industry of semiconductors. A positive financial effect is found and opens the path for additional analysis in this direction. Consequently, this information forms the foundation for the additional work presented in this research.

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