An empirical assessment of a nomological network of organizational design constructs: From culture to structure to pull production to performance

Xenophon A. Koufteros, Abraham Y. Nahm, Edwin Tai Chiu Cheng, Kee Hung Lai

Research output: Journal article publicationJournal articleAcademic researchpeer-review

31 Citations (Scopus)


We empirically examine the relationships amongst a set of organizational culture and structure variables, and how they are related to the practice of pull production, and firm performance. Drawing on literature from organizational theory and sociology, we hypothesize that customer orientation, as a manifestation of underlying assumptions in the organization, affects espoused values in regard to beliefs on management control, working with others, and making decisions that are global. These espoused values will influence organizational structure with respect to locus of decision making, number of layers in the hierarchy, level of horizontal integration, and nature of formalization in the organization. The level of communication, the pull production practice, and ultimately the performance of the organization are impacted by the organization structure of the firm. We test these hypotheses with data from 224 manufacturing executives based in the US The validity and reliability of the various constructs in the nomological network were supported, and the relationships between the constructs were tested using structural equation modeling. The results of the hypotheses testing are discussed, and the academic and management implications of the findings are provided.
Original languageEnglish
Pages (from-to)468-492
Number of pages25
JournalInternational Journal of Production Economics
Issue number2
Publication statusPublished - 1 Apr 2007


  • Organizational culture
  • Organizational structure
  • Structural equation models
  • Survey research/design
  • Time-based manufacturing

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Economics and Econometrics
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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