Data-Driven Analysis of Hero-Product Dependence, Operational Risk, and Sustainable Brand Growth in Maternal and Infant E-Commerce
DOI:
https://doi.org/10.70393/6a6374616d.343335ARK:
https://n2t.net/ark:/40704/JCTAM.v3n4a02Disciplines:
Statistics & Data ScienceSubjects:
Multivariate AnalysisReferences:
12Keywords:
Maternal and Infant E-commerce, Hero-product Strategy, Brand Building, Operational Innovation, Risk Governance, Digital Transformation, Brand SustainabilityAbstract
The rapid growth of platform-based retail and social commerce has enabled maternal and infant e-commerce businesses to achieve market visibility through hero-product strategies. However, reliance on a limited number of high-performing products may increase product concentration and expose businesses to sales volatility, returns, cancellations, and other operational risks. This study develops a conceptual framework linking hero-product dependence, digital operational innovation, risk governance, and sustainable brand building, and provides an exploratory empirical analysis using public transaction data. We extracted a maternal- and infant-related subsample of 23,622 transaction lines and 94 product codes from the UCI Online Retail II dataset and aggregated it into 25 monthly observations. We measured hero-product dependence as the monthly revenue share of the highest-selling product, while product concentration, return and cancellation value rate, and rolling sales volatility were measured using the Herfindahl–Hirschman Index and transaction-based indicators. We used descriptive statistics, Spearman correlation analysis, and quadratic regressions with HC3 robust standard errors. The results show that hero-product revenue share is strongly associated with overall product concentration and negatively associated with monthly purchase sales. However, within the observed hero-product share range of 6.7% to 18.6%, we find no statistically significant nonlinear relationship with return and cancellation risk or sales volatility. These findings suggest that hero-product success does not automatically generate operational risk; such risk may become more pronounced when product concentration is extreme and is not supported by portfolio development and effective risk governance.
References
[1] Du, J. (2025). Innovation of cross border e-commerce supply chain management mechanism under digital background. International Journal of Networking and Virtual Organisations, 32(1/2/3/4), 291–312. https://doi.org/10.1504/IJNVO.2025.145394
[2] Wang, Y., Jia, F., Schoenherr, T., Gong, Y., & Chen, L. (2020). Cross-border e-commerce firms as supply chain integrators: The management of three flows. Industrial Marketing Management, 89, 72–88. https://doi.org/10.1016/j.indmarman.2019.09.004
[3] Zainuddin, S. A., et al. (2023). The study on technology acceptance in baby and mother product business operation. In From Industry 4.0 to Industry 5.0: Mapping the transitions (pp. 517–525). Springer. https://doi.org/10.1007/978-3-031-28314-7_45
[4] Schultz, D. E., & Block, M. P. (2015). Beyond brand loyalty: Brand sustainability. Journal of Marketing Communications, 21(5), 340–355. https://doi.org/10.1080/13527266.2013.821227
[5] Grubor, A., & Milovanov, O. (2017). Brand strategies in the era of sustainability. Interdisciplinary Description of Complex Systems, 15(1), 78–88. https://doi.org/10.7906/indecs.15.1.6
[6] Shou, Y., Zhao, X., Dai, J., & Xu, D. (2021). Matching traceability and supply chain coordination: Achieving operational innovation for superior performance. Transportation Research Part E: Logistics and Transportation Review, 145, Article 102181. https://doi.org/10.1016/j.tre.2020.102181
[7] Chen, D. (2012). Online Retail II [Data set]. UCI Machine Learning Repository. https://doi.org/10.24432/C5CG6D
[8] Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining. Journal of Database Marketing & Customer Strategy Management, 19(3), 197–208. https://doi.org/10.1057/dbm.2012.17
[9] Johnston, M. P. (2014). Secondary data analysis: A method of which the time has come. Qualitative and Quantitative Methods in Libraries, 3(3), 619–626.
[10] Rhoades, S. A. (1993). The Herfindahl-Hirschman Index. Federal Reserve Bulletin, 79(3), 188–189.
[11] Petersen, J. A., & Kumar, V. (2009). Are product returns a necessary evil? Antecedents and consequences. Journal of Marketing, 73(3), 35–51. https://doi.org/10.1509/jmkg.73.3.35
[12] Griffis, S. E., Rao, S., Goldsby, T. J., & Niranjan, T. T. (2012). The customer consequences of returns in online retailing: An empirical analysis. Journal of Operations Management, 30(4), 282–294. https://doi.org/10.1016/j.jom.2012.02.012
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