Firms acquire the effectively operating companies in order to adopt the effective business processes and technology, or to eliminate the performant competitor from the market or both. The importance of this motive is affirmed by the positive significant influence of the target's ROE on the M&A performance.

Substantiality of the R&D for this industry is confirmed by the significance and positive signs of the variables for target and acquirer. As for medical and biotech industry we prove the joint significance of the factor by likelihood ratio test, which results is presented on the Picture 3 in supplement. According to the test statistics the variables of target's and acquirer's R&D are jointly significant, that affirm the motive of R&D scale accumulation for computer and electrical equipment industry.

To sum up the findings for the medical technology industry, the industry specificity of the M&A deals is stipulated by the main factors which influence the market respond to M&A, the deal performance. For the deal performance the most important features are the scale of target's operation, combined amount of R&D expenses, and acquirer's growth options. The positive sign and significance of the first factor attests the importance of scale acquisition for medical and biotech industry and proves the hypothesis 9. Second finding indicates the importance of acquirer's ability to transfer the value from R&D to market capitalization and affirm the hypothesis 10. The joint significance of the target's and acquirer's R&D shows the actuality of the critical mass of the R&D accumulation through M&A and proves the hypothesis 5.

For the software industry the most important motives are the R&D acquisition, scale of operation accumulation and purchasing of the target with lower growth options. R&D plays the significant role in software industry and translates into the possible market opportunities, the importance of this factor is proved by positive significant impact of the variable, that affirm hypothesis 4. Motive to obtain the company with high amount of R&D expenses which value was not transferred to the market entirely is also substantial. It affirmed by the negative significant value of the target's growth options and confirm the hypothesis 11. Access to the new groups of customers or product range expansion through M&A is also important motive for software industry. It is corroborated by the positive and significant value of the target's cash flow variable. The hypothesis 8 is proved.

The positive significant influence of target's ROE on the M&A performance in computer and electrical equipment industry confirm the motive of effective firm's acquisition and prove the hypothesis 2. Hypothesis 6 implicating the motive of R&D scale accumulation through R&D was also proved by positive and joint significance of the according variables.

To conclude, we conduct the regression analysis and prove the hypotheses created in the paper. The distinct influence of the factors on acquirer's M&A performance across several industries was demonstrated. The hypothesizes for communication and automotive and aerospace industries was not checked due to industrial dummy variables insignificance and small amount of observations. However, the conclusions made for medical/biotech, computer/electrical equipment and software industries indicates the strong industry specificity influence on the M&A performance of the acquirer.

Conclusion

In the paper, we have chosen factors that could be used as a proxy to characterize the motives numerically and check their influence on the M&A performance. We have showed the different influence of the factors on M&A performance according to the industry specificity and have proved the majority of hypotheses made. Even though, all high-tech M&A deals were initially divided into five groups with respect to the motives and industry specificity, the hypotheses have been checked for medical technology and biotech, software and computer/electrical equipment.

In the paper, we have found the higher return of high-tech M&A deals if the acquirer is also a high-tech company. Regression analysis of the data allowed us to prove several hypotheses made after the literature review. The main motives for M&A in medical equipment and biotech industries are R&D mass accumulation for the acquirer, the potential increase in the scale of operation and the ability of the acquirer to efficiently transform the R&D investments into the market value of the company. Software companies in M&A follow the motives of R&D acquisition, increase in the scale of operations accompanied by low ability of the target to create companies' market capitalization using R&D. Acquirers in the computer and electrical equipment industry pursue the goal to obtain a high performing company and create R&D synergy by M&A.

Even though, theoretical conclusions have been made for automotive and aerospace equipment and communications industries, the empirical research for them was unsuccessful. The reason for it is the low share of this deals in the sample; the amount of them does not allow to conduct the proficient regression analysis with the considered amount of variables. The variables controlling for the industry in the regressions were insignificant. However, for this particular research, the removal of sample collection restrictions was not desirable, because it would lead to violations of homogeneity of the sample. It has been conditioned by the same country of companies' incorporation, high quality of data presented due to uniform reporting standards and the period that covers a long period but stay in the similar M&A paradigm according to the M&A wave theory. Thus, an increase in the period that has been considered or additional markets inclusion might have a negative impact on the whole research, because it might bring additional factors that influence the M&A performance beyond the subject of the research.

Obtained results have an array of limitations. Firstly, it is the consequence of the regression analysis approach: obtained results are applicable to the whole market in general, but may have a different impact while considering a particular M&A deal. Secondly, the determination of a high-tech company that is used for the paper is based on SIC codes. The company might indicate itself with the various SIC codes, including non-high-tech, and the field of operation might be blurred.

Mentioned above limitations and imperfections of the paper disclose the prospects research extensions. The first part of the extensions might be dedicated to the theoretical section improvement. Several hypotheses have been made, but the number of researches dedicated to the subject is rather small. The possible direction of the researches is to conduct a detailed study in the communication and automotive and aerospace industries and M&A in these fields. The base level of this research might include a detailed overview of the several deals, including financial indicators investigation, deep interview with companies' stakeholders and market's reaction measurement.

The second field of improvement is to check the research's results applicability to other markets. This direction might include other developed and emerging markets in order to prove the results on other examples. The USA market for this research was chosen due to its homogeneity, transfer of the results on other economies might force the researcher to take into account the market specificity. In the paper, only one macroeconomic factor has been applied - GDP change. In case when acquirers or targets are incorporated within different countries, should be taken into account not only the economic expansion factors, but also variables considering the cultural differences, for example, Hofstede measurements.

Another field of prospect researches is cross-industrial analysis. The group of deals when the acquirer is a high-tech company and target is the non-high-tech company is also an interesting subject of study. Due to the high difference in industry specificity, proved by this paper, the performance of such deals is unpredictable, and the level of investigation of this subject in the literature is low.

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Supplement

Table 7. Hypotheses overview

Picture 2. Likelihood ratio test's results for R&D expenses joint influence in medical technology and biotech industry

Source: author's calculations

Picture 3. Likelihood ratio test's results for R&D expenses joint influence in computer and electrical equipment industries

Source: author's calculations

Table 8. Regressions with industrial dummy variables

Source: author's calculations

*P-values are significant at 10% level.

**P-values are significant at 5% level.

***P-values are significant at 1% level.

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