Information Technology, Cross-Channel Capabilities, and Managerial Actions: Evidence from the Apparel Industry

Information technology (IT) has changed the dynamics of competition in the U.S. economy. Firms are gaining competitive advantage by competing on technology-enabled processes. For the retail industry, technology is breaking down the barriers between different retail channels and is making omnichannel retailing inevitable—an integrated sales experience that melds touch-and-feel information in the physical world with online content. Omnichannel retailing is becoming a trend and critical for retailers’ success. To keep up with the pace of change, existing retailers will need to create an omnichannel strategy and develop more omnichannel innovations. Based on the theories of the resource-based view (RBV), IT business value, and competitive dynamics, this study examines the factors that affect cross-channel capabilities and managerial actions in the U.S. apparel industry. We collected a longitudinal dataset on public apparel companies from 1995 to 2007. The empirical results reveal that both the quantity and scope of investments in enterprise IT applications were positively related to cross-channel capabilities. Financial resources positively moderated the relationship between enterprise IT applications and cross-channel capabilities. We found that enterprise IT applications increased the frequency and broadened the types of managerial actions. We found that cross-channel capabilities had mixed effects on managerial actions. Whereas market-oriented capabilities such as e-commerce and multi-channel cross-selling capabilities broadened the types of managerial actions, operation-oriented capabilities, such as cross-channel fulfillment, narrow the range of a firm’s managerial actions. Our findings provide important implications for managers in apparel and other retail sectors

Detecting genuine multipartite entanglement via machine learning

In recent years, supervised and semi-supervised machine learning methods such as neural networks, support vector machines (SVM), and semi-supervised support vector machines (S4VM) have been widely used in quantum entanglement and quantum steering verification problems. However, few studies have focused on detecting genuine multipartite entanglement based on machine learning. Here, we investigate supervised and semi-supervised machine learning for detecting genuine multipartite entanglement of three-qubit states. We randomly generate three-qubit density matrices, and train an SVM for the detection of genuine multipartite entangled states. Moreover, we improve the training method of S4VM, which optimizes the grouping of prediction samples and then performs iterative predictions. Through numerical simulation, it is confirmed that this method can significantly improve the prediction accuracy.Comment: 9 pages, 8 figure