Knowledge acquisition in supply chain partnerships: The role of power

This is the post-print version of the final paper published in International Journal of Production Economics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.Knowledge is recognised as an important source of competitive advantage and hence there has been increasing academic and practitioner interest in understanding and isolating the factors that contribute to effective knowledge transfer between supply chain actors. The literature identifies power as a salient contributor to the effective operation of a supply chain partnership. However, there is a paucity of empirical research examining how power among actors influences knowledge acquisition and in turn the performance of supply chain partners. The aim of this research is to address this gap by examining the relationship between power, knowledge acquisition and supply chain performance among the supply chain partners of a focal Chinese steel manufacturer. A structured survey was used to collect the necessary data. Two conceptually independent variables – ‘availability of alternatives’ and ‘restraint in the use of power’ – were used to assess actual and realised power, respectively. Controlling for contingencies, we found that the flow of knowledge increased when supply chain actors had limited alternatives and when the more powerful actor exercised restraint in the use of power. Moreover, we found a positive relationship between knowledge acquisition and supply chain performance. This paper enriches the literature by empirically extending our understanding of how power affects knowledge acquisition and performance

Earnings Management and Long-Run Stock Underperformance of Private Placements

The study investigates whether private placement issuers manipulate their earnings around the time of issuance and the effect of earnings management on the long-run stock performance. We find that managers of U.S. private placement issuers tend to engage in income-increasing earnings management in the year prior to the issuance of private placements. We further speculate that earnings management serves as a likely source of investor over-optimism at the time of private placements. To support this speculation, we find evidence suggesting that the income-increasing accounting accruals made at the time of private placements predict the post-issue long-term stock underperformance. The study contributes to the large body of literature on earnings manipulation around the time of securities issuance

Analysing web search logs to determine session boundaries for user-oriented learning

Incremental learning approaches based on user search activities provide a means of building adaptive information retrieval systems. To develop more effective user-oriented learning techniques for the Web, we need to be able to identify a meaningful session unit from which we can learn. Without this, we run a high risk of grouping together activities that are unrelated or perhaps not from the same user. We are interested in detecting boundaries of sequences between related activities (sessions) that would group the activities for a learning purpose. Session boundaries, in Reuters transaction logs, were detected automatically. The generated boundaries were compared with human judgements. The comparison confirmed that a meaningful session threshold for establishing these session boundaries was confined to a 11-15 minute range

Robust H∞ filtering for networked systems with multiple state delays

This is the post print version of the article. The official published version can be obtained from the link below - Copyright 2007 Taylor & Francis Ltd.In this paper, a new robust H∞ filter design problem is studied for a class of networked systems with multiple state-delays. Two kinds of incomplete measurements, namely, measurements with random delays and measurements with stochastic missing phenomenon, are simultaneously considered. Such incomplete measurements are induced by the limited bandwidth of communication networks, and are modelled as a linear function of a certain set of indicator functions that depend on the same stochastic variable. Attention is focused on the analysis and design problems of a full-order robust H∞ filter such that, for all admissible parameter uncertainties and all possible incomplete measurements, the filtering error dynamics is exponentially mean-square stable and a prescribed H∞ attenuation level is guaranteed. Some recently reported methodologies, such as delay-dependent and parameter-dependent stability analysis approaches, are employed to obtain less conservative results. Sufficient conditions, which are dependent on the occurrence probability of both the random sensor delay and missing measurement, are established for the existence of the desired filters in terms of certain linear matrix inequalities (LMIs). When these LMIs are feasible, the explicit expression of the desired filter can also be characterized. Finally, numerical examples are given to illustrate the effectiveness and applicability of the proposed design method.This work was supported by the National Natural Science Foundation of China under Grant 60574084, the National 863 Project of China under Grant 2006AA04Z428, and the National 973 Program of China under Grant 2002CB312200

Social reference: Aggregating online usage of scientific literature in CiteULike for clustering academic resources

Citation-based methods have been widely studied and employed for clustering academic resources and mapping science. Although effective, these methods suffer from citation delay. In this study, we extend reference and citation analysis to a broader notion from social perspective. We coin the term "social reference" to refer to the references of literatures in social academic web environment. We propose clustering methods using social reference information from CiteULike. We experiment for journal clustering and author clustering using social reference and compare with citation-based methods. Our experiments indicate: first, social reference implies connections among literatures which are as effective as citation in clustering academic resources; second, in practical settings, social reference-based clustering methods are not as effective as citation-based ones due to the sparseness of social reference data, but they can outperform in clustering new resources that have few citation. © 2011 Authors