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An investigation of post-merger supply chain performance.
Subject:
Business performance management (Analysis)
Logistics (Management)
Acquisitions and mergers (Influence)
Author:
Langabeer, Jim
Pub Date:
03/01/2003
Publication:
Name: Journal of Academy of Business and Economics Publisher: International Academy of Business and Economics Audience: Academic Format: Magazine/Journal Subject: Business; Business, general; Economics; Government Copyright: COPYRIGHT 2003 International Academy of Business and Economics ISSN: 1542-8710
Issue:
Date: March, 2003 Source Volume: 2 Source Issue: 1
Topic:
Event Code: 200 Management dynamics Computer Subject: Company business management
Geographic:
Geographic Scope: United States Geographic Code: 1USA United States

Accession Number:
113563628
Full Text:
ABSTRACT

This study examined the relationship between mergers and acquisitions and operational performance in the manufacturing and logistics environment (i.e., supply chain). The research compares how supply chain performance is impacted by merger activity, by analyzing both pre-merger and post-merger performance during the 1990 to 2000 timeframe. Findings suggest that there is a negative relationship between the volume and intensity of mergers with overall supply chain performance, or specifically that mergers have a negative impact on supply chain performance. Findings also indicate that this negative relationship was substantially moderated by the size of the target acquisition. Conclusions drawn from these findings suggest that this negative relationship is at least partially due to the lack of attention that critical supply chain processes are afforded prior to the transaction.

1. INTRODUCTION

Mergers and acquisitions have become common business strategies in increasingly competitive environments (Rappaport, 1981). The use of mergers and acquisitions continues to grow nearly exponentially, with over 6,000 M&A transactions conducted globally in 2001, valued at over a trillion dollars (Mergers and Acquisitions, 2002).

Yet, despite the frequency of their use, there continues to be significant scholarly debate over whether mergers and acquisitions are successful in achieving their objectives (Lajoux & Weston, 1998). Some studies have found that fewer than 25% of all mergers achieve their stated strategic objectives (Marks & Mirvis, 2001). This of course depends on the motives and the rationale of the two firms prior to embarking on the merger.

Historically the strategic rationale for mergers has been organized around four different competitive effects: improving competitive positioning, extending product portfolios, leveraging economies of scale, or re-defining scope and industry. Based on these strategic objectives, researchers have previously isolated the specific measure that would define success for each of these cases, which almost always has been measured in financial terms. Figure 1 summarizes the traditional financial measures of evaluating M&A success.

Most of the empirical studies that have been conducted focus on the relationship between mergers and these high-level financial indicators (e.g., shareholder value, profitability, and rate of stock returns). There has been no empirical research however that focused on more operational indicators of merger success specifically relating supply chain effectiveness to M&A activity. Previous research has not however explored the real critical relationships between mergers and non-financial performance measures. Specifically, since operations (i.e., manufacturing and logistics) tend to influence and display positive correlations with financial performance, more effective and efficient operations are expected to result in better financial results (Fawcett & Closs, 1993). A better understanding of this linkage between mergers and operational performance will help to explain reasons why mergers have a negative effect on financial results.

Therefore, despite the fact that the relationship between M&A and financial performance is of interest, my focus here is to specifically concentrate on the relationship between supply chain performance and mergers and acquisitions. Supply chains are defined here as the processes involved in procuring, storing, manufacturing, and delivering goods, information, and cash between buyers and suppliers (Simchi-Levi, 1999; Cooper et al, 1997). Supply chain effectiveness is increasingly becoming a concern due to the amount of resources consumed in these processes and because of the recent research tying supply chains to overall corporate strategy (Langabeer, 2001; Vedra Beach, 2001; Harrison et al, 2002).

Operational performance from supply chains are typically measured in inventories, working capital, customer service levels, delivery costs, and overall asset and employee utilization. These indicators are important measures of a firm's ability to meet customer's needs in a cost effective manner, which ultimately will decide the firm's fate in the long run.

2. THEORETICAL FOUNDATION AND HYPOTHESES

As stated previously, there has been no empirical research analyzing the effect that mergers and acquisitions have in either contributing or detracting from the performance of the supply chain. The broad research question being asked here is: When one firm merges with another, is the new organization capable of integrating the new business and then maintain or improve actual supply chain business results? Since supply chains represent the operational manufacturing and logistical processes, and can often consume between 25-75% of a firm's total expenses and resources, is it logical to assume that a new business model can immediately replace the two existing ones, and that this newly integrated firm can generate better operating results?

2.1 Merger-Performance Theory

This study involves an exploration of how supply chain operational performance is related to merger and acquisition activity. The literature in the area of performance is extremely divided for reasons: first, previous research has focused only on financial measures of performance and less on operation-specific metrics; and second, the financial performance relationships that have been established with mergers are not conclusive. Tetenbaum found that between 60% and 80% of all mergers are financial failures (1999). Lajoux and Weston find in their meta-analyses similar results (1998). Brouthers found that indeed using traditional measures of operating and financial performance, most mergers will result in lower performance post-merger, but goes on to point out that possibly the wrong performance indicators are being measured (1998).

Agrawal found that acquiring firm's relative returns decreased by 5% over a five-year period during the post-merger period (1992). Similarly, other studies have found that most mergers fail to produce the kinds of performance gains expected in the pre-merger stages (Ashkenas et al, 1992). Healy, Palepu, and Ruback (1992) find conflicting results, suggesting that merged firms exhibit higher levels of financial effectiveness than control groups in the same industry. Switzer came to similar conclusions that post-acquisition the combined firm exhibited better performance than otherwise would have been expected (1996). Franks, Harris, and Titman reached similar conclusions using a larger sample from the 1980's (1991). Overall, however the majority of the research appears to support the hypotheses that mergers and acquisitions have a negative effect on financial performance measures.

Interestingly, despite the conflicting findings, none of these studies analyzing performance and M&A actually relate variables representing supply chain performance. Additionally, there is inconsistency in the findings with other measures of operational performance preventing generalization to the areas of manufacturing and logistics.

Since financial performance has been found to have statistically significant negative relationships with mergers and acquisitions, one could assume that M&A also has a negative impact on operational performance and specifically on supply chain indicators. Based on this, I predict that mergers will have a negative impact on post-merger supply chain performance.

Hypothesis 1: The post-merger supply chain performance for firms will be significantly less than the pre-merger performance. This hypothesis is grounded in two foundations: a) firms have not historically made decisions about mergers on the basis of a supply chain's capability to integrate new operations, and b) as firms strive to adapt to the new environments and supply chain business practices required for the combined firm, significant time will pass before they will see better results. In other words, firms merging multiple supply chains will experience a learning curve effect, which requires them to understand the new operations that will temporarily cause performance to decrease for some period of time before possibly recovering.

Building on this first hypothesis, that firms cannot assimilate or integrate the new supply chain business models and processes, the lack of tight integration is expected to result in reduced performance in the periods following a merger. Additionally, since some firms are participating in dozens of merger and acquisitions annually, taking this logic further would imply that the greater the intensity of mergers, the lower the expected supply chain performance. This second hypothesis explores the impact of mergers on supply chain performance in a multivariate model.

Hypothesis 2: Increased intensity of mergers will be negatively related to supply chain performance. In general, I am assuming a research model similar to that in Figure 2.

2.2 Supply Chain Performance Theory

The production and distribution functions within firms are responsible for effectively and efficiently moving products through the various stages in the chain in order to ultimately satisfy a customer demand. The better that firms are at this, the more likely they will be to enjoy an operational competitive advantage. Early research on operational effectiveness clearly pointed to the value chain as a driver for improved performance (Porter, 1985; Milgrom & Roberts, 1991).

More recently, supply chain performance is starting to be viewed as a competitive differentiator (Harrison & New, 2002; Vokurka et al, 2002).

Firms that can quickly respond to changing demand patterns, can ensure consistently higher customer service levels, and can deliver goods more efficiently at lower costs have higher levels of operational effectiveness, which ultimately results in better financial and business results. Flexibility, cost efficiency, responsiveness, reliability, and quality are all essential components of excellent supply chain performance. Exploring relationships between these types of performance variables in the supply chain with merger transactions is the purpose of this research.

3. METHODS

To test these hypotheses, I examine the relationship between a number of variables related to supply chain performance and mergers and acquisitions over a ten-year period, 1990 to 2000. Although there were over 65,000 mergers transacted in the 1990's alone, this research focused on a specific category that appeared as one of the most active M&A industries each year during this time frame. The SIC code 28 (i.e., chemical, pharmaceutical, and allied products) was selected mainly because of the number of firms in this category and the extent to which this category relies on M&A as a competitive strategy. Additionally, using only one industry reduces the need for wide distribution and variation in performance inherent between different industries. This therefore eliminates the need for inter-industry normalization of data.

3.1 Sample and Sources

The research examined this subset of mergers and acquisitions announced and completed during 1990 to 2000 in SIC 28, representing over 1,200 transactions. Since the analyses rely on transactions where comprehensive financial and operating information is publicly available for that timeframe, many firms were excluded where gaps or incomplete data existed. Complete data were available on approximately one-third of these, representing 397 deals.

The source of the merger data for this sample is the Securities Data Company's (SDC) Mergers and Acquisitions database. Financial and supply chain information was available from Hoovers and Lexis-Nexis databases, and the industry operating metrics were available from the American Chemistry Council. Additional data was derived from Standard and Poor's Industry Outlooks.

3.2 Variables

The variables being explored include a measure of merger intensity, six measures of supply chain performance, three control variables, and a pre-merger and post-merger performance factor. These measures were defined as follows.

Cost Per Shipment. This variable measures the ability of a supply chain to control the landed or delivered costs between the manufacturer and the customer. For most industries, shipment costs are a critical measure of supply chain performance for firms deploying a cost leadership supply chain strategy. A lower cost per shipment indicates higher levels of supply chain performance. Calculation of this variable was based on actual operational expenses divided by an estimate of the number of shipments annually. A shipment estimate was calculated by taking the total number of reported industry-wide shipments, and multiplying a relative share based on revenue for each firm in the analysis.

Inventory Turns. This variable measures a firm's efficiency in "turning" or moving inventory. In general, the greater the number of turns, the more efficient the firm is in managing their inventories properly, since a unit of inventory will sit on the shelf non-productively consuming working capital less time. Greater turns typically means fewer items sitting idle in warehouses or plants, and indicates higher performance.

Labor productivity. This variable is a measure of a supply chain's efficiency. This variable measures the employees involved per shipment. The fewer the number of employees involved, the higher the level of efficiency and performance.

Capacity Utilization. This variable is a standard measure of a firm's manufacturing productivity, reflecting the efficiency to which products are manufactured. It is an operational measure of asset consumption or yield. The higher the utilization rate, the better the supply chain performance. Since this information was not available for each firm, an industry dummy variable was used to represent the firm's approximate utilization.

Total Stock in Inventory. This variable measures the total dollar value of inventory placed in inventory at the end of the reporting period. The higher the figure, the greater the amount of working capital, which is typically considered inefficient supply chain performance.

Operating Margin per Shipment. A standard measure of operating performance, this measure indicates a firm's ability to achieve prices in excess of the costs required. Defined as ((price less costs) * total number of shipments) / number of shipments, the greater this percentage the better the operating performance of the supply chain.

Merger Value. This measure indicates the dollar value of the merger transaction. It indicates the both the volume and the intensity of the transaction. This calculation is a sum of the total dollar value reported, regardless of the form of payment (e.g., cash, stock swaps, other financing). All mergers are reported in US dollars.

Relative Target Size. This control variable is used to measure the relative size of the acquisition target for the transaction. Obviously one would expect that the larger the firm being acquired, the more difficult it would be to successfully integrate it in the new organization. This variable is defined as the assets of target / assets of combined firm.

Pre-Merger Performance. This variable reflects a measure of the historical supply chain performance in the year before the merger (t-1). This measure was calculated by using principal component factor analysis to factorize three variables into one linear combination, in order to compare pre- versus post-merger performance. Specifically, the factor was a result of factoring inventory turns, total finished goods inventory, and operating margin.

Post-Merger Performance. This variable reflects a measure of the merger one-year post integration (i.e., where t=0 is the merger year, this variable represents t+1). As with the measure of pre-merger performance, this is calculated by using principal component analysis to factor inventory turns, total finished goods inventory, and operating margin.

Sales. This measure reflects the total dollar value of sales for the firm.

Return on Assets. Although not a supply chain performance indicator, this variable is used as a financial performance control variable. Typically, ROA is the financial indicator used in most empirical studies to analyze the effect of mergers on financial performance. It is defined here as operating income / total assets.

Table 1 represents the sample distribution based on the year of the merger and average size of the transaction. The average size of the transaction did not vary much from the 1980's to the 1990's, as the mean number of transactions completed annually remained near 110 per year. The average size of the merger however went from around $70 million to around $98 million during this same period.

To test the hypothesis regarding the pre- versus post-merger impact on the supply chain, statistical distribution tests were conducted to examine if significant differences in the means or standard deviations exist in the pre versus post performance basis. Both t-tests and F-tests were conducted to test the first hypothesis. Factor analysis was used to combine the principal components of supply chain performance into one independent variable. This variable represented three of the six measures of supply chain performance into a discrete factor. The pre-merger performance was this factor for year t-1, and the post-merger performance was for year's t+1. The factor analysis produced one factor, which accounted for approximately 72% of the variability in the data. The factor loadings and eigenvalues for the analyses are presented in Table 2.

To test hypothesis 2, a multivariate regression model was tested against merger intensity. This technique used OLS regression, taking into account all of the variables described above except for the pre- and post-merger performance variables, since these were tested in hypothesis 1. Where needed to augment the tests, univariate correlation analysis was conducted between the merger variable and the supply chain performance variables. Table 3 presents the descriptive statistics and correlation coefficients.

4. RESULTS

Both hypotheses were tested with ordinary least squares regression or distribution analysis such as t-tests and F-tests. Therefore, assumptions of normality and linearity in the data, as well as a lack of noncollinearity were tested and applied to the data.

Hypothesis 1 posits that the supply chain post-merger performance will be significantly less than pre-merger performance. This hypothesis was supported by the analysis. The three variables that were factored into a pre-merger and a post-merger performance variable had a mean of 0.23 and 0.01 respectively. As seen from these figures of the pre and post-merger groupings, pre-merger performance was significantly better for firms in year t-1 than post-merger t+1. T-tests confirm that indeed these differences in the means are statistically significant (t=0.65, p<.05). Similarly, F-tests were used to examine the differences in the distribution and deviations and found statistical significance in the difference of the groups (F=0.127, p<.01). This difference in the means between the pre- and post-performance groups suggests that mergers and acquisitions had an overall negative effect on performance. In summary, both the t-test and the F-test clearly show that the post-merger performance was statistically significantly less than the pre-merger performance across the entire population, which fully supports hypothesis 1.

The second hypothesis builds on the first by looking for negative relationships between the volume and intensity of mergers with measures of supply chain performance. The second hypothesis was also fully supported by the research. Findings suggest that there is a statistically significant negative relationship between mergers and supply chain performance. Using both multiple regression techniques and individual correlation analyses, statistical results show that the greater the volume and intensity of mergers, the lower the expected post-merger logistical performance in all areas of the supply chain ([R.sup.2]=.765, p<.001). Those variables that had the statistically significant relationships in the regression models include those representing supply chain costs, labor productivity, and inventory. In other words, there was a positive relationship between these variables and mergers. Additionally, it was discovered that the relative size of the target merger helped to moderate the impact on supply chain performance. Thus, as firms attempt to merge with larger firms (i.e., relative to the original size of the firm) they are less likely to have better post-merger supply chain performance. Table 4 presents the regression results.

Additionally, as independent Pearson correlations prove, mergers have a negative relationship with inventory turns (r=-0.489. p<.05), operating margins (r=-0.565, p<.001), and number of employees per shipment (r=-0.586, p<.001). Additionally, mergers had a positive relationship with costs (r=.672, p<.001) and with total stock or inventory (r=.644, p<.001). Only one variable, capacity utilization, did not have a statistically significant relationship with mergers, and this is probably explained away since an industry dummy variable was used in lieu of specific firm measures. Of additional importance is the fact that the fact that the relative size of the target group showed a statistically significant negative relationship with supply chain performance (r=-.394, p<.001).

5. DISCUSSION

Interest in how supply chains can create competitive advantages for firms has grown significantly in recent years. So too has the use of mergers and acquisitions as a business strategy for competing in challenging environments. This research is grounded in the belief that these two strategies are often in conflict with another. If supply chains are to become an advantage, by producing and distributing products at the right time with lower costs, then the impact that increasingly common mergers will have on this is of significance. This research is the first of its kind to explore the impact that mergers and acquisitions have on supply chain performance.

The research foundation partially suggests that operational aspects of firms cannot absorb or integrate new business models, which might support why mergers tend to fail more often than they succeed in delivering desired objectives (Lajoux & Weston, 1998). Specifically, the results show that mergers and acquisitions have a negative influence on the supply chain's ability to perform effectively and efficiently. The findings suggest areas that might help researchers and practitioners better understand the influences and their contributing factors. The research suggests that firms' mergers have a negative or destructive impact on manufacturing and logistics performance. Since operational performance is believed to drive financial and strategic performance, these findings are of particular importance.

If both of these hypotheses hold true, an important question must be addressed: if mergers tend to have a negative impact on supply chain performance, why do firms merge at all? Since supply chain performance is a key driver of operational performance, which tends to have a direct relationship with the firm's financial performance (Heron & Lie, 2002), then one would question whether the issue of supply chain integration is even addressed in the pre-merger planning stage. These types of supply chain issues need to start being planned and discussed prior to the merger, so plans can be developed to negate the otherwise destructive influences on supply chain results.

Limitations and Future Research

This research is significant in identifying that mergers have a negative influence on supply chain performance. It has identified an important problem that needs to be further identified. This research however, is still in its infancy and future research needs to expand the scope and overcome potential limitations in these analyses. Future research can be improved in a few ways. I have identified here a supply chain performance taxonomy around five interrelated performance metrics: efficiency, quality, reliability flexibility, and responsiveness. This current research used primarily accounting data to measure these relationships, and thus focused on supply chain efficiency more than the other four indicators of performance. This is because obtaining how flexible or responsive a firm is cannot be readily captured across different firms, since no consistent metric is reported or even calculated to make these comparisons easily. Future research should use different methods (e.g., surveys) to estimate and capture this type of information, in order to identify if the same findings can be generalized to all areas of performance.

Further, this research is conducted within one SIC code. Although the distribution and competitive intensity of these industries appear to be fairly normal, and the size of the industry makes it one of the top ten largest, further research should broaden to multiple SIC codes. This will ensure broad generalization across all industries and market segments.

Finally, the pre- versus post-merger performance used a time period of one year before, the merger year, and one year following the transaction. This three-year view provides a relatively long period of analysis given the quarterly focus that stock markets and other financial institutions place on operational management. However, the definition of this time horizon should be broadened to define the relative year at which supply chain performance will stabilize and ultimately start to exceed pre-merger levels of performance. Obviously based on this research, the soonest that performance will recover is year t+2, but further research would benefit on capturing additional time periods.

7. References

Agrawal, A., Jaffe, J., Mandelker, G. The Post-Merger Performance of Acquiring Firms: A Re-Examination of an Anomaly. The Journal of Finance, 47 (4), 1992: 1605-1622.

Ashkenas, R.N., DeMonaco, L.J., & Francis, S.C. "Making the Deal Real: How GE Capital Integrates Acquisitions". Harvard Business Review, January 1998.

Brouthers, K., Van Hastenburg, P., & Van den Ven, J., If Most Mergers Fail, Why are they so Popular? Lonq Range Planning, 31 (3), 1998: 347-353.

Cooper, A., Lambert, D., & Pag, J. Supply Chain Management: More Than a New Name for Logistics. International Journal of Logistics Management, 8 (1), 1997:1-14.

Fawcett, S. & Closs, D. Coordinated Global Manufacturing, the Logistics/Manufacturing Interaction, and Firm Performance. Journal of Business Logistics, 14, (1), 1993:1-26.

Franks, J., Harris, R, & Titman, S. The Post-Merger Share Price of Acquiring Firms. Journal of Financial Economics, 29, (1), 1991: 81-97.

Harrison, A. & New, C. The Role of Coherent Supply Chain Strategy and Performance Management in Achieving Competitive Advantage. The Journal of the Operational Research Society, 53 (3), 2002: 263-271.

Healy, P.M., Palepu, K., & Ruback, R.S., Does Corporate Performance Improve After Mergers? Journal of Financial Economics, 31, 1992: 131-175.

Heron, R. & Lie, E. Operating Performance and the Method of Payment in Takeovers. Journal of Financial and Quantitative Analysis, 37 (1), 2001: 137-155.

Lajoux, A.R. & Weston, J.F. Do Deals Deliver Post-Merger Performance? Mergers and Acquisitions, 33 (2), 1998: 34-37.

Langabeer, J.R. & Rose, J. Creating Demand Driven Supply Chains. Oxford: Chandos Publishing, 2001.

Marks, M.L. and Mirvis, P.H. Making Mergers and Acquisitions Work. Academy of Management Executive. 15 (2), 2001: 80-94.

Milgron, P. & Roberts, J. The Economics of Modern Manufacturing: Technology, Strategy, and Organization. American Economic Review, 81, 1998: 84-88.

Porter, M.E. Competitive Advantage. New York: The F tee Press, 1995.

Rappaport, A.P. Selecting Strategies that Create Shareholder Value. Harvard Business Review, May-June, 1981.

Securities Data Company. Mergers and Acquisitions. M&A Profile. May/June, 1991-2001.

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. Designing and Managing the Supply Chain: Concepts, Strategies, and Cases. New York: McGraw-Hil, 1991.

Switzer, J. Evidence on Real Gains in Corporate Acquisitions. Journal of Economics and Business, 48, (5), 1996: 443-461.

Tetenbaum, T. Beating the Odds of Merger and Acquisition Failure: Seven Key Practices that Improve the Chance for Expected Integration and Synergies. Organizational Dynamics, 28 (2), 1999: 22-36.

Vedra Beach, P. Retail Operational Strategies in Complex Supply Chain. Journal of Logistics Management, 12 (1), 2001: 97-111.

Vokurka, R., Zank, G., & Lund, C. Improving Competitiveness through Supply Chain Management. Competitiveness Review, 12, (1), 2002: 14-25.

Jim Langabeer, Boston University

Author Profile

Dr. Jim Langabeer earned his doctorate from the University of Houston in 1996. Currently he is on the graduate faculty at Boston University's department of Management Sciences.
TABLE 1: SAMPLE DISTRIBUTION BY YEAR

                          N          Mean
        Total M&A     (compete    Transaction    % of
Year   Transactions   data set)   Value ($ M)   Firms

1990        67           21          $56.1        5%
1991        29           18         $117.9        5%
1992        94           32          $48.6        8%
1993        97           42          $88.9       11%
1994        94           29          $25.9        7%
1995       130           30          $52.8        8%
1996       156           41          $90.8       10%
1997       139           35         $142.8        9%
1998       157           39         $163.0        1%
1999       149           48         $150.5       12%
2000       162           62         $111.8       16%

TABLE 2: PRINCIPAL COMPONENT FACTOR ANALYSIS

PRE-MERGER PERFORMANCE

Factor                Percent of   Cumulative
Number   Eigenvalue    Variance    Percentage

  1      2.16796        72.265       72.265
  2      0.653169       21.772       94.037
  3      0.178875        5.963      100.000

Factor                Percent of   Cumulative
Number   Eigenvalue    Variance    Percentage

  1       2.532591     72.407        72.407
  2       0.653169     20.005        92.412
  3       0.178875      7.588       100.000

TABLE 3: DESCRIPTIVE STATISTICS AND REASON COEFFICIENTS

         Variable              Mean         s.d.     1          2

 1 Capacity utilization         78.56       3.11              -0.02
 2 Costs per shipment          244.70      82.57   -0.02
 3 Employees per shipment     1055.18      23.99    0.38      -0.33
 4 Finished goods            32867.50    8268.43   -0.04       0.99 **
   inventory
 5 Inventory turns per           8.45       0.67    0.39       0.83 **
   year
 6 Operating margin              6.55       2.97    0.30      -0.52 **
 7 Return on assets              4.83       2.33    0.13      -0.56 **
 8 Sales                    281748.00   92759.30    0.02       0.99 **
 9 Relative target size          0.42       0.23   -0.57 **   -0.20
10 Mergers                      88.10      32.10   -0.05       0.67 **
11 Pre-Merger Performance        0.23       0.53   -0.27       0.85 **
12 Post-Merger                   0.01       1.47    0.04       0.94 **
   Performance

         Variable               3          4          5           6

 1 Capacity utilization        0.38      -0.04       0.39 **    0.30
 2 Costs per shipment                     0.99       0.83      -0.52 *
 3 Employees per shipment                -0.33       0.05       0.56 *
 4 Finished goods inventory   -0.33                  0.80 **   -0.53 *
 5 Inventory turns per year    0.05       0.80 **              -0.39
 6 Operating margin            0.56 **   -0.53 **   -0.39
 7 Return on assets           -0.04      -0.61 **   -0.38       0.22
 8 Sales                      -0.28       0.99 **    0.87 **   -0.53 *
 9 Relative target size       -0.43       0.21      -0.39      -0.16
10 Mergers                    -0.59 **    0.64 **   -0.49 *    -0.57 **
11 Pre-Merger Performance     -0.47       0.84 **    0.69 **   -0.84 **
12 Post-Merger                -0.31       0.93 **    0.88 **   -0.72 **
   Performance

         Variable               7          8          9         10

 1 Capacity utilization        0.13       0.02      -0.57      -0.05
 2 Costs per shipment         -0.56 *     0.99 **   -0.20       0.67 **
 3 Employees per shipment      0.04      -0.28      -0.43      -0.59 **
 4 Finished goods inventory   -0.61       0.99 **   -0.21       0.64 **
 5 Inventory turns per year   -0.38       0.87 **   -0.39      -0.49 *
 6 Operating margin            0.22      -0.53 *    -0.16      -0.57 **
 7 Return on assets                      -0.57 *     0.34      -0.17
 8 Sales                      -0.57 **              -0.24       0.65 **
 9 Relative target size        0.34      -0.24                 -0.18
10 Mergers                    -0.17       0.65 **   -0.18
11 Pre-Merger Performance     -0.38       0.85 **   -0.02       0.73 **
12 Post-Merger                -0.49 *     0.96 **   -0.39 **    0.67 **
   Performance

         Variable               11         12

 1 Capacity utilization       -0.27       0.04
 2 Costs per shipment          0.85 **    0.94 **
 3 Employees per shipment     -0.47 *    -0.31
 4 Finished goods inventory    0.84 **    0.93 **
 5 Inventory turns per year    0.69 **    0.88 **
 6 Operating margin           -0.84 **   -0.72 **
 7 Return on assets           -0.38      -0.49 *
 8 Sales                       0.85 **    0.96 **
 9 Relative target size       -0.02      -0.19
10 Mergers                     0.73 **    0.67 **
11 Pre-Merger Performance                 0.92 **
12 Post-Merger                 0.92 **
   Performance

* p<.05

** p<.01

TABLE 4: RESULTS OF REGRESSION ANALYSIS

                                  Mergers:
Variable                        Final Model

Capacity utilization
Costs per shipment                 274.95 **
Employees per shipment             194.00 **
Stock Inventory                      2.14 *
Inventory turns per year
Operating margin per shipment
Return on assets
Sales
Relative target size            -12334.60 *

[R.sup.2]                            0.77 **
Adjusted [R.sup.2]                   0.70 **

* p<.05

** p<.01
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