Towards a Contingency Theory perspective of Quality Management in Enabling Strategic Alignment

The aim of this paper is to explore the role of Quality Management (QM) theory and practice using a contingency theory perspective. The study is grounded in the role of QM in improving strategic alignment within Small and Medium Sized Enterprises (SMEs) using Contingency Theory rather than adopting best practice approaches. An inductive theory building research methodology was used involving multiple case analyses of five SMEs, involving repeat interviews (n=45), focus groups (n=5) and document analysis. From the findings, it was found that Contingency Variables (strategy, culture, lifecycle and customer focus) and their respective typologies were found to interact with QM practices in helping to shape strategic alignment between the SMEs and their environments. This shaping process based on contingency approaches occurred in a manner unique to each SME and their respective environments rather than in an overarching best practice manner.

Evaluating Document Retrieval Methods for Resource Selection in Clustered P2P IR

Resource Selection (or Query Routing) is an important step in P2P IR. Though analogous to document retrieval in the sense of choosing a relevant subset of resources, resource selection methods have evolved independently from those for document retrieval. Among the reasons for such divergence is that document retrieval targets scenarios where underlying resources are semantically homogeneous, whereas peers would manage diverse content. We observe that semantic heterogeneity is mitigated in the clustered 2-tier P2P IR architecture resource selection layer by way of usage of clustering, and posit that this necessitates a re-look at the applicability of document retrieval methods for resource selection within such a framework. This paper empirically benchmarks document retrieval models against the state-of-the-art resource selection models for the problem of resource selection in the clustered P2P IR architecture, using classical IR evaluation metrics. Our benchmarking study illustrates that document retrieval models significantly outperform other methods for the task of resource selection in the clustered P2P IR architecture. This indicates that clustered P2P IR framework can exploit advancements in document retrieval methods to deliver corresponding improvements in resource selection, indicating potential convergence of these fields for the clustered P2P IR architecture.