IT carve-out projects are complex and cost-intensive components of M&A transactions. Existing research sheds little light on the determinants of IT carve-out project complexity and/or its effects on divestor performance. Instead, research has focused on the post-acquisition IT integration project and acquirer performance. This paper presents the first divestor-centric model of IT transactions from the divestor to the acquirer when a Business Unit in a Multi-Business Organization (MBO) is carved out and integrated into another MBO. The model explains how divestor business and IT alignment pre-conditions contribute to increased IT carve-out project complexity. Such complexity increases IT carve-out project time to physical IT separation and creates IT stranded assets, which decrease post-divestment business, IT alignment and divestor performance. The current recommended strategy of adopting transitional service agreements (TSAs) to handle IT carve-out complexity is compared with two new proactive strategies derived from the model. TSA-based strategies restrict the divestor from both decommissioning IT stranded assets and reconfiguring its IT assets to support its new post-divestment business strategy. The two new strategies address IT carve-out complexity without incurring the negative effects from adopting TSAs.

Sharma and Yetton, in 2003, hypothesized and found empirical support for the moderating effect of task interdependence on the relationship between management support and implementation success. In this issue of EJIS, Hwang and Schmidt critique Sharma and Yetton's meta-analysis protocol, arguing that a random-effects model should have been employed, rather than the fixed-effect model they used. Further, Hwang and Schmidt claim that the moderating effect hypothesized by Sharma and Yetton is not supported when a random-effects model is employed. We re-analyze Sharma and Yetton's data by employing a dedicated, commercially available package for conducting random-effects meta-analysis. Our analysis finds no support for Hwang and Schmidt's claim. The random-effects meta-analysis supports Sharma and Yetton's original findings: task interdependence moderates the effect of management support on implementation success.

Software engineers use a number of different types of software development technical review (SDTR) for the purpose of detecting defects in software products. This paper applies the behavioral theory of group performance to explain the outcomes of software reviews. A program of empirical research is developed, including propositions to both explain review performance and identify ways of improving review performance based on the specific strengths of individuals and groups. Its contributions are to clarify our understanding of what drives defect detection performance in SDTRs and to set an agenda for future research. In identifying individuals' task expertise as the primary driver of review performance, the research program suggests specific points of leverage for substantially improving review performance. It points to the importance of understanding software reading expertise and implies the need for a reconsideration of existing approaches to managing reviews.