O utsourcing of information technology (IT) services has received much attention in the information systems (IS) literature. However, considerably less attention has been paid to actual contract structures used in IT outsourcing (ITO). Examining contract structures yields important insights into how the contracting parties structure the governance provisions and the factors or transaction risks that influence them. Based on insights from prior literature, from practicing legal experts, and through in-depth content analysis of actual contracts, we develop a comprehensive coding scheme to capture contract provisions across four major dimensions: monitoring, dispute resolution, property rights protection, and contingency provisions. We then develop an empirical data set describing the contract structures across these distinct dimensions, using a sample of 112 ITO contracts from the Securities and Exchange Commission (SEC) database from 1993 to 2003.Drawing on transaction cost, agency, and relational exchange theories, we hypothesize the effects of transaction and relational characteristics on the specific contractual provisions, as well as on overall contract extensiveness. Furthermore, we examine how these associations vary under conditions of fixed price and time and materials pricing structures. The results provide good support for the main hypotheses of the study and yield interesting insights about contractual governance of ITO arrangements.
In this paper, we propose an end-to-end deep neural network for solving the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images. To conduct radiotherapy planning for nasopharyngeal cancer, more than 10 organs-at-risk (normal organs) need to be precisely segmented in advance. However, the size ratio between large and small organs in the head could reach hundreds. Directly using such imbalanced organ annotations to train deep neural networks generally leads to inaccurate small-organ label maps. We propose a novel end-to-end deep neural network to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ sub-networks while maintaining the accuracy of large organ segmentation. A strong main network with densely connected atrous spatial pyramid pooling and squeeze-andexcitation modules is used for segmenting large organs, where large organs' label maps are directly output. For small organs, their probabilistic locations instead of label maps are estimated by the main network. High-resolution and multi-scale feature volumes for each small organ are ROI-pooled according to their locations and are fed into small-organ networks for accurate segmenting small organs. Our proposed network is extensively tested on both collected real data and the MICCAI Head and Neck Auto Segmentation Challenge 2015 dataset, and shows superior performance compared with state-of-the-art segmentation methods.
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