44,107 research outputs found

    A Conceptual Framework of Reverse Logistics Impact on Firm Performance

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    This study aims to examine the reverse logistics factors that impact upon firm performance. We review reverse logistics factors under three research streams: (a) resource-based view of the firm, including: Firm strategy, Operations management, and Customer loyalty (b) relational theory, including: Supply chain efficiency, Supply chain collaboration, and institutional theory, including: Government support and Cultural alignment. We measured firm performance with 5 measures: profitability, cost, innovativeness, perceived competitive advantage, and perceived customer satisfaction. We discuss implications for research, policy and practice

    Integrating Scale Out and Fault Tolerance in Stream Processing using Operator State Management

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    As users of big data applications expect fresh results, we witness a new breed of stream processing systems (SPS) that are designed to scale to large numbers of cloud-hosted machines. Such systems face new challenges: (i) to benefit from the pay-as-you-go model of cloud computing, they must scale out on demand, acquiring additional virtual machines (VMs) and parallelising operators when the workload increases; (ii) failures are common with deployments on hundreds of VMs - systems must be fault-tolerant with fast recovery times, yet low per-machine overheads. An open question is how to achieve these two goals when stream queries include stateful operators, which must be scaled out and recovered without affecting query results. Our key idea is to expose internal operator state explicitly to the SPS through a set of state management primitives. Based on them, we describe an integrated approach for dynamic scale out and recovery of stateful operators. Externalised operator state is checkpointed periodically by the SPS and backed up to upstream VMs. The SPS identifies individual operator bottlenecks and automatically scales them out by allocating new VMs and partitioning the check-pointed state. At any point, failed operators are recovered by restoring checkpointed state on a new VM and replaying unprocessed tuples. We evaluate this approach with the Linear Road Benchmark on the Amazon EC2 cloud platform and show that it can scale automatically to a load factor of L=350 with 50 VMs, while recovering quickly from failures. Copyright © 2013 ACM

    Finding Streams in Knowledge Graphs to Support Fact Checking

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    The volume and velocity of information that gets generated online limits current journalistic practices to fact-check claims at the same rate. Computational approaches for fact checking may be the key to help mitigate the risks of massive misinformation spread. Such approaches can be designed to not only be scalable and effective at assessing veracity of dubious claims, but also to boost a human fact checker's productivity by surfacing relevant facts and patterns to aid their analysis. To this end, we present a novel, unsupervised network-flow based approach to determine the truthfulness of a statement of fact expressed in the form of a (subject, predicate, object) triple. We view a knowledge graph of background information about real-world entities as a flow network, and knowledge as a fluid, abstract commodity. We show that computational fact checking of such a triple then amounts to finding a "knowledge stream" that emanates from the subject node and flows toward the object node through paths connecting them. Evaluation on a range of real-world and hand-crafted datasets of facts related to entertainment, business, sports, geography and more reveals that this network-flow model can be very effective in discerning true statements from false ones, outperforming existing algorithms on many test cases. Moreover, the model is expressive in its ability to automatically discover several useful path patterns and surface relevant facts that may help a human fact checker corroborate or refute a claim.Comment: Extended version of the paper in proceedings of ICDM 201

    Conceptualisation of intellectual capital in analysts’ narratives: a performative view

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    Purpose: This study tests the performativity of Intellectual Capital (IC) from the perspective of sell-side analysts, a type of actor who consumes and creates IC information and in whose practice IC information plays a significant role. Design/methodology/approach: The empirical component of the study comprises a narrative analysis of the text of a large corpus of sell-side analysts’ initiation coverage reports. We adopt Mouritsen’s (2006) performative and ostensive conceptualisations of IC as our theoretical framework. Findings: We find that the identities and properties of IC elements are variable, dynamic and transformative. The relevance of IC elements in the eyes of analysts is conditional on the context, temporally contingent and bestowed indirectly. IC elements are attributed to firm value both directly, in a linear manner, and indirectly, via various non-linear interrelationships established with other IC elements, tangible capital and financial capital. Research limitations/implications: This study challenges the conventional IC research paradigm and contributes towards a performativity-inspired conceptualisation of IC and a resultant situated model of IC in place of a predictive model. Originality/value: This is the first study to apply a performative lens to study IC identities, roles and relationships from the perspective of a field of practice that is external to the organisation where IC is hosted. Examining IC from analysts’ perspective is important because not only can it provide an alternative perspective of IC, it also enables an understanding of analysts’ field of practice
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