Choice of IT Governance Mode:Effect of IT infrastructure Agglomeration

Abstract

Information technologies have enabled growth of large firms with multiple business units spread across geographies and markets. Governance of IT infrastructure is a key strategic priority for managing digitally-enabled operations across these firms. Specifically, firms either govern the IT infrastructure centrally or decentralize the decision-making to business units. Making this choice is challenging. Offering foundation platforms, IT infrastructure enables (or limits) the business units’ capability for digitizing business operations. Using an option-based perspective, we argue that firms’ assessment of IT infrastructure options requires considering various future scenarios. An effective governance model facilitates assessment of these scenarios. In general, firms consider costs of coordination and responsiveness, while choosing centralized or decentralized mode of IT infrastructure governance. Previous research examines how the characteristics of business unit or central headquarters, or the relationship between the two, influence this choice of a governance mode. However, network effects, due to knowledge exchange between units, have been overlooked in the previous research. In this research, we argue that agglomeration effects influence the choice of a governance mode. Identifying these agglomeration effects, we assess if similarity of technologies–software and hardware–across business units influences the choice of IT infrastructure governance mode. Specifically, we propose and test if firms centralize IT infrastructure governance for a business unit that has similar hardware and software profiles as that of other business units in the firm. Study also assesses the moderating effect of organizational size on these relationships. We test relationships using data from CI Technology Database and additional data from SDC Platinum Mergers and Acquisitions, Lexis-Nexis corporate affiliation, and COMPUSTAT. We test various econometric time series models, to test our hypotheses. Study discusses the academic contributions and practical implications of our findings.