Model Evaluation of Post-M&A Success Metrics in the German Biopharmaceutical Industry
Damian Leschik
What the paper says
Purpose: This study evaluates the success metrics of post-merger and acquisition (M&A) activities in the German biopharmaceutical industry, focusing on small- and medium-sized enterprises (SMEs). It examines how metrics such as economies of scale, economies of scope, market share, clinical success rate, and efficient allocation of personnel and resources affect post-M&A revenue. Design/Methodology: The study uses partial least squares structural equation modeling (PLS-SEM) to analyze data from a survey of 384 biopharmaceutical SMEs in Germany. The survey targeted senior management involved in M&A processes, with the analysis assessing the reliability, convergent validity, and discriminant validity of the success metrics, and the statistical significance of the structural path coefficients. Findings: The results indicate that efficient allocation of personnel and resources, as well as clinical success rate, significantly impact post-M&A revenue. In contrast, economies of scale, economies of scope, and market share do not significantly affect revenue. The study also emphasizes that while M&A activities can lead to operational efficiencies and cost savings through synergies, these benefits alone do not ensure revenue growth without effective resource management and innovation. Internal efficiencies and clinical outcomes are more critical than market expansion strategies. Originality/Value: The study introduces a novel methodology for evaluating different success metrics in post-M&A performance within the biopharmaceutical industry using PLS-SEM. The combination of success metrics and their impact on post-M&A revenue is identified as a relatively unique research contribution.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.