The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Development and validation of a measurement instrument for studying supply chain management practices
Abstract
It is widely argued that competition is no longer between organizations, but among supply chains. Effective supply chain management (SCM) has become a potentially valuable way of securing competitive advantage and improving organizational performance. This research conceptualizes, develops, and validates six dimensions of SCM practices (strategic supplier partnership, customer relationship, information sharing, information quality, internal lean practices, and postponement). Data for the study were collected from 196 organizations and the measurement scales were tested and validated using structural equation modeling. It is hoped that this study will provide a parsimonious measurement instrument to assess the performance of the overall supply chain.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Coefficient Alpha and the Internal Structure of Tests
Cronbach, L. J. (1951) · Psychometrika · doi:10.1007/bf02310555 · Cited by 43,931
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
Significance tests and goodness of fit in the analysis of covariance structures.
Bentler, P. M., & Bonett, D. G. (1980) · Psychological Bulletin · doi:10.1037//0033-2909.88.3.588 · Cited by 19,023
Convergent and discriminant validation by the multitrait-multimethod matrix.
Campbell, D. T., & Fiske, D. W. (1959) · Psychological Bulletin · doi:10.1037/h0046016 · Cited by 17,116
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549
Estimating Nonresponse Bias in Mail Surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.2307/3150783 · Cited by 12,222
Estimating nonresponse bias in mail surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.1177/002224377701400320 · Cited by 10,390
Moore, G. C., & Benbasat, I. (1991) · Information Systems Research · doi:10.1287/isre.2.3.192 · Cited by 9,150
Representing and Testing Organizational Theories: A Holistic Construal
Bagozzi, R. P., & Phillips, L. W. (1982) · Administrative Science Quarterly · doi:10.2307/2392322 · Cited by 2,064
Cited by
Defining and developing measures of lean production
Shah, R., & Ward, P. T. (2007) · Journal of Operations Management · doi:10.1016/j.jom.2007.01.019 · Cited by 2,399
Successful lean implementation: Organizational culture and soft lean practices
Bortolotti, T., Boscari, S., & Danese, P. (2015) · International Journal of Production Economics · doi:10.1016/j.ijpe.2014.10.013 · Cited by 678
Operational capabilities: the secret ingredient
Wu, S. J., Melnyk, S. A., & Flynn, B. B. (2010) · Decision Sciences · doi:10.1111/j.1540-5915.2010.00294.x · Cited by 263
Lean practices and their effect on performance: a literature review
Negrão, L. L. L., Godinho Filho, M., & Marodin, G. (2016) · Production Planning & Control · doi:10.1080/09537287.2016.1231853 · Cited by 243
Lean manufacturing performance in Indian manufacturing plants
Ghosh, M. (2012) · Journal of Manufacturing Technology Management · doi:10.1108/17410381311287517 · Cited by 201
Metadata
- Authors
- Li, S., Rao, S. S., Ragu‐Nathan, T. S., & Ragu‐Nathan, B.
- Venue
- Journal of Operations Management
- Publisher
- Wiley
- Published
- 2005-03-13
- Volume
- 23
- Issue
- 6
- Pages
- 618-641
- ISSN
- 0272-6963 (ISSN-L)
- 1873-1317
- OpenAlex
- W2052489129
- Cited by
- 972
- References
- 11
- FWCI
- 29.57
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Development and validation of a measurement instrument for studying supply chain management practices. https://app.cochat.ai/papers/10.1016/j.jom.2005.01.002
- The paper
- Li, S., Rao, S. S., Ragu‐Nathan, T. S., & Ragu‐Nathan, B. (2005). Development and validation of a measurement instrument for studying supply chain management practices. Journal of Operations Management. https://doi.org/10.1016/j.jom.2005.01.002
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.