Firm Size and Information Technology Investment: Beyond Simple Averages

We attempt to gain a better perspective on evolving firm-size in the past 20 years across industries by combining the empirical framework of Brynjolfsson et al. (1994) for measuring the effect of coordination cost reduction due to information technology investment, and the synopsis of theories of the firm by Kumar et al. (2001). We find that although in general Brynjolfsson et al.’s result holds for new firm data from COMPUSTAT, the firm size of the professional service sector grows as IT investment increases. The paper’s potential contributions to empirical methods include (1) a different focus on the measurement of firm size by utilizing the weighted average employee-measure of firm size adopted by Kumar et al. work to replicate Brynjolfsson et al.’s findings with a new dataset, and (2) refinement of Kumar et al.’s weighted average employee-measure of firm size using entropy partition techniques from the machine learning literature, to fully account for the effect of larger firms within each industry

Models of Customer Behavior: From Populations to Individuals

There have been various claims made in the marketing community about the benefits of 1-to-1 marketing versus traditional customer segmentation approaches and how much they can improve understanding of customer behavior. However, few rigorous studies exist that systematically compare these approaches. In this paper, we conducted such a systematic study and compared the performance of aggregate, segmentation, and 1-to-1 marketing approaches across a broad range of experimental settings such as multiple segmentation levels, multiple real world marketing datasets, multiple dependent variables, different types of classifiers, different segmentation techniques, and different predictive measures. Our results show that, overall, 1-to-1 modeling significantly outperforms the aggregate approach among high-volume customers and is never worse than aggregate approach among low-volume customers in our experimental settings. Moreover, the best segmentation techniques tend to outperform 1-to-1 modeling among low-volume customers.Information Systems Working Papers Serie

Shipper's title to sue after the transfer of the bill of lading: A comparative study for the reform of Chinese Maritime Law

Cargo interests’ title to sue the carrier to recover loss or damage caused by the carrier’s default is a crucial issue in carriage of goods by sea. However, the current maritime code in China does not provide explicit guidance on this issue. One significant problem that arises therefrom is whether the shipper who has transferred the bill of lading to the endorsee/consignee is still entitled to sue the carrier. This article critically examines the current rule under the Chinese Maritime Code 1993 and pinpoints the fundamental loophole that gives rise to the aforesaid problem. In addition, based on reviewing various solutions provided by other jurisdictions, this article discusses the possible solution that could be considered when reforming current maritime law in Chin

The Latest Development of the Insurance Law in Life Insurance in China:The Third Judicial Interpretation on the Insurance Law by the Supreme People’s Court of China

The third Interpretation on the Chinese Insurance Law by the Supreme People’s Court came into force on 1 December 2015. It has clarified many long-disputed issues relating to insurable interest, beneficiaries and other issues in life insurance in China. This paper critically examines the provisions of the Interpretation and discusses the new development of the Insurance Law by the Interpretation on these issues

Tensorized Self-Attention: Efficiently Modeling Pairwise and Global Dependencies Together

Neural networks equipped with self-attention have parallelizable computation, light-weight structure, and the ability to capture both long-range and local dependencies. Further, their expressive power and performance can be boosted by using a vector to measure pairwise dependency, but this requires to expand the alignment matrix to a tensor, which results in memory and computation bottlenecks. In this paper, we propose a novel attention mechanism called "Multi-mask Tensorized Self-Attention" (MTSA), which is as fast and as memory-efficient as a CNN, but significantly outperforms previous CNN-/RNN-/attention-based models. MTSA 1) captures both pairwise (token2token) and global (source2token) dependencies by a novel compatibility function composed of dot-product and additive attentions, 2) uses a tensor to represent the feature-wise alignment scores for better expressive power but only requires parallelizable matrix multiplications, and 3) combines multi-head with multi-dimensional attentions, and applies a distinct positional mask to each head (subspace), so the memory and computation can be distributed to multiple heads, each with sequential information encoded independently. The experiments show that a CNN/RNN-free model based on MTSA achieves state-of-the-art or competitive performance on nine NLP benchmarks with compelling memory- and time-efficiency

Studies of Phase Transitions of Chromium Coordination Compounds under High Pressure

In this paper, the high pressure Raman scattering spectroscopy of Cd2(HATr)4(NO3)4·H2O (Cd) was measured by diamond anvil cells (DACs) up to 10GPa. The Raman spectra of Cd at 0GPa was assigned completely. With pressure increased to 6GPa, a new Raman peak appeared and the original C-NH2 bending vibration mode and N-NH2 bending vibration mode disappeared, indicating that Cd underwent a phase transition