We apply a contemporary approach to study the effect of organizational complexity on post-merger integration. A computational, virtual experiment was carried out to determine how the level of structural complexity, a characteristic of all formal organizations, impacts the dynamics of organization performance during the post-merger integration period. We found that performance during this period is affected by the pre-existing complexities of the two merging organizations; surprisingly, the organizations’ size was found to be only a marginally relevant factor, instead, the number of work groups had a greater consequence. Moreover, we found that the homogeneity tendencies of the actors may be the source of an upper constraint on the merged organization's performance. Consistent to these findings, we develop hypotheses for later empirical study. Broadly, this chapter puts forth computational modeling as a vital methodology for advancing mergers and acquisitions research; in addition, this chapter uncovers previously unpronounced, phenomenological discoveries that were found using this promising approach. Throughout this chapter, we endeavor to advance the broad use of computational modeling into the fore of leading-edge post-merger integration and related research and practice.
Frantz, T.L. and Carley, K.M. (2009), "Computationally modeling the effect of organizational complexity on post-merger integration", Cooper, C.L. and Finkelstein, S. (Ed.) Advances in Mergers and Acquisitions (Advances in Mergers & Acquisitions, Vol. 8), Emerald Group Publishing Limited, Bingley, pp. 79-101. https://doi.org/10.1108/S1479-361X(2009)0000008007
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