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Języki publikacji
Abstrakty
Background: Global product identification standards and methods of product data exchange are known and widespread in the traditional market. However, it turns out that the e-commerce market needs data that have not already received much attention, for which no standards have been established in relation to their content. Furthermore, their current quality is often perceived below expectations. This paper discusses the issues of product name and highlights its problems in the context of e-commerce. Attention is also drawn to the source of liability for erroneous data. Methods: The research methodology is based on the analysis of data of products available on the Internet through product catalog services, online stores, and e-marketplaces, mainly in Poland, but addresses a global problem. Three research scenarios were chosen, comparing product names aggregated by GTIN, starting with e-commerce sites and ending with product catalogs working with manufacturers. In addition, a scenario of name-photo compatibility was included. Results: The results show that the product name, which in the real world is an integral part of the product as it appears on the label provided by the manufacturer, in the virtual world is an attribute consciously or not modified by the service provider. It turns out that discrepancies appear already at the source - at the manufacturer's level - publishing different names for the same product when working with data catalogs or publishing on product pages contributing to the so-called snowball effect. Conclusions: On the Internet, products do not have a fixed name that fully describes the product, which causes problems in uniquely identifying the same products in different data sources. This in turn reduces the quality of data aggregation, search, and reliability. This state of affairs is not solely the responsibility of e-commerce marketplace vendors, but of the manufacturers themselves, who do not take care to publicize the unambiguous and permanent name of their products in digital form. Moreover, there are no unambiguous global guidelines for the construction of a full product name. The lack of such a template encourages individual interpretations of how to describe a product.
Wydawca
Czasopismo
Rocznik
Tom
Strony
357--364
Opis fizyczny
Bibliogr. 17 poz., fot., tab., wykr.
Twórcy
autor
- Łukasiewicz - Poznań Institute of Technology, Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland
autor
- Faculty of Engineering Management, Poznan University of Technology, Poznan, Poland
Bibliografia
- 1. Batini, C., Cappiello, C., Francalanci, C., & Maurino, A. (2009). Methodologies for data quality assessment and improvement. ACM Computing Surveys, 41(3), 1-52. https://doi.org/10.1145/1541880.1541883
- 2. Cichy, C., & Rass, S. (2019). An overview of data quality frameworks. IEEE Access, 7, 24634-24648. https://doi.org/10.1109/ACCESS.2019.2899751
- 3. Feltham, G. A. (1968). The value of information. The Accounting Review, 684-696.
- 4. Guoling, L., & Qinyun, W. (2008). Research on e-business model of distance education. 400-403. https://doi.org/10.1109/CSSE.2008.145
- 5. Haug, A., Zachariassen, F., & Van Liempd, D. (2011). The costs of poor data quality. Journal of Industrial Engineering and Management, 168-193. http://dx.doi.org/10.3926/jiem.2011.v4n2.p168-193
- 6. Kawa, A., & Pierański, B. (2021). Green logistics in E-commerce. Logforum, 17(2), 1. https://doi.org/10.17270/J.LOG.2021.588
- 7. Lone, S., Harboul, N., & Weltevreden, J. (2021). 2021 European E-commerce Report.
- 8. Marsh, R. (2005). Drowning in dirty data? It’s time to sink or swim: A four-stage methodology for total data quality management. Journal of Database Marketing & Customer Strategy Management, 12, 105–112. https://doi.org/10.1057/palgrave.dbm.3240247
- 9. Niemir, M., & Mrugalska, B. (2021). Basic Product Data in E-Commerce: Specifications and Problems of Data Exchange. European Research Studies Journal, XXIV(Special Issue 5), 317–329. https://doi.org/10.35808/ersj/2735
- 10. Niemir, M., & Mrugalska, B. (2022). Product Data Quality in e-Commerce: Key Success Factors and Challenges. 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). https://doi.org/10.54941/ahfe1001626
- 11. Qalati, S. A., Vela, E. G., Li, W., Dakhan, S. A., Hong Thuy, T. T., & Merani, S. H. (2021). Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Business & Management, 8. https://doi.org/10.1080/23311975.2020.1869363
- 12. Reeves, C., & Bednar, D. (1994). Defining quality: Alternatives and implications. Academy of Management Review, 419–445. https://doi.org/10.2307/258934
- 13. Wand, Y., & Wang, R. Y. (1996). Anchoring data quality dimensions in ontological foundations. Communications of the ACM, 86–95. https://doi.org/10.1145/240455.240479
- 14. Wang, R. Y., & Strong, D. M. (1996). Beyond Accuracy: What Data Quality Means to Data Consumers. J. Manage. Inf. Syst., 12(4), 5–33. https://doi.org/10.1080/07421222.1996.11518099
- 15. Zhou, C. H., Chen, B., Gao, Y., Zhang, C., & Guo, Z. J. (2011). A technique of filtering dirty data based on temporal-spatial correlation in wireless sensor network. 511–516. https://doi.org/10.1016/j.proenv.2011.09.083
- 16. Zmud, R. (1978). An empirical investigation of the dimensionality of the concept of information. Decision Sciences, 187–195. https://doi.org/10.1111/j.1540-5915.1978.tb01378.x
- 17. Żuchowski, W. (2022). The smart warehouse trend: Actual level of technology availability. Logforum, 18(2), 7. https://doi.org/10.17270/J.LOG.2022.702
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3323095c-ecbf-4dd4-92e6-8a9448028838