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Recursive Effects to Study Feature-Based Capabilities in Supply Chain Management

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  • Pietro De Giovanni

    (Department of Business and Management, Luiss University, 00197 Rome, Italy)

Abstract

This paper explores the benefits that firms obtain when investing in feature-based capabilities. We investigate the external pressures when deciding their feature-based strategy. In addition, we analyze the consumers’ customization options and the needs for facilitators to mitigate the negative effects of excessive features. We assess the influence of feature-based capabilities on performance and search for an economically feasible loop that feature-based capabilities might entail. This latter is carried out by investigating the recursive effects in structural equation modeling. Our findings reveal that feature-based capabilities entail an economically feasible loop through competitors and supply chain partners but not also through facilitators and operational performance.

Suggested Citation

  • Pietro De Giovanni, 2020. "Recursive Effects to Study Feature-Based Capabilities in Supply Chain Management," Logistics, MDPI, vol. 4(4), pages 1-17, November.
    • Handle: RePEc:gam:jlogis:v:4:y:2020:i:4:p:28-:d:438743
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    References listed on IDEAS

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    1. Mick, David Glen & Fournier, Susan, 1998. "Paradoxes of Technology: Consumer Cognizance, Emotions, and Coping Strategies," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 25(2), pages 123-143, September.
    2. De Giovanni, Pietro & Zaccour, Georges, 2019. "Optimal quality improvements and pricing strategies with active and passive product returns," Omega, Elsevier, vol. 88(C), pages 248-262.
    3. Genc, Talat S. & De Giovanni, Pietro, 2020. "Closed-loop supply chain games with innovation-led lean programs and sustainability," International Journal of Production Economics, Elsevier, vol. 219(C), pages 440-456.
    4. Kathy A. Paulson Gjerde & Susan A. Slotnick & Matthew J. Sobel, 2002. "New Product Innovation with Multiple Features and Technology Constraints," Management Science, INFORMS, vol. 48(10), pages 1268-1284, October.
    5. Hongyan Shi & Yunchuan Liu & Nicholas C. Petruzzi, 2013. "Consumer Heterogeneity, Product Quality, and Distribution Channels," Management Science, INFORMS, vol. 59(5), pages 1162-1176, May.
    6. Buratto, Alessandra & Cesaretto, Rudy & De Giovanni, Pietro, 2019. "Consignment contracts with cooperative programs and price discount mechanisms in a dynamic supply chain," International Journal of Production Economics, Elsevier, vol. 218(C), pages 72-82.
    7. De Giovanni, Pietro, 2020. "Blockchain and smart contracts in supply chain management: A game theoretic model," International Journal of Production Economics, Elsevier, vol. 228(C).
    8. Sacco, Armando & De Giovanni, Pietro, 2019. "Channel coordination with a manufacturer controlling the price and the effect of competition," Journal of Business Research, Elsevier, vol. 96(C), pages 97-114.
    9. Thomas Johnsen, 2011. "Supply network delegation and intervention strategies during supplier involvement in new product development," Post-Print hal-00771835, HAL.
    10. Preeker, Tim & De Giovanni, Pietro, 2018. "Coordinating innovation projects with high tech suppliers through contracts," Research Policy, Elsevier, vol. 47(6), pages 1161-1172.
    11. Genc, Talat S. & De Giovanni, Pietro, 2018. "Optimal return and rebate mechanism in a closed-loop supply chain game," European Journal of Operational Research, Elsevier, vol. 269(2), pages 661-681.
    12. Li, Dong & Nagurney, Anna, 2015. "A general multitiered supply chain network model of quality competition with suppliers," International Journal of Production Economics, Elsevier, vol. 170(PA), pages 336-356.
    13. Colicev, Anatoli & De Giovanni, Pietro & Vinzi, Vincenzo Esposito, 2016. "An empirical investigation of the antecedents of partnering capability," International Journal of Production Economics, Elsevier, vol. 178(C), pages 144-153.
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