Language Similarity and M&A Transactions

ABSTRACT

We investigate how language similarity between acquirers and targets affects post-M&A performance using a large sample of M&A transactions in China. We hypothesize and find that acquirers achieve higher post-M&A operating performance when they acquire targets that share the same native languages. We provide evidence that language similarity enhances post-M&A performance via the communication channel. Additionally, we show that the impact of language similarity is more pronounced when acquirers are not controlled by government agencies and when they are not related parties of targets. This study indicates an important role of native language similarity on post-M&A performance.

Keywords:

• Language Similarity
• M&A
• Information Asymmetry
• Integration Efficiency

JEL codes:

• G14
• G34
• Z13

Acknowledgements

The authors are grateful for insightful comments from Beatriz García Osma (the editor), one anonymous reviewer, Kevin Huang, Ben Marshall, Sean Sun, Nuttawat Visaltanachoti, Jeff Wongchoti, George Wu, Karen Zhang, and seminar participants at the Massey University and the University of Queensland. All errors are our own.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The employees involved in post-M&A integration include top managers, mid-level managers, and key employees in the areas of research and development, manufacturing, marketing, sales, finance, etc. (Reus & Lamont, 2009). Paruchuri et al. (2006) show that inventors from the target firm may not work well with the new policies, procedures and supervision, and therefore underperform after M&A.

2 M&A requires extensive oral communication (Slangen, 2011). Although people in China all use the same written language, the languages they speak still form barriers in communication (Egger & Lassmann, 2015). Even if these people use standard Chinese Mandarin to communicate, their native dialect will affect their expressions and pronunciations in standard Chinese Mandarin, which creates language barriers. For example, their native dialect may affect their tone and therefore induce misunderstanding (Zhang, 2021). Standard Chinese Mandarin is a tonal language. When the same syllable is combined with different tones, it conveys different information (Feng et al., 2012). When people say ‘shí sì dūn,’ that means ‘fourteen tons.’ However, when people say ‘shì sì dūn,’ that means ‘four tons.’

3 The report is available at http://old.moe.gov.cn/publicfiles/business/htmlfiles/moe/cmsmedia/document/2594.pdf (In Chinese).

4 As required by the China Securities Regulatory Commission, companies with negative net income for two consecutive years are labelled as special treatment firms and receive restriction on the trading of their stocks (with ST or ST* in front of code name).

5 In China, a prefecture-level city always contain several counties. If there is only one dialect used in a city, we use location city to identify the dialect. If there are more than one dialects used in a city, we use location county to identify the dialect.

6 In our baseline sample, we only require the acquirers to be public firms. Therefore we can only observe the performance change of the acquirers.

7 The intelligibility data is only available for 11 dialect groups. Therefore, we have to drop 215 deals where acquirers and targets share a common dialect that is not covered by Tang and Van Heuven (2009).

8 The results are similar if we use ΔCF as the dependent variable.

9 The results are similar, if we require the local residence ratio to be higher than 60%, 70%, or 90%.

10 The results are similar if we use ΔCF as the dependent variable.

11 When we study the impact of language similarity on goodwill impairment, we exclude 19 M&As where acquirers and targets are under the control of the same controlling shareholders. According to Accounting Standard for Enterprises No. 20 – Business Combination, acquirers cannot recognise goodwill if the acquirers and targets are ultimately controlled by the same controlling shareholders before the deal. When we study the impact of language similarity on deal completion likelihood, we include 216 withdrawn M&A deals, and therefore the sample size increases to 2411.

12 For comparison purpose, we also examine how the language similarity between target firms’ employees and acquirer firms affects these M&A outcomes. We repeat the analyses in Table 5 by using Similarity employee as the independent variable. We then report the regression results in Table A3 in the Appendix. The results suggest that employee-level language similarity help reduce the percentage of equity payment and likelihood to write down goodwill or to divest acquired assets.

13 For comparison purpose, we also examine how manager-level language similarity affects post-M&A TFP. We repeat the analyses in Table 6 by using Similarity manager as the independent variable, and report the regression results in Table A4 in the Appendix. Across all the three columns, the coefficients on Similarity manager are not significant.

14 The results are similar if we use ΔCF as the dependent variable.

15 The results are similar if we use ΔCF as the dependent variable.

Additional information

Funding

Pan acknowledges the financial support from the Faculty of Business and Law at University of Wollongong. Zhang acknowledges the financial support from Monash University.