What Causes the M&A Performance of High-Tech Firms?
Abstract
:1. Introduction
2. Literature Review and Hypotheses
2.1. Review of Studies
2.2. Hypotheses
3. Materials and Methods
3.1. Methodology
3.1.1. Measuring AR
- : AR of individual stock on day .
- : Actual return of individual stock on day .
- : Market index returns on day .
3.1.2. Measuring Long-Term Operating Performance
- : Number of high-tech firms.
- : Number of non-high-tech firms.
- : Mean of .
- : Mean of .
3.1.3. Multiple Regression Analysis Model
- •
- : Dummy variable indicating whether an acquiring firm is a high-tech firm; the assigned value is 1 if the acquiring firm is a high-tech firm and 0 otherwise. High-tech acquiring firms are defined as firms whose main business is in biotechnology, telecommunications, computer equipment, electronics, and general technologies.
- •
- : Dummy variable indicating whether a target firm is a high-tech firm; the assigned valus is 1 if a target firm is a high-tech firm and 0 otherwise.
- •
- : Variable for acquiring firms’ organizational age; this variable indicates acquiring firms’ organizational age at the time of M&A disclosure, measured by subtracting the year when the acquiring firms were founded from the year of their M&A disclosure.
- •
- : Dummy variable indicating whether a target firm is listed; 1 if a target firm is listed, and 0 otherwise.
- •
- : Dummy variable indicating whether an M&A is executed between affiliated firms; 1 if an acquiring firm and a target firm are affiliated, and 0 if they are not. “Affiliation” means that the acquirers and target firms share the same ultimate parent ticker symbol (provided by SDC Platinum).
- •
- : Year-end debt ratio of acquiring firms for the year immediately preceding M&A disclosure = (borrowed capital/equity) × 100.
- •
- : Variable representing acquiring firms’ size; it is the natural log value of the mean market capitalization of acquiring firms’ common stocks for the year immediately preceding M&A disclosure.
- •
- : Dummy variable indicating the acquiring firms’ industrial classification based on the first digit of the Standard Industry Classification code.
- •
- : Dummy variable for the year of M&A disclosure.
3.2. Samples
3.2.1. Sample Composition
3.2.2. Descriptive Statistics of Variables
4. Findings
4.1. Results of the Validity Test on AR at the Time of M&A Disclosure
4.2. Results of Difference Analysis on CAR between High-Tech Group and Non-High-Tech Group
4.3. Results of Univariate Analysis on Post-M&A Long-Term Performance between High-Tech Group and Non-High-Tech Group
4.4. Results of Multiple Regression Analysis
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Korea Institute of S&T Evaluation and Planning [KISTEP]. Analysis of High-Tech Industries in Major Countries; KISTEP: Eumseong-gun, Korea, 2014. [Google Scholar]
- Harrison, J.S.; Hitt, M.A.; Hoskisson, R.E.; Ireland, R.D. Resource complementarity in business combinations: Extending the logic to organizational alliances. J. Manag. 2001, 27, 679–690. [Google Scholar] [CrossRef]
- Ahuja, G.; Katila, R. Technological acquisitions and the innovation performance of acquiring firms: A longitudinal study. Strateg. Manag. J. 2001, 22, 197–220. [Google Scholar] [CrossRef]
- Ragozzino, R. Firm valuation effects of high-tech M&A: A comparison of new ventures and established firms. J. High Technol. Manag. Res. 2006, 17, 85–96. [Google Scholar] [CrossRef]
- André, P.; Ben-Amar, W.; Saadi, S. Family firms and high technology Mergers & Acquisitions. J. Manag. Gov. 2014, 18, 129–158. [Google Scholar] [CrossRef]
- Jung, J.Y.; Wang, W.; Cho, S.W. The role of Confucius institutes and One Belt, One Road Initiatives on the values of cross-border M&A: Empirical evidence from China. Sustainability 2020, 12, 277. [Google Scholar] [CrossRef]
- Nelson, R.; Winter, S. An Evolutionary Theory of Economic Change; Harvard University Press: Cambridge, MA, USA, 1982. [Google Scholar]
- Fleming, L. Recombinant Uncertainty in Technological Search; Working Paper; Harvard Business School: Boston, MA, USA, 1999. [Google Scholar]
- Cassiman, B.; Colombo, M.G.; Garrone, P.; Veugelers, R. The impact of M&A on the R&D process. Res. Policy 2005, 34, 195–220. [Google Scholar] [CrossRef]
- Hitt, M.A.; Hoskisson, R.E.; Ireland, R.D.; Harrison, J.S. Effects of acquisitions on R&D inputs and outputs. Acad. Manag. J. 1991, 34, 693–706. [Google Scholar] [CrossRef]
- Hoskisson, R.A.; Hitt, M.A.; Ireland, D. The effects of acquisitions and restructuring (strategic refocusing) strategies on innovation. In The Management of Corporate Acquisitions: International Perspectives; Von Krogh, G.A., Sinatra, A., Singh, H., Eds.; MacMillan: New York, NY, USA, 1994; pp. 144–169. [Google Scholar]
- Ernst, H.; Vitt, J. The influence of corporate acquisitions on the behavior of key inventors. R&D Manag. 2000, 30, 105–120. [Google Scholar] [CrossRef]
- Singh, H.; Montgomery, C.A. Corporate acquisition strategies and economic performance. Strateg. Manag. J. 1987, 8, 377–386. [Google Scholar] [CrossRef]
- Kogut, B.; Zander, U. Knowledge of the firm, combinative capabilities, and the replication of technology. Organ. Sci. 1992, 3, 383–397. [Google Scholar] [CrossRef]
- Grant, R.M. Prospering in dynamically competitive environments: Organizational capability as knowledge integration. Organ. Sci. 1996, 7, 375–387. [Google Scholar] [CrossRef] [Green Version]
- Hagedoorn, J.; Duysters, G. External sources of innovative capabilities: The preferences for strategic alliances or mergers and acquisitions. J. Manag. Stud. 2002, 39, 167–188. [Google Scholar] [CrossRef]
- Hagedoorn, J.; Duysters, G. Learning in dynamic inter-firm networks: The efficacy of multiple contacts. Organ. Stud. 2002, 23, 525–548. [Google Scholar] [CrossRef]
- Chandler, A.D. Scale and Scope: The Dynamics of Industrial Capitalism; Harvard University Press: Cambridge, MA, USA, 1990. [Google Scholar]
- Haspeslagh, P.C.; Jemison, D.B. Managing Acquisitions: Creating Value through Corporate Renewal; Free Press: New York, NY, USA, 1991. [Google Scholar]
- Cohen, W.M.; Levinthal, D.A. Absorptive capacity: A new perspective on learning and innovation. Admin. Sci. Q. 1990, 35, 128–152. [Google Scholar] [CrossRef]
- Kang, K.; Kim, M.; Yoon, W. Technology support policy and SMEs’ technological capabilities: The mediating role of industry, academia and research cooperation. J. Strateg. Manag. 2020, 23, 47–64. [Google Scholar] [CrossRef]
- Söderblom, A.; Samuelsson, M.; Wiklund, J.; Sandberg, R. Inside the black box of outcome additionality: Effects of early-stage government subsidies on resource accumulation and new venture performance. Res. Policy 2015, 44, 1501–1512. [Google Scholar] [CrossRef]
- Freeman, C.; Soete, L. The Economics of Industrial Innovation; MIT Press: Cambridge, MA, USA, 1997. [Google Scholar]
- Henderson, R.; Cockburn, I. Measuring competence: Exploring firm-effects in pharmaceutical research. Strateg. Manag. J. 1994, 15, 63–84. [Google Scholar] [CrossRef] [Green Version]
- Cloodt, M.; Hagedoorn, J.; Van Kranenburg, H. Mergers and acquisitions: Their effect on the innovative performance of companies in high-tech industries. Res. Policy 2006, 35, 642–654. [Google Scholar] [CrossRef]
- Higgins, M.J.; Rodriguez, D. The outsourcing of R&D through acquisitions in the pharmaceutical industry. J. Financ. Econ. 2006, 80, 351–383. [Google Scholar] [CrossRef]
- Bertrand, O.; Zuniga, P. R&D and M&A: Are cross-border M&A different? An investigation on OECD countries. Int. J. Ind. Organ. 2006, 24, 401–423. [Google Scholar] [CrossRef]
- Canace, T.G.; Mann, S.V. The impact of technology-motivated M&A and joint ventures on the value of IT and non-IT firms: A new examination. Rev. Quant. Financ. Account. 2014, 43, 333–366. [Google Scholar] [CrossRef]
- De Man, A.P.; Duysters, G. Collaboration and innovation: A review of the effects of mergers, acquisitions and alliances on innovation. Technovation 2005, 25, 1377–1387. [Google Scholar] [CrossRef]
- Kallunki, J.P.; Pyykkö, E.; Laamanen, T. Stock market valuation, profitability and R&D spending of the firm: The effect of technology mergers and acquisitions. J. Bus. Financ. Account. 2009, 36, 838–862. [Google Scholar] [CrossRef]
- Ornaghi, C. Mergers and innovation in big pharma. Int. J. Ind. Organ. 2009, 27, 70–79. [Google Scholar] [CrossRef]
- Hitt, M.; Harrison, J.; Ireland, R.D.; Best, A. Attributes of successful and unsuccessful acquisitions of US firms. Br. J. Manag. 1998, 9, 91–114. [Google Scholar] [CrossRef]
- Zollo, M.; Singh, H. Deliberate learning in corporate acquisitions: Post-acquisition strategies and integration capability in US bank mergers. Strateg. Manag. J. 2004, 25, 1233–1256. [Google Scholar] [CrossRef]
- Fowler, K.L.; Schmidt, D.R. Determinants of tender offer post-acquisition financial performance. Strateg. Manag. J. 1989, 10, 339–350. [Google Scholar] [CrossRef]
- Bouwman, C.; Fuller, K.; Nain, A. Market valuation and acquisition quality: Empirical evidence. Rev. Financ. Stud. 2009, 22, 633–679. [Google Scholar] [CrossRef]
- Healy, P.M.; Palepu, K.G.; Ruback, R.S. Does corporate performance improve after mergers? J. Financ. Econ. 1992, 31, 135–175. [Google Scholar] [CrossRef] [Green Version]
- Loughran, T.; Ritter, J.R. The operating performance of firms conducting seasoned equity offerings. J. Financ. 1997, 52, 1823–1850. [Google Scholar] [CrossRef]
- Kim, B.; Jung, J. What causes the size effect and the diversification effect in the global M&A transactions? Korean J. Financ. Stud. 2016, 45, 507–529. [Google Scholar]
- Han, M.Y.; Shin, Y.K. The corporate governance and mergers effects. J. Bus. Educ. 2018, 35, 121–147. [Google Scholar] [CrossRef]
- Kim, B.J.; Jung, J.Y.; Cho, S.W. Listing effect in acquirer returns and economic growth uncertainty in the target country: The case of cross-border M&A from emerging economies. Emerg. Mark. Financ. Trade 2021, 57, 427–443. [Google Scholar] [CrossRef]
Year | 2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | |
---|---|---|---|---|---|---|---|---|---|---|
Country | ||||||||||
Korea | 55,822 | 52,063 | 52,379 | 66,220 | 57,735 | 56,110 | 66,670 | 65,571 | ||
USA | −77,954 | −78,493 | −73,547 | −111,198 | −119,298 | −115,349 | −111,259 | −120,162 | −139,194 | |
Japan | 25,686 | 21,302 | 6257 | 5505 | −3092 | −17,908 | −31,114 | −34,489 | −33,101 | |
Germany | 21,109 | 24,068 | 15,985 | 8818 | 16,138 | 36,456 | 40,680 | 36,439 | 37,574 | |
France | 5595 | 11,624 | 8454 | 9074 | 8487 | 13,366 | 18,435 | 16,644 | 11,567 | |
UK | −22,741 | −19,937 | −13,304 | −19,651 | −6823 | −11,848 | −17,635 | −27,456 | −22,505 |
M&A Cases between Korean Firms from 2002 to 2015 | 8063 Cases |
---|---|
Sample conditions:
| |
Final sample size | 2824 cases |
Year | Total | Group with High-Tech Acquirers | Group with Non-High-Tech Acquirers | |||
---|---|---|---|---|---|---|
# of Cases | % | # of Cases | % | # of Cases | % | |
2002 | 53 | 1.9 | 20 | 1.5 | 33 | 2.2 |
2003 | 37 | 1.3 | 13 | 1.0 | 24 | 1.6 |
2004 | 40 | 1.4 | 13 | 1.0 | 27 | 1.8 |
2005 | 50 | 1.8 | 16 | 1.2 | 33 | 2.2 |
2006 | 293 | 10.4 | 154 | 11.5 | 138 | 9.3 |
2007 | 260 | 9.2 | 137 | 10.2 | 120 | 8.1 |
2008 | 359 | 12.7 | 183 | 13.7 | 174 | 11.8 |
2009 | 332 | 11.8 | 167 | 12.5 | 165 | 11.2 |
2010 | 298 | 10.6 | 147 | 11.0 | 149 | 10.1 |
2011 | 249 | 8.8 | 127 | 9.5 | 122 | 8.3 |
2012 | 231 | 8.2 | 103 | 7.7 | 128 | 8.7 |
2013 | 171 | 6.1 | 62 | 4.6 | 109 | 7.4 |
2014 | 176 | 6.2 | 70 | 5.2 | 106 | 7.2 |
2015 | 275 | 9.7 | 125 | 9.3 | 149 | 10.1 |
Total | 2824 | 100 | 1337 | 100 | 1477 | 100 |
Variable | Total (N = 2824) | Group with High-Tech Acquirers (N = 1337) | Group with Non-High-Tech Acquirers (N = 1447) | |||
---|---|---|---|---|---|---|
Mean | Median | Mean | Median | Mean | Median | |
LN Market cap | 11.8278 | 11.2574 | 11.5602 | 10.8827 | 12.0701 | 11.5981 |
Debt ratio | 188.6181 | 96.3750 | 161.0180 | 79.2900 | 213.6021 | 115.5100 |
Organizational age | 26.2043 | 22.0000 | 20.5079 | 16.0000 | 31.3609 | 31.0000 |
Disclosure Timeframe (Days) | Total (N = 2824) | High-Tech Group (N = 1337) | Non-High-Tech Group (N = 1447) | |||
---|---|---|---|---|---|---|
Mean | T-Value | Mean | T-Value | Mean | T-Value | |
−10AR | 0.002 | 2.241 ** | 0.001 | 1.245 | 0.002 | 1.887 ** |
−9AR | 0.000 | 0.245 | 0.001 | 0.743 | 0.000 | −0.451 |
−8AR | 0.001 | 1.294 | 0.001 | 0.671 | 0.001 | 1.191 |
−7AR | 0.002 | 2.857 *** | 0.001 | 1.137 | 0.003 | 2.987 *** |
−6AR | 0.003 | 2.958 *** | 0.002 | 1.750 * | 0.003 | 2.404 ** |
−5AR | 0.002 | 1.950 * | 0.003 | 2.651 *** | 0.000 | −0.002 |
−4AR | 0.002 | 2.877 *** | 0.003 | 2.285 ** | 0.002 | 1.775 * |
−3AR | 0.003 | 4.203 *** | 0.005 | 3.981 *** | 0.002 | 1.774 * |
−2AR | 0.002 | 2.909 *** | 0.003 | 2.342 ** | 0.002 | 1.749 * |
−1AR | 0.008 | 8.792 *** | 0.009 | 6.949 *** | 0.006 | 5.425 *** |
0AR | 0.010 | 9.484 *** | 0.012 | 7.635 *** | 0.008 | 5.794 *** |
1AR | 0.005 | 4.220 *** | 0.004 | 2.355 ** | 0.005 | 3.666 *** |
High-Tech Group (N = 1337) | Non-High-Tech Group (N = 1447) | Difference in Mean | T-Value | |
---|---|---|---|---|
CAR(−1,+1) | 0.0251 *** | 0.0193 *** | 0.0058 | 1.5908 |
CAR(−2,+1) | 0.0281 *** | 0.0212 *** | 0.0069 | 1.6975* |
CAR(−3,+1) | 0.0333 *** | 0.0229 *** | 0.0104 | 2.3025 ** |
CAR(−4,+1) | 0.0362 *** | 0.0248 *** | 0.0114 | 2.3174 ** |
CAR(−5,+1) | 0.0395 *** | 0.0248 *** | 0.0147 | 2.7732 *** |
CAR(−10,+1) | 0.0461 *** | 0.0338 *** | 0.0123 | 1.9116 * |
Total (N = 1865) | High-Tech Group (N = 892) | Non-High-Tech Group (N = 973) | ||||
---|---|---|---|---|---|---|
Mean | T-Value | Mean | T-Value | Mean | T-Value | |
AROCF(−1,+1) | −0.008 | −2.242 ** | −0.006 | −1.013 | −0.011 | −2.235 ** |
AROCF(−1,+2) | −0.003 | −0.947 | 0.002 | 0.308 | −0.007 | −1.949 * |
AROCF(−1,+3) | −0.017 | −2.291 ** | −0.016 | −1.091 | −0.018 | −3.645 *** |
AROCF(−2,+1) | −0.013 | −3.136 *** | −0.018 | −2.875 *** | −0.008 | −1.530 |
AROCF(−2,+2) | −0.007 | −2.135 ** | −0.010 | −1.891 * | −0.005 | −1.093 |
AROCF(−2,+3) | −0.021 | −2.823 *** | −0.027 | −1.893 * | −0.015 | −2.875 *** |
Variables | Model 1 | Model 2 | |
---|---|---|---|
(1) | (2) | (3) | |
A_High_D | 0.014 ** (2.192) | −0.028 * (−1.655) | |
T_High_D | −0.013 ** (2.167) | −0.002 (−0.189) | |
A_Year × A_High_D | 0.001 * (1.781) | ||
AHTH | 0.001 (0.163) | ||
ALTH | −0.021 ** (−2.229) | ||
AHTL | 0.009 (1.132) | ||
A_Year | 0.000 (−0.042) | 0.000 (−0.041) | 0.000 (1.288) |
T_List_D | −0.007 (−1.207) | −0.007 (−1.267) | −0.016 * (−1.811) |
SameG_D | −0.002 (−0.319) | −0.003 (−0.437) | −0.011 (−1.107) |
D/E_r | 0.000(−0.017) | 0.000 (−0.009) | 0.000 *** (3.839) |
Size | −0.009 *** (−6.010) | −0.009 *** (−6.071) | −0.003 (−1.322) |
Intercept | 0.170 *** (8.307) | 0.173 *** (8.379) | −0.030 (−0.324) |
Ind_D | Yes | Yes | Yes |
Year_D | Yes | Yes | Yes |
F-value | 3.830 | 3.736 | 2.931 |
(p-value) | (0.001) | (0.001) | (0.001) |
Adj. R2 | 0.026 | 0.027 | 0.026 |
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Cho, S.-w.; Jung, J.-y.; Kim, B.-J.; Song, H. What Causes the M&A Performance of High-Tech Firms? Sustainability 2022, 14, 2820. https://doi.org/10.3390/su14052820
Cho S-w, Jung J-y, Kim B-J, Song H. What Causes the M&A Performance of High-Tech Firms? Sustainability. 2022; 14(5):2820. https://doi.org/10.3390/su14052820
Chicago/Turabian StyleCho, Sung-woo, Jin-young Jung, Byoung-Jin Kim, and Hyunjoo Song. 2022. "What Causes the M&A Performance of High-Tech Firms?" Sustainability 14, no. 5: 2820. https://doi.org/10.3390/su14052820