What factors influence corporate acquisition decision-making success?
Daniel J. Power
Editor, DSSResources.COM
In 2017 in the United States, there were a record number of merger and acquisition transactions with 15,100, a 12.2% increase over 2016. In 2015, the record for total value of deals was set at $24,100 billion USD, cf., IMAA, 2019. Healthcare, high technology, finance, and consumer products and services industries have seen a large number of transactions. The size of deals in terms of transaction value has been especially high in healthcare. In the US, this current acquisition activity is part of the seventh M&A wave that started in 2014 (Cordeiro, 2014). Not all mergers and acquisitions are a success and limited research has been conducted about factors influencing success. Success of an acquisition can be challenging to measure for both internal stakeholders and for external observers. Both process and decision maker variables likely influence success. For example, use of analytics and decision support may influence success. Uncontrollable external factors like the economy also influence success.
Laveren and Verstreken (2017) identified 15 success factors in a research study including: 1) choosing a suitable partner, 2) trust between the parties, 3) due diligence and good valuation, 4) experience from previous mergers and acquisitions, 5) communication before the execution of the merger or acquisition, 6) quality of the plan, 7) execution of the plan, 8) swiftness of integration, 9) communication during the implementation, 10) strategic fit, 11) organizational fit, 12) cultural fit, 13) calculation and realization of synergies, 14) legislation enabling the merger or acquisition, and 15) state of the economy. The last 2 macroenvironmental factors can and should be considered in corporate acquisition decision-making, but they can not be altered directly by managers.
My Ph.D. dissertation field research (Power, 1982) investigated corporate acquisition decisions made early in the fourth M&A wave by managers in U.S. manufacturing and conglomerate firms. The study investigated hypotheses for two descriptive models: a prediction model and a decision-process model. Through mail questionnaires, data were gathered about 28 acquisitions completed between October 1, 1979 and March 31, 1980. Managers were asked for retrospective information (Huber and Power, 1985) about decision activities and for a current assessment of the acquired company's performance. Twenty-six companies made the acquisitions: nine very large companies with 1979 sales of more than $450 million and seventeen
smaller companies. Of the 28 acquired companies, 14 had 1979 sales
of $1 to $10 million, 13 had sales of $10 to $35 million, and one
had sales of more than $350 million.
The following paragraphs are a summary of the Power (1982) results:
Most investigations of prospects took many months. And many of
the decision processes spanned more than 9 months. The
acquisitions were generally perceived as more satisfactory than
unsatisfactory.
All 14 of the formal analytical activities provided in a list (see Power, 1982, p. 206, Table 6.2 Formal Analytical Activities) to
key informants were used by managers in at least 8 companies as part
of their acquisition decision process. These managers frequently used analytical
activities include: investigating managers of the prospect;
examining dilution of earnings per share and debt/equity ratios; and
determining payback period, cash flows and/or projected Return on
Investment. Many of the managers that were interviewed commented on
the importance of examining the financial implications of making a
proposed acquisition.
Some activities that one might expect for normative reasons to
be important to good decision making were not used very frequently.
For example, only one third of the companies compared purchasing the
prospect to other investment opportunities. Not as much formal analytical activity occurred during the
investigation of the acquisitions included in the study as had been
expected. Many activities that have been recommended by acquisition
specialists and authors in the normative literature on decision
making were not frequently included in corporate acquisition
decision processes. Analysis of prospects seemed to heavily stress
financial questions.
Many fewer information sources were used and they were used much less frequently by buyers
during the investigation of an acquisition prospect than was
expected. For example, computerized data bases, like
MERGEX and COMPUSTAT were only used as part of two decision processes. Information gathering often involved discussions with managers
in the selling firm. The available of online information about companies and hence acquisition prospects has greatly increased since 1980 and hence this finding may no longer be true.
The results of correlation and regression analyses indicated
that moderate levels of participation in acquisition sub-decisions
and direct contact with the prospect were related to successful
acquisitions, but higher levels of both activities were related to
lower levels of success. Also, increased participation in decision making did not
increase the perceived effectiveness of implementation activities.
The amount of formal analytical activity and CEO involvement
were not related to successful acquisitions. CEO involvement seems
to depend on the size of the acquiring company, with CEOs in smaller
companies more involved in making small and medium-sized
acquisitions.
Managers used more complex and extensive decision processes when
an unrelated business was investigated, but the process was apparently
often ineffective. The number of prior
acquisitions made by a firm was a good predictor of the success of an
acquisition. Firms that made more acquisitions used different
decision processes, including lower levels of participation in
sub-decisions and less use of information sources. Both experience
making acquisitions and acquiring a related business were good
predictors of a successful acquisition. Perceived experience was correlated with the number of companies acquired, i.e. r = .36 (p <= .05), but it is not
significantly correlated with the number of firms investigated.
In the study sample, a planned search for prospects was not related to more
successful acquisitions. Only initiation by an unusual source
altered the decision process, and then more CEO involvement and
intensive search occur.
The primary dependent variable in Power (1982) was perceived
acquisition effectiveness. This variable was measured using seven
effectiveness dimensions. Some financial data was also collected from external sources for a sub-sample of large publicly traded firms. Managers may have reported an acquisition was effective in the short-run to justify making additional acquisitions. Also, there are casual problems associated with interpreting these results. Was prior success encouraging additional acquisitions? Did managers bias perceptions of acquisition effectiveness to rationalize, justify, and validate a decision that they had made two years prior to data collection.
Critically assessing the strengths and weaknesses of both the buyer and the acquisition target should help determine the viability and future success of potential mergers and acquisitions. Improving a subjective, qualitative analysis with relevant data may be useful. From a different perspective, Chui and Ip (2017) propose a M&A evaluation and prioritization model (MAEPM) that aims to maximize the M&A success rate. The MAEPM incorporates risk analysis, fuzzy critical path analysis, cost–benefit evaluation, decision rules and prioritization. They tested the model using 11 case studies.
Mergers and acquisitions (M&A) transactions overall are estimated to only have a 50% chance of success (Picardo, 2018). For this reason alone, more research is needed about pre-acquisition decision process factors, including data-based decision support, that may influence M&A success. My research suggests experience making M&A decisions is a good predictor of M&A success. What did experienced managers learn during prior decision processes? How does the decision process change when managers have previously made M&A decisions? Can computerized decision support increase the success rate? Can "big data" analytics improve the effectiveness of corporate mergers and acquisitions?
References
Chui, A. B. S. and W. H. Ip, "Improving merger and acquisition decision-making using fuzzy logic and simulation," International Journal of Engineering Business Management, Volume 9: pp 1–18, June 19, 2017 at https://doi.org/10.1177/1847979017711521
Cordeiro, M., "The Seventh M&A Wave," Camaya Partners, September 2014 at URL https://camayapartners.com/the-seventh-ma-wave/
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Picardo, E., "How mergers and acquisitions can affect a company," Investopedia, March 7, 2018 at URL https://www.investopedia.com/articles/investing/102914/how-mergers-and-acquisitions-can-affect-company.asp
Power, D. J., Acquiring Small and Medium-sized Companies: A Study of
Corporate Decision Behavior," University of Wisconsin-Madison Doctoral Dissertation, 1982 at URL http://works.bepress.com/daniel-power/55/
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Salter, M. S. and W. A. Weinhold, Diversification Through Acquisition (New York: Free Press, 1979)
Last update: 2019-05-01 02:41
Author: Daniel Power
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