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An empirical study of RFID productivity in the U.S. retail supply chain

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  • Shin, Seungjae
  • Eksioglu, Burak

Abstract

Radio Frequency Identification, or RFID, technology has received great attention when, beginning in 2003, Walmart announced its plan to use the technology. RFID׳s automatic scanning ability can reduce product scanning error rates and product scanning manpower, which can lead to increased labor productivity. This paper evaluates labor productivity of RFID adopted retailers. This paper uses data from financial statements and explains the association between RFID technology and adopted retailer׳s labor productivity. The regression analysis using the Cobb–Douglas production function shows that RFID retailers have a higher labor to gross income elasticity than their non-RFID counterpart, indicating that RFID retailers have higher labor productivity.

Suggested Citation

  • Shin, Seungjae & Eksioglu, Burak, 2015. "An empirical study of RFID productivity in the U.S. retail supply chain," International Journal of Production Economics, Elsevier, vol. 163(C), pages 89-96.
    • Handle: RePEc:eee:proeco:v:163:y:2015:i:c:p:89-96
    DOI: 10.1016/j.ijpe.2015.02.016
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    Cited by:

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    2. Ayodeji Emmanuel Oke & Ahmed Farouk Kineber & Mohamed Elseknidy & Fakunle Samuel Kayode, 2023. "Radio frequency identification implementation model for sustainable development: A structural equation modeling approach," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(3), pages 1824-1844, June.
    3. Chaoxun Ding & Ruidan Zhang, 2021. "The Measurement and Influencing Factors of Total Factor Productivity in the Chinese Rural Distribution Industry," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
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    5. Li-Hao Zhang & Shan-Shan Wang, 2022. "Strategic analysis of RFID adoption sequences in a supply chain with Cournot competition: effects of ordering-timing strategies," Annals of Operations Research, Springer, vol. 315(2), pages 2169-2208, August.
    6. Patalinghug Jason C., 2019. "Estimation of a Production Function for Philippine Retail Stores," Economics, Sciendo, vol. 7(1), pages 69-79, June.
    7. Lui, Ariel K.H. & Lo, Chris K.Y. & Ngai, Eric W.T. & Yeung, Andy C.L., 2023. "A tough pill to swallow? The lessons learned from mandatory RFID adoption," International Journal of Production Economics, Elsevier, vol. 258(C).
    8. Emre Berk & Onurcan Ayas & M. Ali Ülkü, 2023. "Optimizing Process-Improvement Efforts for Supply Chain Operations under Disruptions: New Structural Results," Sustainability, MDPI, vol. 15(17), pages 1-23, August.
    9. Bai, Qingguo & Gong, Yeming (Yale) & Jin, Mingzhou & Xu, Xianhao, 2019. "Effects of carbon emission reduction on supply chain coordination with vendor-managed deteriorating product inventory," International Journal of Production Economics, Elsevier, vol. 208(C), pages 83-99.
    10. Alessandro Scuderi & Agata Matarazzo & Giovanni La Via & Mariarita Cammarata & Giuseppe Timpanaro, 2022. "Multi-Criteria Analysis of Investment Choices Following Flood Damage: The Case Study of Sustainable Citrus Farming in Sicily," Agriculture, MDPI, vol. 12(8), pages 1-16, July.
    11. Alqahtani, Ammar Y. & Gupta, Surendra M. & Nakashima, Kenichi, 2019. "Warranty and maintenance analysis of sensor embedded products using internet of things in industry 4.0," International Journal of Production Economics, Elsevier, vol. 208(C), pages 483-499.
    12. Cannella, Salvatore & Dominguez, Roberto & Framinan, Jose M., 2017. "Inventory record inaccuracy – The impact of structural complexity and lead time variability," Omega, Elsevier, vol. 68(C), pages 123-138.

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