# Impact of cross-country mergers and acquisitions on operating cost

The previous article highlighted the impact of **mergers and acquisitions (M&A)** on the firm’s current ratio. A dataset containing values of 100 mergers and acquisitions **(M&A)** from Europe and Asia during 2007-2017 was utilised. It was found that, in the pre-merger period, the leverage ratio of the acquired firm positively impacted the current ratio. While in the post-merger period the deal size and the leverage ratio impacted the current ratio of acquiring firms positively. Amihud & Miller (2011) in their study highlighted that mergers and acquisitions can also lead to an increase in operating cost if the operation culture of the potential partners is different.

This article examines the impact of M&A activities on the operating cost. Through regression analysis, the acquirer’s pre-merger and post-merger effect on the operating costs is compared.

## Proposed hypothesis

In order to examine the impact of mergers and acquisitions on the operating cost of the firms, the following null hypothesis is framed:

H

_{0}: There is no significant effect of cross -country mergers and acquisitions on operating cost in the period 2000- 2017.

The data set is divided into two periods.

- The time period t-1 represents the pre-merger period and,
- the time period t+1 represents the post-merger period.

## Empirical findings of the impact of the leverage ratio on operating cost

In the case of pre-merger performance, the dependent variable is operating cost and the set of independent variables include the following:

- size of the firm
- geographical expansion
- R &D innovation
- leverage ratio.

The table below presents the regression result for the pre-merger performance.

Dependent variable | Independent variables | Coefficient | P-value | R^{2 }– value | Adjusted R^{2 }-value |
---|---|---|---|---|---|

Operating cost | Size of firm | 2.83446 | 0.815 | 0.7052 | 0.6958 |

Geographical expansion | -5.107505 | 0.655 | |||

R&D innovation | 21.07849 | 0.077 | |||

Leverage ratio | .7965234 | 0.008 |

Table 1: Premerger performance (t-1)

R-square and the adjusted R-squared values are 0.70 and 0.69 respectively. This shows that the independent variable explains 69% variation independent variable. This means that the model is robust enough to explain the variation in the independent variable (Mooi, 2014). Thus, it can be concluded that in the pre-merger period, a high leverage ratio leads to a rise in the operating costs of the firm. Thus, there is a probability that the null hypothesis will be rejected.

The results clearly indicate that only the leverage ratio has a significant impact on the operating cost of the firm. As the P-value of leverage ratio (0.008) is less than the significance value of 0.05. Other independent variables like the size of the firm, geographical expansion and R&D innovation were rejected as their P-value came out to be greater than 0.05 which indicates that these variables do not have a significant effect on the operating cost of the firms in the pre-merger period.

## Pre-merger performance of the acquiring firms

The leverage ratio shows the capital borrowed to finance operations. The coefficient of leverage ratio indicates that with one unit of increase in the leverage ratio, the operating cost increases by about 0.79 units. In the pre-merger period, firms have borrowed more capital to finance their capital structure and thus increases the fixed cost for the firm. Moreover, the firms have to invest in the machinery and the production of goods and service. However, as these firms are unable to achieve economies of scale in their production. Their output remains low which in turn leads to a rise in operating cost (Saini & Singla, 2015).

## Empirical analysis to compare the post-merger performance

The table below presents the regression results for the post-merger firm performance.

Dependent variable | Independent variables | Coefficient | P-value | R^{2}-value | Adjusted R^{2}-value |
---|---|---|---|---|---|

Operating cost | Size of firm | 18.85601 | 0.338 | 0.7852 | 0.7233 |

Industry relatedness | 13.83828 | 0.595 | |||

Deal size | -.0023615 | 0.374 | |||

Geographical expansion | 30.81495 | 0.0052 | |||

R&D innovation | -14.74222 | 0.043 | |||

Leverage ratio | -1.106043 | 0.588 |

Table 2: Post-merger performance (t+1)

R-Square and the adjusted R-squared values are 0.78 and 0.72 respectively. This indicates that the independent variables explain about 72% variation in the dependent variable. This means that the model is good enough to explain variation in the dependent variable. Thus, there is a probability that the null hypothesis will be rejected.

## Variables affecting the post-merger performance of acquiring firms

The regression results for the post-merger performance are shown in the above table. The results clearly indicate that among all the independent variables, geographic expansion and R&D innovation have a significant impact on the operating cost of the firm. Since they have the P-value as 0.0052 and 0.043 respectively which is less than the significance value of 0.05.

**Geographical expansion:**The coefficient of the geographic expansion indicates that with one unit of increase in the geographic area, the operating cost increases by about 30.81 units. Expansion of operations in the new markets will lead to a rise in the amount of expenditure. This expenditure arises in the form of rent, office utilities, marketing and so on (Jung, 2007).**R&D innovation:**There is an inverse relationship between R&D innovation and the firms’ operating cost. In this context, the study of Dimofte, Johansson, & Ronkainen (2008), highlighted that R&D innovation can generate better product design that advantage over the other firms in terms of operating cost and profit. By innovating in terms of processes businesses can lower the production cost and improve product quality. Thus R&D innovation allows achieving greater efficiency.

Other independent variables like the size of the firm, industry relatedness, deal size and leverage ratio were rejected as their P-value came out to be greater than 0.05 which indicates that these variables do not have a significant effect on the operating cost of the firms in the post-merger period.

Thus the null hypothesis; there is no effect of cross-country mergers and acquisitions on operating cost in the period 2000-2017 is rejected.

## Geographical expansion and R&D innovation positively affect operating cost post M&A

This article presented a comparison between the performance in terms of operating cost in the pre-merger and the post-merger period. The regression result in the pre-merger period showed that the acquirer’s performance is impacted by leverage ratio which was leading to the rise in operating cost. It was mainly due to the capital borrowings to finance operations. While in the post-merger period it was the geographic expansion and the R&D innovation that impacted the firm’s performance. The geographic expansion that leads to increased operating cost due to market expansion adversely affected the firm performance. R&D innovation on the other had a positive impact on the performance by reducing redundant expenses. Thus, cross-country mergers and acquisitions lead to falling operating cost.

#### References

- Dimofte, C. V., Johansson, J. K., & Ronkainen, I. A. (2008). Cognitive and Affective Reactions of U.S. Consumers to Global Brands. In
*Journal of International Marketing*(Vol. 16). https://doi.org/10.1509/jimk.16.4.113 - Jung, S. (2007).
*The Relationship between international expansion and firm performance: An investigation of U . S . – based restaurants and firms*. 1–37. - Saini, A., & Singla, R. (2015). Impact of Mergers on Corporate Performance in India.
*Asian Journal of Research in Business Economics and Management*,*5*(3), 350. https://doi.org/10.5958/2249-7307.2015.00082.1 - Sarstedt M, Mooi, E. (2014). Conducting a Cluster Analysis. In
*A Concise Guide to Market Research*. https://doi.org/10.1007/978-3-642-53965-7

Priya is the co-founder and Managing Partner of Project Guru, a research and analytics firm based in Gurgaon. She is responsible for the human resource planning and operations functions. Her expertise in analytics has been used in a number of service-based industries like education and financial services.

Her foundational educational is from St. Xaviers High School (Mumbai). She also holds MBA degree in Marketing and Finance from the Indian Institute of Planning and Management, Delhi (2008).

Some of the notable projects she has worked on include:

- Using systems thinking to improve sustainability in operations: A study carried out in Malaysia in partnership with Universiti Kuala Lumpur.
- Assessing customer satisfaction with in-house doctors of Jiva Ayurveda (a project executed for the company)
- Predicting the potential impact of green hydrogen microgirds (A project executed for the Government of South Africa)

She is a key contributor to the in-house research platform Knowledge Tank.

She currently holds over 300 citations from her contributions to the platform.

She has also been a guest speaker at various institutes such as JIMS (Delhi), BPIT (Delhi), and SVU (Tirupati).

I am a master's in Economics from Amity university. Besides my keen interest in Economics i have been an active member of the team Enactus. Apart from the academics i love reading fictions.

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