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A methodology for the evaluation of high response time on E-commerce users and sales

Author

Listed:
  • Nicolas Poggi

    (Technical University of Catalonia (UPC))

  • David Carrera

    (Technical University of Catalonia (UPC)
    Barcelona Supercomputing Center)

  • Ricard Gavaldà

    (Technical University of Catalonia (UPC))

  • Eduard Ayguadé

    (Technical University of Catalonia (UPC)
    Barcelona Supercomputing Center)

  • Jordi Torres

    (Technical University of Catalonia (UPC)
    Barcelona Supercomputing Center)

Abstract

The widespread adoption of high speed Internet access and it’s usage for everyday tasks are causing profound changes in users’ expectations in terms of Web site performance and reliability. At the same time, server management is living a period of changes with the emergence of the cloud computing paradigm that enables scaling server infrastructures within minutes. To help set performance objectives for maximizing user satisfaction and sales, while minimizing the number of servers and their cost, we present a methodology to determine how user sales are affected as response time increases. We begin with the characterization of more than 6 months of Web performance measurements, followed by the study of how the fraction of buyers in the workload is higher at peak traffic times, to then build a model of sales through a learning process using a 5-year sales dataset. Finally, we present our evaluation of high response time on users for popular applications found in the Web.

Suggested Citation

  • Nicolas Poggi & David Carrera & Ricard Gavaldà & Eduard Ayguadé & Jordi Torres, 2014. "A methodology for the evaluation of high response time on E-commerce users and sales," Information Systems Frontiers, Springer, vol. 16(5), pages 867-885, November.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:5:d:10.1007_s10796-012-9387-4
    DOI: 10.1007/s10796-012-9387-4
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    Citations

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    Cited by:

    1. Nils Boysen & Stefan Fedtke & Stefan Schwerdfeger, 2021. "Last-mile delivery concepts: a survey from an operational research perspective," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(1), pages 1-58, March.
    2. Adah-Kole Emmanuel Onjewu & Sundas Hussain & Mohamed Yacine Haddoud, 2022. "The Interplay of E-commerce, Resilience and Exports in the Context of COVID-19," Information Systems Frontiers, Springer, vol. 24(4), pages 1209-1221, August.
    3. Saurabh Pratap & Sunil Kumar Jauhar & Yash Daultani & Sanjoy Kumar Paul, 2023. "Benchmarking sustainable E‐commerce enterprises based on evolving customer expectations amidst COVID‐19 pandemic," Business Strategy and the Environment, Wiley Blackwell, vol. 32(1), pages 736-752, January.
    4. Ehsan Mousavi Khaneghah & Nosratollah Shadnoush & Amin Salem, 2017. "Artemis time: A mathematical model to calculate maximum acceptable waiting time in B2C e-commerce," Cogent Business & Management, Taylor & Francis Journals, vol. 4(1), pages 1405509-140, January.
    5. Fleckenstein, David & Klein, Robert & Steinhardt, Claudius, 2023. "Recent advances in integrating demand management and vehicle routing: A methodological review," European Journal of Operational Research, Elsevier, vol. 306(2), pages 499-518.
    6. Boysen, Nils & de Koster, René & Füßler, David, 2021. "The forgotten sons: Warehousing systems for brick-and-mortar retail chains," European Journal of Operational Research, Elsevier, vol. 288(2), pages 361-381.

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