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Basic Product Data in E-Commerce: Specifications and Problems of Data Exchange

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  • Maciej Niemir
  • Beata Mrugalska

Abstract

Purpose: This paper summarizes and compares the types and interpretations of the basic attributes necessary to enter product data in selected e-commerce platforms. Design/Methodology/Approach: The research methodology was based on an analysis and selection of a reference group of basic product attributes and identification of appropriate market representatives, platforms and tools commonly used in e-commerce. Furthermore, for each of the selected basic attributes of the product, an analysis was made in terms of the presence, mandatory field, and data input validators. The best practices indicated by the platform developers were also reviewed. Findings: The research results indicate discrepancies in the understanding of the basic attributes of the product. A lack of commonly available, standardized, consistent data describing products for which the manufacturer would take responsibility lead to creating own solutions for the e-commerce market and development of their own meanings of some data. Practical implications: It is necessary to clearly understand e-product data as e-commerce market is relatively young and for which product data has a much greater and often completely different meaning than in a traditional trade. Originality: It provides recommendations for e-commerce platforms for managing e-product core/ basic product data while using a single standard for product master data and common product identifier.

Suggested Citation

  • Maciej Niemir & Beata Mrugalska, 2021. "Basic Product Data in E-Commerce: Specifications and Problems of Data Exchange," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 317-329.
  • Handle: RePEc:ers:journl:v:xxiv:y:2021:i:special2-part3:p:317-329
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    References listed on IDEAS

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    2. Fei Tao & Fangyuan Sui & Ang Liu & Qinglin Qi & Meng Zhang & Boyang Song & Zirong Guo & Stephen C.-Y. Lu & A. Y. C. Nee, 2019. "Digital twin-driven product design framework," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 3935-3953, June.
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    JEL classification:

    • L87 - Industrial Organization - - Industry Studies: Services - - - Postal and Delivery Services

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