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Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency. / Tsionas, Mike G.; Patel, Pankaj C.
In: International Journal of Production Economics, Vol. 260, 108835, 06.2023.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Tsionas, MG & Patel, PC 2023, 'Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency', International Journal of Production Economics, vol. 260, 108835. https://doi.org/10.1016/j.ijpe.2023.108835

APA

Tsionas, M. G., & Patel, P. C. (2023). Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency. International Journal of Production Economics, 260, Article 108835. https://doi.org/10.1016/j.ijpe.2023.108835

Vancouver

Tsionas MG, Patel PC. Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency. International Journal of Production Economics. 2023 Jun;260:108835. doi: 10.1016/j.ijpe.2023.108835

Author

Tsionas, Mike G. ; Patel, Pankaj C. / Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency. In: International Journal of Production Economics. 2023 ; Vol. 260.

Bibtex

@article{1ba258d6835a4fb299a70de7f584a93e,
title = "Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency",
abstract = "In operations management literature, efficiency is usually measured using parallel shifts of production functions. This practice is based on the assumption that operations technologies are similar across decision-making units. Relaxing this assumption is essential as firms endowed with heterogeneous operational technologies develop distinctive operational resources and capabilities that vary systematically within an industry, resulting in non-parallel shifts of production functions. By relaxing the assumption of parallel production functions, we focus on technological differences as a measure of inefficiency in production and use non-parametric local linear estimates. Our approach based on Bayesian methods and stochastic dominance is novel in that it models for non-parallel production curve slopes that account for unknown frontier technology which is not observed but can be estimated using intra-industry variation in individual firm operational technologies. The proposed approach makes an important contribution to operations management research by relaxing a non-trivial assumption of parallel shifts of production functions in efficiency analysis.",
keywords = "Productivity and competitiveness, Stochastic frontier modelsncy, Technological differences, Technical efficiency",
author = "Tsionas, {Mike G.} and Patel, {Pankaj C.}",
year = "2023",
month = jun,
doi = "10.1016/j.ijpe.2023.108835",
language = "English",
volume = "260",
journal = "International Journal of Production Economics",
issn = "0925-5273",
publisher = "Elsevier Science B.V.",

}

RIS

TY - JOUR

T1 - Accounting for intra-industry technological heterogeneity in the measurement of operations efficiency

AU - Tsionas, Mike G.

AU - Patel, Pankaj C.

PY - 2023/6

Y1 - 2023/6

N2 - In operations management literature, efficiency is usually measured using parallel shifts of production functions. This practice is based on the assumption that operations technologies are similar across decision-making units. Relaxing this assumption is essential as firms endowed with heterogeneous operational technologies develop distinctive operational resources and capabilities that vary systematically within an industry, resulting in non-parallel shifts of production functions. By relaxing the assumption of parallel production functions, we focus on technological differences as a measure of inefficiency in production and use non-parametric local linear estimates. Our approach based on Bayesian methods and stochastic dominance is novel in that it models for non-parallel production curve slopes that account for unknown frontier technology which is not observed but can be estimated using intra-industry variation in individual firm operational technologies. The proposed approach makes an important contribution to operations management research by relaxing a non-trivial assumption of parallel shifts of production functions in efficiency analysis.

AB - In operations management literature, efficiency is usually measured using parallel shifts of production functions. This practice is based on the assumption that operations technologies are similar across decision-making units. Relaxing this assumption is essential as firms endowed with heterogeneous operational technologies develop distinctive operational resources and capabilities that vary systematically within an industry, resulting in non-parallel shifts of production functions. By relaxing the assumption of parallel production functions, we focus on technological differences as a measure of inefficiency in production and use non-parametric local linear estimates. Our approach based on Bayesian methods and stochastic dominance is novel in that it models for non-parallel production curve slopes that account for unknown frontier technology which is not observed but can be estimated using intra-industry variation in individual firm operational technologies. The proposed approach makes an important contribution to operations management research by relaxing a non-trivial assumption of parallel shifts of production functions in efficiency analysis.

KW - Productivity and competitiveness

KW - Stochastic frontier modelsncy

KW - Technological differences

KW - Technical efficiency

U2 - 10.1016/j.ijpe.2023.108835

DO - 10.1016/j.ijpe.2023.108835

M3 - Journal article

VL - 260

JO - International Journal of Production Economics

JF - International Journal of Production Economics

SN - 0925-5273

M1 - 108835

ER -