John N. Pearson
Arizona State University
26 Papers
264 Citations
John N. Pearson is an academic researcher from Arizona State University. The author has contributed to research in topics: Purchasing & Strategic planning. The author has an hindex of 16, co-authored 26 publications.
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Papers
Strategically managed buyer–supplier relationships and performance outcomes
Amelia S. Carr,John N. Pearson +1 more
TL;DR: In this paper, a structural model of strategic purchasing and its influence on supplier evaluation systems, buyer-supplier relationships, and firm's financial performance is presented, and the results of the data analysis provide support for each of the five hypotheses above.
1.1K
Jit Manufacturing: a Survey of Implementations in Small and Large U.S. Manufacturers
TL;DR: In this article, the authors investigated JIT implementation differences between small and large U.S. manufacturers and found that the frequencies of the 10 JIT management practices implemented differ between the two groups of manufacturer size and an association exists between the JIT practices implemented and manufacturer size.
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Planning and Financial Performance of Small, Mature Firms
TL;DR: The study overcomes several methodological shortcomings of prior research on strategic planning and firm performance and identifies statistically significant differences between the financial performance data of firms that employ structured, strategic plans and those that do not.
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The impact of purchasing and supplier involvement on strategic purchasing and its impact on firm’s performance
Amelia S. Carr,John N. Pearson +1 more
TL;DR: In this paper, the authors developed hypotheses concerning purchasing/supplier involvement, strategic purchasing and firm's financial performance using the resource-base view of the firm, and empirically tested these hypotheses using structural equation modeling.
395
Just Modeling Through: a Rough Guide to Modeling
TL;DR: Six principles of modeling are discussed here cover simplicity versus complexity; model development as a gradual, almost piecemeal process; dividing larger models into smaller components; using analogies; proper uses of data; and finally the way in which the modeling process can seem chaotic.
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