JultikaUniversity of Oulu repository

Abstract

The IT-industry has become ever more central in the world’s economy. It has a considerable market share and impact on the economy, while mergers and acquisitions (M&A) in the IT-industry are also prevalent. There is limited research specifically focusing on the IT sector M&A. The IT-sector has a number of peculiar characteristics and different business models which affect how business is conducted within the sector. This thesis attempts to study whether these differences manifest themselves by studying M&A performance by dividing the IT sector into software and hardware industries. No prior research was found that has studied the impact of this division — to software and hardware industries — on long-term M&A performance. There is, however, some research focusing specifically on software M&A.

This thesis delves into the intricacies of the IT industry, examining the distinctions within mergers and acquisitions (M&A), encompassing both software and hardware industries. Through empirical analysis, this study aims to ascertain whether there is a difference in the long-term accounting performance of M&A activities between software and hardware industries, particularly when factoring in the pre-acquisition performance of the acquired firms. The research question in the study is: do software and hardware industry M&A long-term accounting performance, as measured by return on equity (ROE), differ when considering the acquired firm’s pre-acquisition performance measured by ROE? The long-term study period in the study is four years after the acquisition. This study employs multiple linear regression and Mann-Whitney U test, utilizing data sourced from Refinitiv’s databases. The dataset includes publicly listed U.S. firms’ M&A activities between 2004 to 2016.

The results of the Mann-Whitney U test indicate disparities between the software and hardware industries in terms of M&A outcomes measured by change in ROE. While the multiple linear regression model also includes control variables, particularly acquirer size, which seems to negate the statistical significance of the target industry group. The results suggest no statistically significant correlation with the target industry group or the acquisition’s target firm pre-acquisition performance with the long-term accounting performance of M&A, as measured by ROE. This is in contrast to prior research on target firm pre-acquisition performance which suggests that lower performance leads to better M&A outcomes. The study highlights the importance of having proper controls: without acquirer size as a control factor, it would seem that the target industry group impacts the M&A outcome. This is in line with previous M&A research which suggests that acquirer size is a relevant control factor.