1
Explanation of the post-acquisition performance of the combination
using accounting data.
Rogier Grootkop, 10499326
Amsterdam Business School, University of Amsterdam
Plantage Muidergracht 12 1018 TV Amsterdam
The Netherlands
Introduction
Drawing on work from the accounting based mergers and acquisition (M&A) research in business and economics, this research attempts to map a set of explanatory factors of the outcome of the
post-acquisition economic performance on accounting data as provided in balance sheet and income statement of companies. The explanatory factors of the post-acquisition performance are selected by observing the literature of the different disciplines in M&A research field. The factors which can be conceptualized by using accounting data are verified on the ability to explain the difference in post-acquisition economic performance of the acquirer and target before, and the combination of the two companies after the merger or acquisition.
Theoretical Background
There are three major theories which can be applied to M&A (Montgomery 1994). The marketed power view, the agency view and the resource based view. The market based view, looks at the anti-competitive effects of M&A. Market power can be gained by both types of M&A. Horizontal acquisitions, with the aim to get more market share by acquiring competitors and vertical acquisitions with the purpose to extent the value chain and disruption the power of suppliers and/or customers. According to the agency view, managers pursue M&A for a couple of reasons. First reason is to reduce the corporate risk, hence ensuring their own position by reducing the change negative results, inducing personal sanctions by shareholders. Second reason is the increase of the firms demand for the skills of top managers and the ultimate reason is empire building. The resource based view explains the M&A from the constrains of resources in terms of capabilities and time a firm encounters. A company can choose to develop these resources internally or acquire them externally, but the internal development of resources takes often a
2 long time (Nelson and Winter 1982, Singh and Montgomery 1987), subsequently many firms seizure the market to acquire these new resources (Capron et al. 1998). Because the lacking resources are often not separately available on the market, firms have to acquire a bundle of resources, typically in a form of a complete company. The resources of interest have to be extracted from the acquired firms (Barney 1986) and redundant or unwanted resources have to be divested. A crucial topic explained by the resource based view is to what extent the combination of firms have integrated certain resources after a M&A, with the aim to achieve a stronger competitive position.
Disciplines
Scholars from different disciplines have researched different M&A topics. First, Strategic management academics looked at the strategic combination motives firms (Ansoff et al 1971, Trautwein 1990, Walter & Barney 1990) or strategic relatedness ( Lubatkin 1983) and how relatedness do have an effect on the M&A performance (Singh & Montogomery 1987, Lubatkin 1987, Shelton 1988, Seth 1990, Datta et al. 1992). Second, Ecomonic scholars looked also to the economic combination motives (Steiner 1975, Scherer 1980, Goldberg 1983). Furthermore, they investigated the M&A performance from an economic perspective based on accounting bases measures (Ravenscraft and Scherer 1987). Third, Finance
researchers measured M&A performance using Stock Market based measures (Jensen and Ruback 1983, Jarrel et al. 1988). Forth, organization science scholars have investigated organizational integration of the combination (Searby 1969, Yunker 1983, Shrivastava 1986, Pablo 1994) and the subsequent employee’s reactions in the case of cultural clashes between the two firms (Sales and Mirvis 1984, Nahavandi and Malekzadeh 1988) and in what manner conflict resolution is handled between the two firms (Blake and Mouton 1985). And final, Human Resource scholars have looked at the employee reactions on
communications (Sinetar 1981, Schweiger and DeNisi 1991) and the employee reactions on the career implications of the M&A (Hirsch 1987, Walsh 1989, Hambrick and Cannella 1993).
Strategic management method
There are different strategic management reasons for acquisitions. First, the cost driven acquisitions, aiming making stand alone cost improvements or remove cost redundancies from integrating the firms. Examples are economies of scale (Bain 1959), vertical economies (Harrigan 1984), economies of scope (Seth 1990) or cutting staff and administration costs by the introduction of shared services. Second, the revenue-driven acquisitions, with the goal cross selling opportunities by access new customers and market segments. And third, the learning driven acquisitions, with the intention to attain new technological
3 competences by knowledge transfer and create new products or services (Zollo and Meier 2008). Forth, acquisition to gain market power or purchasing power (Porter 1977, Charterjee 1986, Scherer 1980) and finally, the financial driven acquisitions, with different motives like risk diversification or coinsurance (Lubatkin 1983, Seth 1990).
Financial-based method
The Acquiring Company’s M&A corporate finance performance is often measured using event studies (Agrawal et al., 1992, McWilliams, Siegel, 1997). The cumulative abnormal returns (CAR) of stock during a short period around the announcement of the acquisition are the subject of interest. This measure of M&A performance represents the potential expectations of the financial market and not the long term financial performance of the new combination. Analyzing stock prices of the combination over a longer period after the acquisition is a method of measuring, but should be compensated with the industry and macroeconomics trend. In the event of a not related M&A, for instance by conglomerates, it is nearly impossible to compensate for the industry trend, for the reason that the conglomerate is active in multiple industries. Also many other factors which influence the stock prices, e.g. top management turnover which not related to the actual performance of the M&A, will induce significant measuring errors. Another important factor is the relative size of the acquired firm in comparison to the acquiring firm. A relatively small acquired firm will probably not significantly influence the stock price of the acquiring firm unless the combination impact for the acquiring firm is enormous. Since this research is based on accountancy data, the financial-base method is submitted.
4
Accounting-based method
The consolidation of accountancy data of the new combination and the industry and macroeconomics trend is problematic for the accounting-based method. Many accounting-based M&A research is done at the firm level, which means that the acquired firm accounting data is consolidated and only the overall performance of the new combination after the M&A can be investigated. Especially in case of relative large company size difference between the acquired firm and acquiring firm, the effect on the
consolidated data will probably be non-significant. In case of a conglomerate, it will be nearly impossible to measure the performance of individual M&A’s since the conglomerate often has acquired more firms during the same time span of interest. The industry and macro influences on the accountancy performance can be compensated by comparing it to independent companies in same industry and with the same size as the profit center. As been done by prior researchers (Harrison et., 1991, Ramaswamy, 1997, Zollo and Singh, 2004), post-acquisition economic performance can be measured using the variation in Return on Assets (ROA). The accounting based research method conclude often that M&A are unsuccessful (Goldberg 1983, Hogarty 1970, Lubatkin 1983). The vanity of an M&A is investigated by hypothesis 1.
H1: The post-acquisition performance of the combination is less than the sum of the performances of acquirer and target before the M&A event.
Organizational Integration
Many strategic combination potentials as foreseen by the M&A are not automatically realized after the M&A is formalized. These potentials depend on the management quality during the post-acquisition integration process. The degree of interaction and coordination between the combined firms is an
important success factor of an M&A (Buono and Bowditch 1989, Pablo 1994, Shrivasstava 1986, Yunker 1983). The integration of the organizations depends on two interdependent factors. The first factor is the degree of integration between the firms. Examples are the restructuring and exchange of services and materials. The second factor is the degree of coordination effort to improve this integration by e.g. special integrators, transition teams, preplanning etc. (Larsson and Finkelstein, 1999). These two factors have a positive effect on the synergy realization (Datta 1991, Hunt 1990, Schweiger et al 1987). Another factor is the relative size of the acquired firm. Relative small acquisitions are expected to result in less
combination potential than large acquisitions (Ravenshaft and Scherer 1987). Besides the strategic acquisition reason, it is also an important determinant of how intensive the integration of the new
combination will be. A cost driven acquisition requires a high level of integration of the organizations and in contrast, financial driven acquisitions and revenue driven acquisition, requiring a low level of
5 M&A is nearly impossible by means of accounting data, but the relative size assumption can be
hypothesized.
H2: Post-acquisition performance of the combination is negatively related to relative size difference between the acquirer and the target.
Synergy concept
A concept to measure the M&A performance is measuring the synergy realization between the two firms (Larsson and Finkelstein 1999). Synergy can be interpreted as the integration of the combination
potential, the organizational integration and the employee reaction/resistance factors and thus integrates the M&A fields of Strategy, Economics, Organization science and Human Resource Management. It excludes the dependence on accounting-based measures of economics and event studies of stock returns as used in finance. According to the synergy concept scholars, accounting-based measures and event studies are subject to significant error (Bradley and Jarrell 1988, Jensen 1988, Ravenshaft and Scherer 1987, Schleifer and Summers 1988) and conclude often that M&A are unsuccessful (Goldberg 1983, Hogarty 1970, Lubatkin 1983) while they obey the importance of the post-acquisition integration process (Haspeslagh and Jemison 1991, Pablo 1994) is which much of the value of the M&A is created. Despite the critics of the synergy concept scholars an attempt is made to hypothesize the combination potential by assuming that M&A in the same industry of industry sector do create more synergy.
H3: Post-acquisition performance of the combination increases if the acquired firm is in the same industry or industry sector due to larger combination potential.
Marketing integration
Academics did not pay much attention to the influence of the marketing integration on the
post-acquisition performance (Homburg and Bucerius 2005). An exception is the study of Capron and Hulland of 1999, investigating the redeployment of brands, sales forces and marketing management expertise after horizontal acquisitions, concluding a significant influence on the performance after the M&A. Often the managerial attention is completely absorbed by the post-acquisition integration process, causing the neglect of important customer relation issues (Hitt et al 1990). Customers, already critical to the
acquisition, are getting less attention. This confirms their uncertainties to the future relationship with the new combination. They often start looking for alternatives (Zollo 2008). The suggestion is that small acquired firms will absorb almost all the attention from the top managers during the post-acquisition integration process, resulting in higher loss of customers, than larger acquired firms do, which often do have separate sales and marketing managers. Because it is nearly impossible to conceptualize the market
6 integration in terms of the absolute size of the target. If a target is small, its effect on the economic post-acquisition performance is in general negligible. Market Integration is not conceptualized.
Human Resources
The individual and collective employee reactions on a M&A is a favorite research topic of HRM scholars (Hayes 1979, Larsson et al 1996, Marks 1982, Schweiger et al. 1987, Schweiger and Walsh 1990). Both, the individual and the group reactions on M&A are critically. This critical reaction is often suggested to be one of the factors of unsuccessful M&A’s (Blake and Mouton 1985, Hambrick and Cannella 1993, Walter 1985). Psychological perspective gives answers this “We versus them” syndrome like antagonism, arrogances, cynicism, pressure and hostility. Mark and Mivris (1986), describe the “merger syndrome”. These causes will result in employee turnover during the post-acquisition integration process. The
collective employee reaction can be negative as a result of organizational and national cultural differences (Vaara et al. 2012). It can be assumed that the negative collective employee reaction will result in a more negative economic performance which is measurable by means of accounting data. To test if international acquisitions are performing less that national acquisitions the following hypothesis formulized:
H4: Post-acquisition performance of the combination is negatively related to international acquisitions due to organizational and cultural differences between the acquirer and target.
As stated by the human resource view, the outcome of the cultural differences between the acquirer and the target causes employee turnover. This will result in a lower economic post-acquisition performance. The last hypothesis tests if there is a relation between employee turnover and post-acquisition
performance.
H5: Post-acquisition performance of the combination is negatively related to employee turnover due to organizational and cultural differences between the acquired firm and acquiring firm.
Acquisition Experience
Academicians have suggested that firms with previous acquisition experience will do better than those without such experience, however empirical research failed to find a significant relationship by examine the acquisition experience of firms listed on the Federal Trade Commission's Large Merger Series from 1948 to 1979 (Lubatkin, 1983). Conversely, a case analysis of twelve successful acquirers has found organization acquisition experience to be positively related to M&A acquisition performance when defining M&A performance as a positive change in return on assets and R&D intensity and this acquisition experience leads to a greater realized synergy as result of a more effective acquisition
7 integration process (Hitt et al. 1993). Other scholars investigated if the size specific M&A experience influences the post-acquisition performance (Ellis et al. 2011) and found a negative relation between acquirers having small target size M&A experience and the post-acquisition performance of a large acquisition. To research this topic, the sample data must contain sufficient acquirer companies which executed multiple acquisitions with long periods in between (more than 3 year) otherwise an economical interaction exists between them and influences the consolidated accounting data of the acquiring firm and misleads the research results. For this reason the previous acquisition experience assumption is not hypothesized.
8
Research Methods
The hypothesis are tested using secondary data. To test the assumptions, a dataset is created which contains merger and acquisition events with the necessary accounting data of the acquirer firm and target firm. This condition is achieved by merging two databases, the Thomson One database and the Compustat database. Thomson One database provides de merger and acquisition events and the Compustat database of Wharton Research Data Services (WRDS) retains the financial accounting data i.e. net income and current assets, of the acquirer and the target.
Data Collection
In Thomson One, all worldwide merger and acquisitions in the manufacture and service industries are collected from the years 1981 until 2015. This dataset contains 547559 M&A event records, from 191 different countries. The United States dominates the M&A event record set with 193427 registered M&A events, 35% of the total amount of M&A events. The dataset contains acquirers from 83 and targets from 84 different industry sectors, 991 different acquirer industries and 1011 different target industries. Information of interest is the acquisition date, how many percent of the target company is ultimately owned by the acquirer and if the M&A was successfully completed.
Table 1 Thomson One Top 10 M&A Nations Thomson One Top 10 M&A Nations.
Nation Count % United States 193427 35% United Kingdom 46656 9% Japan 37155 7% Germany 25022 5% China 23371 4% France 22650 4% Canada 21993 4% Australia 14977 3% Sweden 10943 2% Malaysia 10445 2% Other nations 140820 26% Total 547459 100%
9 Table 2 Thomson One Top 10 Industry sectors
Thomson One dataset Top 10 Industry Sectors
Industry Sector Description Industry sector SIC Count %
Prepackaged Software 73XX 89697 16%
Chemicals and Allied Products 28XX 41449 8%
Business Services 87XX 37009 7%
Electronic and Electrical Equipment 36XX 34535 6%
Machinery 35XX 32616 6%
Telecommunications 48XX 29487 5%
Food and Kindred Products 20XX 27853 5%
Electric, Gas, and Water Distribution 49XX 23863 4%
Measuring, Medical, Photo Equipment; Clocks 38XX 18730 3%
Printing, Publishing, and Allied Services 27XX 18682 3%
Other 193538 35%
Total 547459 100%
Table 3 Thomson One Top 10 Industries
Thomson One dataset Top 10 Industries
Industry description Industry SIC Count %
Prepackaged Software 7372 17743 3%
Information retrieval services 7375 15213 3%
Pharmaceutical preparations 2834 11794 2%
Electric services 4911 11309 2%
Telephone communications, except radiotelephone 4813 10206 2%
Engineering services 8711 9509 2%
Business services, nec 7389 8595 2%
Management consulting services 8742 7530 1%
Computer integrated systems design 7373 7336 1%
Hotels and motels 7011 6850 1%
Other 441374 81%
Total 547459 100%
To link the Thomson database and the WRDS Compustat database, two different methods are used. For the M&A events in North America the CUSIP code is used and for all other countries the SEDOL code. All North American financial securities are identified by a CUSIP nine-character alphanumeric code. The CUSIP acronym stands for Committee on Uniform Security Identification, which is based on a committee of American bankers. It was erected in 1964 to uniformly identify companies.
SEDOL is a security identifier used in United Kingdom and Ireland and is assigned by the London Stock exchange. SEDOL is an International Securities Identifying number (ISIN) for all securities issued in the United Kingdom. The SEDOL database contains unique references to millions of global securities.
10 Figure 1 Relation between Thomson One and Compustat North America and Compustat global
Two different Compstat databases are used, the North American Compustat database for collecting the annual financial accounting data of the North American acquirer and target companies and the Global Compustat database for the annual financial accounting data of all companies which are stock market listed on London Stock Exchange.
To select the companies of interest in the Compustat North American database, all Standard Industrial Classification Codes (SIC) for each acquirer and the target company are retrieved from the Thomson One M&A database. The CUSIP code can’t be used directly because the CUSIP code as given in Compustat North America database is the ultimate known Company CUSIP identifier. A company its CUSIP identifier may change for a security if its name or capital structure changes. In case of M&A events this often happens. The Thomson One M&A database uses the CUSIP identifier as listed at the moment of the M&A event which often can’t be found back in Compustat North America. Using the SIC codes of the
Thomson One Database M&A events from Q1 1981 until Q2 2015 of the Manufacturing and Service Industries.
Information:
General
- Acquisition Date - Description of M&A - Percent owned after M&A
event
- Status of M&A event Acquirer
- CUSIP
- SEDOL
- Company Name
- Brief Company Description - Nation of Company - Standard Industry Code
(SIC) Target
- CUSIP
- SEDOL
- Company Name
- Brief Company Description - Nation of Company
- Standard Industry Code (SIC)
Compustat North America Annual accounting data from Q1 1981 until Q2 2015 Information: - Data Date - Financial Year - CUSIP - NCUSIP* - Assets Total - Net Income - Market Value*** - # Employees Compustat Global
Annual accounting data from Q1 1981 until Q2 2015 Information: - Data Date - Financial Year - SEDOL - Assets Total - Net Income - # Employees - ISO currency - Exchange Rate**
* NCUSIP is the historical CUSIP code as the company was identified in the financial year. It can be restored by using an extra translation table containing the CUSIP–NCUSIP-period information.
** Exchange Rate is determined by using an external table containing all historical currencies and exchange rates. The 12 months moving average at the Data Date moment is used.
*** Market Value only available in Compustat North America
11 acquirer and target circumventers this problem. The start date for the Compustat North America
accounting data is the beginning of 1981, the first M&A event date in the Thomson dataset. It has resulted in a dataset of 148323 records classified in an industry by the specified SIC codes.
For selecting the right acquirer and target annual accounting data in the Compustat Global database, all acquirer and target SEDOL identifiers in Thomson One are collected. The resulting list of SEDOL
identifiers are used to query the Compustat Global database. The start date is the query is the beginning of 1987, the year of the foundation of the Compustat Global database. Note there is a 6 years gap between the Thomson One database, which begins in 1981. The query has resulted in a dataset of 297002 records, which contains the data of companies which are listed on the London Stock exchange.
To restore the original historical CUSIP identifiers in the Compustat North America dataset an extra lookup database1 is was used, which contains the last known CUSIP code and the historical NCUSIP
code including the begin and the end effective date. Figure 2 restoring the historical CUSIP
All accounting data in the Compustat Global dataset is provided in the currency of the country of the location of the acquirer and target headquarter. To normalize these currencies to US dollars, a lookup table2 was used which contains all the global currency exchange rate per monthly bases including a
twelve month moving average exchange rate. To match the right average currency exchange rate, the
1 This lookup database was generated using SAS. It is not made available via the web query environment of WRDS. SAS code and method of
retrieving the data can be inquired. Alternatively, the Jie Jay Cao lookup database https://sites.google.com/site/jiejaycao/home/tools can be used, but the database is often outdated.
2 This lookup database was generated using SAS. It is not made available via the web query environment of WRDS. SAS code and method of
retrieving the data can be inquired.
Compustat North America Annual accounting data from Q1 1981 until Q2 2015 Information: - Data Date* - Financial Year - CUSIP - NCUSIP** - Assets Total - Net Income - Market Value - # Employees
NCUSIP Lookup table Historical CUSIP data Information:
- CUSIP - NCUSIP
- Historical Company Name
- Start date effective* - End date effective*
* If data date is between start date and end date effective and CUSIP’s are equal than ** NCUSIP is stored in matched Compustat North America record.
12 month and year of the currency exchange lookup table are compared to the data date and the ISO currency of the global Compustat dataset records and the accounting data is normalized to US dollars using the found exchange rate.
Figure 3 normalizing the currencies to USD
Subsequently the Compustat North America and the Compustat Global dataset are merged to one
combined dataset containing 547459 records of annual accounting data. By using a cross table, the data is reduced to one record for each Thomson One dataset M&A event. If the annual accounting data in the Compustat merged dataset is available for the acquirer or the target, it is placed in the corresponding year before, the year of or the year after the M&A event.
Compustat Global
Annual accounting data from Q1 1981 until Q2 2015 Information: - Data Date* - Financial Year - SEDOL - Assets Total - Net Income - # Employees - ISO currency - Exchange Rate**
Exchange Rate look-up table Historical global exchange rates Information:
- Data Date* - ISO currency - Twelve months moving
average Exchange rate to GBP**
* If Year and Month of Data Date exchange look-up table is equal toYear and Month of Data Date Compustat global than ** store exchange rate to Great Britain pound (GBP) and look up exchange rate from GBP to US dollar for appropriate month and year and determine exchange rate by rate GBP to USD/ISO currency to GBP.
13 Figure 4 Linking Thomson One M&A data to Compustat accounting data
Thomson One Database M&A events from Q1 1981 until Q2 2015 of the Manufacturing and Service Industries.
Information:
General
- Acquisition Date* - Description of M&A - Percent owned after M&A
event
- Status of M&A event Acquirer
- CUSIP**
- SEDOL**
- Company Name
- Brief Company Description - Nation of Company - Standard Industry Code
(SIC) Target - CUSIP**
- SEDOL**
- Company Name
- Brief Company Description - Nation of Company - Standard Industry Code
(SIC)
Compustat Merged
Annual accounting data from Q1 1981 until Q2 2015 Information: - Data Date - Financial Year* - CUSIP - NCUSIP - SEDOL - Assets Total-Normalized - Net Income-Normalized - Market Value - # Employees - ISO currency - Exchange rate
* All Compustat records are selected where CUSIP is NCUSIP or SEDOL is SEDOL and financial year is between 3 years before and 3 years after the year of the acquisition date.
** This process is simultaneously performed for the acquirer and the target, resulting in cross table record for each M&A event, containing 7 successive years of data, three years before, the year of and three years after the M&A event, of the asset total normalized, net-income normalized, market-value and # employees of the acquirer as well as of the target.
14 Figure 5 Transpose acquirer and target data to one case per M&A event
Data Selection and conditioning
To ensure that the acquirer does have significant influence on the post-acquisition performance, the status of the M&A event must be completed and the percentage of ownership of the target is 80% or more. These prerequisites reduces the merged M&A and accounting dataset to 0.07 % of the original quantity of M&A events.
Table 4 Reduction of the dataset after applying the data selection constrains
Data Selection
Dataset Number of Cases %
Unrelated M&A record in Thomson One 547459 100%
Related M&A records in Thomson One with Compustat North America and Global 39090 7,2% Percentage of ownership of the target is 80% or more and status of M&A is completed 26523 4,8% Assets total (AT) and net income (NI) from acquirer and target present for 3 book years
before M&A event date and assets total (AT) and net income (NI) from acquirer present for 3 book years after M&A event. Data is essential for the average delta ROA calculations
576 0,1%
Exclusion of acquirer companies who performed other mergers and acquisitions in three book years before and after the M&A event date
399 0,07%
Exclusion of delta ROA outliers 390 0,07%
SEDOL/ NCUSIP Fiscal Year Diff Year Assets Net Income ….. 46612K 2007 -3 1042.38 42.43 46612K 2008 -2 3524.60 60.71 46612K 2009 -1 3882.60 106.00 46612K 2010 0 5868.10 28.10 46612K 2011 1 57270 -58.90 46612K 2012 2 6023.60 128.70 46612K 2013 3 7093.00 106.70 45071R 1998 -3 4287.05 57.40 SEDOL/ NCUSIP Year of date M&A Assert Year -3 Assert Year -2 Assert Year -1 Assert Year 0 Assert Year 1 Assert Year 2 Assert Year 3 Net Income -3 Net Income -2 .. 46612K 2010 1042.38 3524.60 3882.60 5868.10 5727.00 6023.60 7093.00 42.43 60.71
Transpose/merge all relevant acquirer and target accounting data from the Compustat to one case per M&A event as present in the Thomson One dataset
15
Model
Figure 6 regression model
Dependent Variable. To calculate the average ROA of the acquirer and the target of three years before
and of the combination of three years after the M&A event, all net income and asset total annual data must be present. The 80% ownership, the status complete and all relevant data available reduces the dataset to 576 M&A events. To avoid a spillover between M&A events of the same acquirer which perform multi acquisitions, a minimum M&A event gap of 3 years is applied between two successive M&A events. This reduces the dataset to 399 cases. A Q-Q plot is used to remove outliers and 9 cases are deleted from the dataset, resulting in a research dataset of 390 cases.
The average ROA of the three successive years before the M&A event is calculated by using the formula:
ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−1,−3) =13∑ �𝑛𝑛𝑎𝑎𝑡𝑡 𝑎𝑎𝑛𝑛𝑎𝑎𝑖𝑖𝑖𝑖𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑛𝑛𝑎𝑎𝑡𝑡 𝑎𝑎𝑛𝑛𝑎𝑎𝑖𝑖𝑖𝑖𝑎𝑎𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡
𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡 𝑡𝑡𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡 𝑡𝑡𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡�
𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−𝑎𝑎) 3
𝑎𝑎=1
And if the percentage acquired is 100% the average ROA of the three successive years after M&A event is: ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3)=13 � �𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛 𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛 𝑛𝑛𝑖𝑖𝑛𝑛𝑎𝑎𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎� 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(𝑎𝑎) 3 𝑎𝑎=1
or if percentage acquired is less than 100%,the applied formula is:
ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) =13 � �𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛 𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛 𝑛𝑛𝑖𝑖𝑛𝑛𝑎𝑎𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+ 𝑛𝑛𝑛𝑛𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑖𝑖𝑖𝑖𝑛𝑛𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+ 𝑎𝑎𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛 𝑛𝑛𝑖𝑖𝑛𝑛𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡� 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(𝑎𝑎) 3 𝑎𝑎=1 Independent Variables 1. Relative Size
2. Acquirer to target relatedness
3. National Cultural Differences
4.Relative Employee Turnover
Dependent Variable
∆𝐑𝐑𝐑𝐑𝐑𝐑𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄𝒄 The difference in economic acquisition performance before and after the M&A event
16
Independent Variables
Relative Size. Relative size can be calculated in different ways. First method is taking the factor between
the asset total of the acquirer and the target. Second method is taking the factor between the revenue of the acquirer and the target. Both methods are often used as measure for the relative size (Ellis et al., 2011). It is questionable if this is still applicable between M&A events where the acquirer and the target are in different industries or in different industry sectors. In this research, the assumption is made that they are comparable, even between M&A in different industry sectors. The third method is determining the factor between the number of employees of the acquirer and the target, this method is erroneous if it used for acquirers and targets in different industry sectors, because one industry can be more labor intensive than the other. And the final method is the factor between the market value of the acquirer and the target. Using the market value is only applicable to the North American M&A events while the global Compustat doesn’t provide this data. The methods one and two are applied in this research.
The formula for calculating the relative size as factor between the asset total value of the book year before the M&A event of the acquirer and target is:
𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 =𝐴𝐴𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑎𝑎 𝑇𝑇𝑖𝑖𝑛𝑛𝑎𝑎𝑡𝑡 𝑎𝑎𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑎𝑎𝑛𝑛𝑎𝑎 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎−1 𝐴𝐴𝑎𝑎𝑎𝑎𝑛𝑛𝑛𝑛𝑎𝑎 𝑇𝑇𝑖𝑖𝑛𝑛𝑎𝑎𝑡𝑡 𝑛𝑛𝑎𝑎𝑎𝑎𝑡𝑡𝑛𝑛𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎−1
Or the formula to calculate the factor between the turnover/revenue of the book year before the M&A event of the acquirer and the target is:
𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎 = 𝑅𝑅𝑛𝑛𝑅𝑅𝑛𝑛𝑛𝑛𝑎𝑎𝑛𝑛 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎−1 𝑅𝑅𝑛𝑛𝑅𝑅𝑛𝑛𝑛𝑛𝑎𝑎𝑛𝑛 𝑛𝑛𝑎𝑎𝑎𝑎𝑡𝑡𝑛𝑛𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎−1
The relative size of asset total and revenue are both coded to a categorical variable with 5 relative sizes.
Category 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎 1- Smaller 0 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 ≤ 0.8 0 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎 ≤ 0.8 2- Same 0.8 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 ≤ 2 0.8 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎≤ 2 3- Larger 2 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 ≤ 5 2 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎 ≤ 5 4- Much larger 5 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 ≤ 10 5 < 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎≤ 10 5- Extreme larger 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝐴𝐴𝑎𝑎𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎 𝑇𝑇𝑖𝑖𝑡𝑡𝑎𝑎𝑡𝑡 > 10 𝑅𝑅𝑛𝑛𝑡𝑡 𝑎𝑎𝑖𝑖𝑠𝑠𝑛𝑛𝑅𝑅𝑎𝑎𝑅𝑅𝑎𝑎𝑛𝑛𝑎𝑎𝑎𝑎> 10
17
Acquirer-to-target relatedness. The acquirer to target relatedness is determined by using the standard
industry code of the acquirer and the target. If all four digits of the industry code of the acquirer and the target are the same, they are categorized as in the same industry. If the first two digits of the acquirer and the target are the same, they are categorized as in the same industry sector.
𝐼𝐼𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛𝑎𝑎𝑦𝑦 = (𝐴𝐴𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑎𝑎𝑛𝑛𝑎𝑎 𝑆𝑆𝐼𝐼𝑆𝑆 = 𝑇𝑇𝑎𝑎𝑎𝑎𝑡𝑡𝑛𝑛𝑛𝑛 𝑆𝑆𝐼𝐼𝑆𝑆)
𝐼𝐼𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛𝑎𝑎𝑦𝑦 𝑎𝑎𝑛𝑛𝑖𝑖𝑛𝑛𝑖𝑖𝑎𝑎 = (𝐹𝐹𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛 2 𝑖𝑖𝑖𝑖𝑡𝑡𝑖𝑖𝑛𝑛𝑎𝑎 𝑖𝑖𝑜𝑜 𝐴𝐴𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑎𝑎𝑛𝑛𝑎𝑎 𝑆𝑆𝐼𝐼𝑆𝑆 = 𝐹𝐹𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛 2 𝑖𝑖𝑖𝑖𝑡𝑡𝑖𝑖𝑛𝑛𝑎𝑎 𝑖𝑖𝑜𝑜 𝑇𝑇𝑎𝑎𝑎𝑎𝑡𝑡𝑛𝑛𝑛𝑛 𝑆𝑆𝐼𝐼𝑆𝑆) 𝑖𝑖𝑛𝑛𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛𝑎𝑎𝑦𝑦 𝐿𝐿𝑛𝑛𝑅𝑅𝑛𝑛𝑡𝑡 = 𝑖𝑖𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛𝑎𝑎𝑦𝑦 + 𝐼𝐼𝑛𝑛 𝑖𝑖𝑛𝑛𝑖𝑖𝑎𝑎𝑎𝑎𝑛𝑛𝑎𝑎𝑦𝑦 𝑡𝑡𝑛𝑛𝑅𝑅𝑛𝑛𝑡𝑡
The result is categorical variable industry level, having the value 0, if acquirer and the target industries are outside the same industry sector. Is 1, if acquirer and target industries are inside the same industry sector and is 2, if acquirer and the target industries are inside the same industry.
National Cultural Differences To investigate if national cultural differences between the acquirer and
target are influencing the post-acquisition performance, a national internal explanatory variable is constructed.
𝑁𝑁𝑎𝑎𝑛𝑛𝑖𝑖𝑖𝑖𝑛𝑛𝑎𝑎𝑡𝑡 = (𝐴𝐴𝑖𝑖𝑎𝑎𝑎𝑎𝑖𝑖𝑎𝑎𝑛𝑛𝑎𝑎 𝑁𝑁𝑎𝑎𝑛𝑛𝑖𝑖𝑖𝑖𝑛𝑛 = 𝑇𝑇𝑎𝑎𝑎𝑎𝑡𝑡𝑛𝑛𝑛𝑛 𝑁𝑁𝑎𝑎𝑛𝑛𝑖𝑖𝑖𝑖𝑛𝑛)
The result is dichotomous variable nation with the value 0 in case of an international acquisition and the value 1 in the case of a national acquisition.
Relative Employee Turnover The relative number increase or decrease of employees is calculated by
taking the number of employees of the acquirer and target one year before and of the combination one year after the M&A event. It is unsure if this construct does apply to the M&A cases which are executed with efficiency in mind. Often the redundant assets, including employees are divested (Barney 1986).
𝑅𝑅𝑛𝑛𝑡𝑡𝑎𝑎𝑛𝑛𝑖𝑖𝑅𝑅𝑛𝑛 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑛𝑛𝑎𝑎𝑛𝑛𝑛𝑛𝑖𝑖𝑛𝑛 # 𝑛𝑛𝑖𝑖𝑒𝑒𝑡𝑡𝑖𝑖𝑦𝑦𝑛𝑛𝑛𝑛𝑎𝑎 = 1 − # 𝐸𝐸𝑖𝑖𝑒𝑒𝑡𝑡𝑖𝑖𝑦𝑦𝑛𝑛𝑛𝑛𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎−1 # 𝐸𝐸𝑖𝑖𝑒𝑒𝑡𝑡𝑖𝑖𝑦𝑦𝑛𝑛𝑛𝑛𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎+1
18
Results
Table 5 Descriptive Statistics and Correlations
Descriptive Statistics and Correlations
Mean Std. Deviation 1 2 3 4 5 6 7 8 9 1 ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) 0.03 0.11 2 ROA 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) 0.03 0.13 .886** 3 ROA 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) 0.01 0.14 .684** .675**
4 relative size assets total 40.34 166.55 .080 .057 .059 5 relative size revenue 73.02 747.45 .007 .003 .006 .794**
6 relative size market value 120.89 752.17 .109 .079 .090 .398** .173*
7 Relative difference
employees 0.07 0.38 .096 .099 .002 -.019 -.016 .005
8 Acquirer and target in
same industry - - .030 .002 .043 -.011 .055 -.040 -.066
9 Acquirer and target in
same industry sector - - -.007 -.022 .058 -.047 .026 -.079 -.026 .620
**
10 National acquisition - - -.043 -.053 .037 -.039 .016 .032 .061 .039 .054
**. Correlation is significant at the 0.01 level (2-tailed).
To test all hypothesizes, sample T-tests, ANOVA tests and regression tests were executed. Table 5 report the descriptive data and the correlation matrix for the constructs used in this research. First step is a paired sample T-test between the averages of the ROA before and after the M&A event is performed. The aim is to investigate if there is a significant difference in the mean. The test result is a negative mean of -.015725 with a single sided significance of 0.003/2 = 0.0015, therefore H0 is rejected and H1 is supported.
Conclusion, Hypothesis 1 is supported (Figure 7), a significant difference exist between the ROA before and after the merger of acquisition (delta ROA). Subsequently, the different independent variables are constructed to investigate if these explanatory variables explain this significant mean difference of the delta ROA. The first independent variable is the relative size categorical variable. A one way ANOVA is used to see if there are significant differences in the mean between the relative size categories. Both ANOVA tests pointed out that the difference in mean of the delta ROA are not significant. Therefore hypothesis 2 is not supported (Figure 8). Remarkable is the nearly significance (p=.071>.05) of the delta ROA of the asset total relative size category because the same size category between the acquirer and the target (relative size difference between 0.8 and 2) has the most negative mean of delta ROA. Hypothetical reason can be that the ROA of the combination is depending strongly on the economic performance of the former target which has dropped strongly after the takeover. But this effect should be even stronger by acquirers which are smaller as the target. However this is not the case. The third independent variable is if the acquirer and
19 target are outside the same industry sector, inside the same industry sector or inside the same industry. The outcome of the one way ANOVA is that there is no significant difference (p=.190>.05) between the mean of the delta ROA of the categories and hence hypothesis 3 is not supported (Figure 9). Noteworthy is the mean of the delta ROA of the in industry category is more negative as the delta ROA of the in industry sector category. The expectation was that the delta ROA of the in industry category should be equal or less negative than the delta ROA of the in industry sector category. A reason can be an interaction with an unknown moderator. The fourth independent variable is, if there is a significant difference between the mean in the delta ROA of national or international acquisitions. Background is the expectation is that international acquisitions have more cultural and organizational differences between the acquirer and target. The finding of the T-test indicates a significant difference in the mean delta ROA between national and international acquisitions, with a significant more negative mean delta ROA for international acquisitions. The difference is a negative mean of -.032010 with a single sided significance of p= (.06/2)=.03<.05. Consequently H0 is rejected and H1 is supported and the conclusion of this T-test is that hypothesis 4 is
supported (Figure 10). The fifth and last independent variable is if there is a relation between the relative change of number of employees and the delta ROA. A regression test is used to test this relation, resulting in a no-significant model and a R2 of zero (p ANOVA=.755>.05), Therefore hypothesis 5 is rejected (Figure
11). To test the post-acquisition performance model, the regression model test was executed. The goal of this regression is to model a linear relation between the independent variables relative size, acquirer to target relatedness, international differences, relative employee turnover of the acquired company and the dependent variable post-acquisition performance. In total three models are tested to investigate the importance of the explanatory variables. Model 1 tests only the independent variable national or international M&A events. Model 2 retests the model 1 independent variable but the outside, inside industry sector and inside industry independent variable are added and model 3, all previous independent variables plus the relative size variables. The dependent variable is delta ROA. Figure 12 provides an overview over the regression analysis including the three used models. As expected from the former independent T-test and ANOVA tests, the first two models do have the most explanatory power with a slightly better model explanatory power for model 2 (ANOVA , p=.05 is equal to .05). Nevertheless the R2 adjusted of the model
is only 1%, meaning that the explanatory variables national or international and industry relatedness only explain 1% of post-acquisition performance indicator delta ROA.
20
Conclusions
The results of this research confirms the criticism of supporters of the synergy concept. Accounting-based explanation of post-acquisition performance is hard to archive. In this research it is tried to relate these important explanatory factors to measurable accounting constructs, but it is ambiguous if they reflect the factors, as proposed in synergy concept, correctly. The relevant independent variables of the integration process performance and also the employee and customer reaction/resistance factors cannot be explained in terms of accounting data. The combining of the accounting data with surveys and case studies, the predominantly used research methods in the M&A fields of Strategy, Organization science and Human Resource Management, are necessary to make a better explanatory post-acquisition performance model. Disadvantages of the surveys and case studies is the sensibility for ambiguous questions and biases of the respondents. Interesting research questions as a result of this study are: Is the national cultural dissimilarity between acquirer and target as proposed in human resource discipline the actual reason for the difference in post-acquisition performance between national and international mergers and acquisitions? And why is the post-acquisition performance of an equal sized acquirer and target lower than an acquisition where the acquirer is smaller than the target? And for what reason is the mean of the delta ROA in industry is more negative than the delta ROA of the in industry sector?
21 Figure 7 Hypothesis 1 test results
H1: The post-acquisition performance of the combination is less than the sum of the performances of acquirer and target before the M&A event.
The following H0 is tested using paired sample T test with
𝐻𝐻0= ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) − ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) = 0 𝐻𝐻1= ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) − ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) < 0
Paired Samples Statistics
Mean N Std. Deviation Std. Error Mean
Pair 1 ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) .02510 390 .107458 .005441
ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) .00938 390 .142520 .007217
Paired Samples Correlations
N Correlation Sig.
Pair 1 ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) & ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) 390 .684 .000
Paired Samples Test
Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−3,−1) - ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3) -.015725 .104424 .005288 -.026121 -.005329 -2.974 389 .003
22 Figure 8 Hypothesis 2 test results
H2: Post-acquisition performance of the combination is negatively related to relative size difference between the acquirer and the target.
To test next hypothesizes we first have to determine the difference between the average ROA of the acquirer and target before and after the M&A event.
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛= ROA𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(1,3)𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛− ROA𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎+𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑡𝑡𝑦𝑦𝑎𝑎𝑎𝑎𝑎𝑎(−1,−3)
Next the H0 is tested by running a one way ANOVA using the relative size categories based on the asset total and revenue.
𝐻𝐻0= �∆ROA𝑎𝑎𝑖𝑖𝑎𝑎𝑡𝑡𝑡𝑡𝑎𝑎𝑎𝑎= ∆ROA𝑎𝑎𝑎𝑎𝑖𝑖𝑎𝑎= ∆ROA𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑎𝑎= ∆ROA𝑖𝑖𝑎𝑎𝑎𝑎ℎ 𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑎𝑎= ∆ROA 𝑎𝑎𝑒𝑒𝑡𝑡𝑎𝑎𝑎𝑎𝑖𝑖𝑎𝑎 𝑡𝑡𝑎𝑎𝑎𝑎𝑡𝑡𝑎𝑎𝑎𝑎� 𝐻𝐻1= 𝑂𝑂𝑛𝑛𝑛𝑛 𝑖𝑖𝑜𝑜 𝑛𝑛ℎ𝑛𝑛 ∆ROA 𝑖𝑖𝑎𝑎 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑛𝑛𝑎𝑎𝑛𝑛𝑛𝑛𝑛𝑛
Descriptives Relative Size Assets Total
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
Smaller 23 .00636 .115234 .024028 -.04347 .05619 -.266 .261 Same 73 -.03819 .129355 .015140 -.06837 -.00801 -.547 .251 Larger 97 -.00758 .104063 .010566 -.02855 .01340 -.378 .305 Much Larger 76 -.01037 .114199 .013099 -.03647 .01572 -.411 .246 Extreme Larger 121 -.01626 .074750 .006795 -.02972 -.00281 -.377 .164 Total 390 -.01573 .104424 .005288 -.02612 -.00533 -.547 .305
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 Test of Homogeneity of Variances Relative Size Assets Total
Levene Statistic df1 df2 Sig.
2.178 4 385 .071
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 ANOVA Relative Size Assets Total
Sum of Squares df Mean Square F Sig.
Between Groups .057 4 .014 1.304 .268
Within Groups 4.185 385 .011
23
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 Descriptives Relative Size Revenue
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
Smaller 25 -.00643 .116174 .023235 -.05439 .04152 -.362 .261 Same 73 -.01915 .111621 .013064 -.04519 .00689 -.547 .251 Larger 98 -.01212 .124882 .012615 -.03715 .01292 -.537 .257 Much Larger 71 -.02303 .098190 .011653 -.04627 .00021 -.411 .305 Extreme Larger 120 -.01269 .074820 .006830 -.02622 .00083 -.307 .216 Total 387 -.01526 .102859 .005229 -.02554 -.00498 -.547 .305
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 Test of Homogeneity of Variances Relative Size Revenue
Levene Statistic df1 df2 Sig.
1.619 4 382 .169
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 ANOVA Relative Size Revenue
Sum of Squares df Mean Square F Sig.
Between Groups .009 4 .002 .213 .931
Within Groups 4.075 382 .011
Total 4.084 386
24 Figure 9 Hypothesis 3 test results
H3: Post-acquisition performance of the combination increases if the acquired firm is in the same industry or industry sector due to larger combination potential.
The following H0 is tested using a one way ANOVA with
𝐻𝐻0= ∆ROA outside = ∆ROA in sector = ∆ROA in industry 𝐻𝐻1= 𝐴𝐴𝑛𝑛 𝑡𝑡𝑛𝑛𝑎𝑎𝑎𝑎𝑛𝑛 𝑖𝑖𝑛𝑛𝑛𝑛 𝑖𝑖𝑜𝑜∆ROA 𝑖𝑖𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑖𝑖𝑎𝑎𝑖𝑖𝑛𝑛𝑎𝑎 𝑖𝑖𝑎𝑎 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑛𝑛𝑎𝑎𝑛𝑛𝑛𝑛𝑛𝑛 ∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 Descriptives industry level
N Mean Std. Deviation Std. Error
95% Confidence Interval for Mean
Minimum Maximum Lower Bound Upper Bound
OUTSIDE 138 -.02786 .110325 .009392 -.04643 -.00929 -.537 .305
IN_SECTOR 91 -.00328 .093619 .009814 -.02278 .01622 -.377 .257
IN_INDUSTRY 161 -.01236 .104560 .008241 -.02863 .00392 -.547 .261
Total 390 -.01573 .104424 .005288 -.02612 -.00533 -.547 .305
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig.
.765 2 387 .466
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 ANOVA
Sum of Squares df Mean Square F Sig.
Between Groups .036 2 .018 1.668 .190
Within Groups 4.206 387 .011
Total 4.242 389
25 Figure 10 Hypothesis 4 test results
H4: Post-acquisition performance of the combination is negatively related to international acquisitions due to organizational and cultural differences between the acquirer and target.
The following H0 is tested using an independent sample T test with
𝐻𝐻0= ∆ROA international − ∆ROA national = 0 𝐻𝐻0= ∆ROA international − ∆ROA national < 0
Group Statistics national/international M&A
National Acquisition N Mean Std. Deviation Std. Error Mean
∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 International 42 -.04429 .105103 .016218
National 348 -.01228 .103963 .005573
Independent Samples Test national/international M&A
Levene's Test for Equality of
Variances t-test for Equality of Means
F Sig. t df Sig. (2-tailed) Mean Difference Std. Error Difference 95% Confidence Interval of the Difference Lower Upper ∆ROA𝑎𝑎𝑖𝑖𝑖𝑖𝑐𝑐𝑎𝑎𝑛𝑛𝑎𝑎𝑡𝑡𝑎𝑎𝑖𝑖𝑛𝑛 Equal variances
assumed
.948 .331 -1.883 388 .060 -.032010 .017002 -.065438 .001417
Equal variances not assumed
26 Figure 11 Hypothesis 5 test results
H5: Post-acquisition performance of the combination is negatively related to employee turnover due to organizational and cultural differences between the acquired firm and acquiring firm.
A Q-Q plot is used to identify the outliers. In total 8 outliers were deleted with exceptional changes in the relative difference of employees. Next a regression test in performed with the relative difference in employees as independent variable and the delta ROA as dependent variable. The Hypothesis is
𝐻𝐻0= 𝛽𝛽 ∗ 𝑅𝑅𝑛𝑛𝑡𝑡𝑎𝑎𝑛𝑛𝑖𝑖𝑅𝑅𝑛𝑛 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑛𝑛𝑎𝑎𝑛𝑛𝑛𝑛𝑖𝑖𝑛𝑛 # 𝑛𝑛𝑖𝑖𝑒𝑒𝑡𝑡𝑖𝑖𝑦𝑦𝑛𝑛𝑛𝑛𝑎𝑎 = 0 𝐻𝐻1= 𝛽𝛽 ∗ 𝑅𝑅𝑛𝑛𝑡𝑡𝑎𝑎𝑛𝑛𝑖𝑖𝑅𝑅𝑛𝑛 𝑖𝑖𝑖𝑖𝑜𝑜𝑜𝑜𝑛𝑛𝑎𝑎𝑛𝑛𝑛𝑛𝑖𝑖𝑛𝑛 # 𝑛𝑛𝑖𝑖𝑒𝑒𝑡𝑡𝑖𝑖𝑦𝑦𝑛𝑛𝑛𝑛𝑎𝑎 ≠ 0
The R2 of the model stays 0, implicating that the independent variable relative difference in employees does not have any influence on the
dependent variable delta ROA. The significance of the ANOVA is (p=.755>.05) more than .05, which means that the model is not significant. So H0 is not rejected, relative difference in employees has no significant influence on the delta ROA.
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 REL_DIFF_EMPb . Enter
a. Dependent Variable: DIFF_AVR_A_T_ROA_BAA b. All requested variables entered.
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .016a .000 -.003 .106708
a. Predictors: (Constant), REL_DIFF_EMP
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .001 1 .001 .097 .755b
Residual 4.099 360 .011
Total 4.100 361
a. Dependent Variable: DIFF_AVR_A_T_ROA_BAA b. Predictors: (Constant), REL_DIFF_EMP
Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -.016 .006 -2.809 .005 REL_DIFF_EMP -.008 .025 -.016 -.312 .755
27 Figure 12 Regression Model test results
Regression Model testing
Variables Entered/Removeda
Model Variables Entered Variables Removed Method
1 NATIONAL_ACQb . Enter
2 INDUSTRY_LEVELb . Enter
3 REL_SIZE_REV_CAT,
REL_SIZE_AT_CATb . Enter
a. Dependent Variable: DIFF_AVR_A_T_ROA_BAA b. All requested variables entered.
Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate Change Statistics R Square
Change F Change df1 df2 Sig. F Change
1 .099a .010 .007 .102491 .010 3.780 1 385 .053
2 .125b .016 .010 .102323 .006 2.266 1 384 .133
3 .130c .017 .007 .102519 .001 .265 2 382 .767
a. Predictors: (Constant), NATIONAL_ACQ
b. Predictors: (Constant), NATIONAL_ACQ, INDUSTRY_LEVEL
c. Predictors: (Constant), NATIONAL_ACQ, INDUSTRY_LEVEL, REL_SIZE_REV_CAT, REL_SIZE_AT_CAT
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .040 1 .040 3.780 .053b Residual 4.044 385 .011 Total 4.084 386 2 Regression .063 2 .032 3.029 .050c Residual 4.020 384 .010 Total 4.084 386 3 Regression .069 4 .017 1.641 .163d Residual 4.015 382 .011 Total 4.084 386
a. Dependent Variable: DIFF_AVR_A_T_ROA_BAA b. Predictors: (Constant), NATIONAL_ACQ
c. Predictors: (Constant), NATIONAL_ACQ, INDUSTRY_LEVEL
28 Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics
B Std. Error Beta Tolerance VIF
1 (Constant) -.044 .016 -2.800 .005 NATIONAL_ACQ .033 .017 .099 1.944 .053 1.000 1.000 2 (Constant) -.053 .017 -3.145 .002 NATIONAL_ACQ .031 .017 .095 1.868 .063 .997 1.003 INDUSTRY_LEVEL .009 .006 .076 1.505 .133 .997 1.003 3 (Constant) -.062 .024 -2.567 .011 NATIONAL_ACQ .033 .017 .100 1.949 .052 .973 1.027 INDUSTRY_LEVEL .009 .006 .079 1.533 .126 .964 1.038 REL_SIZE_AT_CAT .005 .008 .064 .668 .505 .280 3.570 REL_SIZE_REV_CAT -.003 .008 -.039 -.406 .685 .280 3.566
a. Dependent Variable: DIFF_AVR_A_T_ROA_BAA
Excluded Variablesa
Model Beta In t Sig.
Partial Correlation Collinearity Statistics Tolerance VIF Minimum Tolerance 1 INDUSTRY_LEVEL .076b 1.505 .133 .077 .997 1.003 .997 REL_SIZE_AT_CAT .018b .359 .720 .018 .989 1.011 .989 REL_SIZE_REV_CAT .000b .009 .993 .000 .999 1.001 .999 2 REL_SIZE_AT_CAT .031c .605 .546 .031 .965 1.036 .965 REL_SIZE_REV_CAT .015c .290 .772 .015 .966 1.035 .964
a. Dependent Variable: DIFF_AVR_A_T_ROA_BAA b. Predictors in the Model: (Constant), NATIONAL_ACQ
29
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