Abstract
Digital technology firms operate in increasingly dynamic and hypercompetitive environments where firms must innovate by exploiting knowledge in existing domains and exploring new ones. Previous research has suggested that firms must balance their exploratory and exploitative knowledge. Firms use their internal organizational structures to incubate exploratory and exploitative knowledge, extend the life of existing products and services, spawn innovations, and generate revenue. However, the structure-knowledge-firm performance link remains largely untested among digital technology firms. Furthermore, firms that do not possess ex-ante incubation structures to balance knowledge output can find it challenging to migrate to such structures. Here, prior research prescribes acquisitions as a route to achieve balance. In this paper, we propose Knowledge-Balancing Acquisitions (KBAs) as the differentiating serial acquisition strategy that determines whether M&As affect the knowledge output and performance of the acquiring firms in a positive fashion. Our primary research question is: Does KBA improve firm performance and moderate the structure-knowledge link for acquiring firms that pursue it? Using a dataset of M&As conducted by serially acquiring and patenting digital technology firms over sixteen years and structure and knowledge output data constructed from patent data, our empirical analysis finds support for a structure-knowledge-performance link, as well as evidence for balanced knowledge output and superior performance for acquirers that pursued KBA. Our study’s main contribution is that firms that pursue KBA during their serial acquisitions create balanced knowledge output and enjoy superior firm performance compared to firms that do not pursue KBA.
DOI
10.17705/1jais.00890
Recommended Citation
Bandodkar, Nikhil R.; Grover, Varun; and Singh, Renu, "Knowing What to Acquire in the Digital Technology Industry: Balancing Exploitation and Exploration for Superior Performance" (2024). JAIS Preprints (Forthcoming). 139.
DOI: 10.17705/1jais.00890
Available at:
https://aisel.aisnet.org/jais_preprints/139