Beyond Binary Decisions : Modeling the Frequency of Mergers and Acquisitions in the Indian IT Industry Using Count Data Models

Purpose : This paper investigated the impact of macroeconomic factors, inflation, exchange rate, interest rate, GDP, and economic policy uncertainty (EPU) on mergers and acquisitions (M&A) activity of the Indian IT industry. Design/Methodology/Approach : This paper used the count data models, Poisson and Negative Binomial models, for a period ranging from 2007 to 2023. Findings : We found several important findings: First, inflation showed no significant impact at any lag, while exchange rates had a significant and negative effect in the contemporary period and one lag, but lost significance after two lags. Second, GDP had a significant negative impact in both models, strongest at one lag, but lost significance in the Negative Binomial model at two lags. Third, interest rates had no significant effect on M&A for all periods. Fourth, EPU did not significantly affect the contemporary and one-lag periods but revealed a significant negative relationship in the two-lag period in the Poisson model. Practical Implications : The findings offered valuable implications for policymakers, business leaders, and investors who are involved in or considering M&A activities in this dynamic industry. Originality/Value : Despite comprehensive research on M&A decision-making, the literature lacked studies applying discrete outcome models like Poisson or Negative Binomial to model M&A activities, particularly in the Indian IT industry. Therefore, this research filled the methodological and contextual gap.