IDEAS home Printed from https://ideas.repec.org/a/spr/jbecon/v92y2022i6d10.1007_s11573-021-01077-2.html
   My bibliography  Save this article

Resource allocation procedures for unknown sales response functions with additive disturbances

Author

Listed:
  • Daniel Gahler

    (University of Regensburg)

  • Harald Hruschka

    (University of Regensburg)

Abstract

We develop a modified exploration–exploitation algorithm which allocates a fixed resource (e.g., a fixed budget) to several units with the objective to attain maximum sales. This algorithm does not require knowledge of the form and the parameters of sales response functions and is able to cope with additive random disturbances. Note that additive random disturbances, as a rule, are a component of sales response functions estimated by econometric methods. We compare the developed algorithm to three rules of thumb which in practice are often used to solve this allocation problem. The comparison is based on a Monte Carlo simulation for 384 experimental constellations, which are obtained from four function types, four procedures (including our algorithm), similar/varied elasticities, similar/varied saturations, high/low budgets, and three disturbance levels. A statistical analysis of the simulation results shows that across a multi-period planning horizon the algorithm performs better than the rules of thumb considered with respect to two sales-related criteria.

Suggested Citation

  • Daniel Gahler & Harald Hruschka, 2022. "Resource allocation procedures for unknown sales response functions with additive disturbances," Journal of Business Economics, Springer, vol. 92(6), pages 997-1034, August.
    • Handle: RePEc:spr:jbecon:v:92:y:2022:i:6:d:10.1007_s11573-021-01077-2
    DOI: 10.1007/s11573-021-01077-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11573-021-01077-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11573-021-01077-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Prabhakant Sinha & Andris A. Zoltners, 2001. "Sales-Force Decision Models: Insights from 25 Years of Implementation," Interfaces, INFORMS, vol. 31(3_supplem), pages 8-44, June.
    2. James G. March, 1991. "Exploration and Exploitation in Organizational Learning," Organization Science, INFORMS, vol. 2(1), pages 71-87, February.
    3. Marc Fischer & Sönke Albers & Nils Wagner & Monika Frie, 2011. "Practice Prize Winner --Dynamic Marketing Budget Allocation Across Countries, Products, and Marketing Activities," Marketing Science, INFORMS, vol. 30(4), pages 568-585, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gahler, Daniel & Hruschka, Harald, 2016. "Resource Allocation Heuristics for Unknown Sales Response Functions with Additive Disturbances," University of Regensburg Working Papers in Business, Economics and Management Information Systems 488, University of Regensburg, Department of Economics.
    2. Arman Avadikyan & Gilles Lambert & Christophe Lerch, 2016. "A Multi-Level Perspective on Ambidexterity: The Case of a Synchrotron Research Facility," Working Papers of BETA 2016-44, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    3. Henri A. Schildt & Markku V.J. Maula & Thomas Keil, 2005. "Explorative and Exploitative Learning from External Corporate Ventures," Entrepreneurship Theory and Practice, , vol. 29(4), pages 493-515, July.
    4. Giuliani, Elisa & Martinelli, Arianna & Rabellotti, Roberta, 2016. "Is Co-Invention Expediting Technological Catch Up? A Study of Collaboration between Emerging Country Firms and EU Inventors," World Development, Elsevier, vol. 77(C), pages 192-205.
    5. Insoo Cho & Peter F. Orazem, 2021. "How endogenous risk preferences and sample selection affect analysis of firm survival," Small Business Economics, Springer, vol. 56(4), pages 1309-1332, April.
    6. Tomasz Helbin & Amy Van Looy, 2021. "Is Business Process Management (BPM) Ready for Ambidexterity? Conceptualization, Implementation Guidelines and Research Agenda," Sustainability, MDPI, vol. 13(4), pages 1-25, February.
    7. Son K. Lam & Thomas E. DeCarlo & Ashish Sharma, 2019. "Salesperson ambidexterity in customer engagement: do customer base characteristics matter?," Journal of the Academy of Marketing Science, Springer, vol. 47(4), pages 659-680, July.
    8. Jonathan H. Reed, 2022. "Operational and strategic change during temporary turbulence: evidence from the COVID-19 pandemic," Operations Management Research, Springer, vol. 15(1), pages 589-608, June.
    9. Alan Hevner & Isabelle Comyn-Wattiau & Jacky Akoka & Nicolas Prat, 2018. "A pragmatic approach for identifying and managing design science research goals and evaluation criteria," Post-Print hal-02283783, HAL.
    10. Felipe A. Csaszar & Nicolaj Siggelkow, 2010. "How Much to Copy? Determinants of Effective Imitation Breadth," Organization Science, INFORMS, vol. 21(3), pages 661-676, June.
    11. Bruneel, Johan & Clarysse, Bart & Bobelyn, Annelies & Wright, Mike, 2020. "Liquidity events and VC-backed academic spin-offs: The role of search alliances," Research Policy, Elsevier, vol. 49(10).
    12. Sylvie Héroux & Mélanie Roussy, 2020. "Three cases of compliance with governance regulation: an organizational learning perspective," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 24(2), pages 449-479, June.
    13. Anne Corcos & Yorgos Rizopoulos, 2011. "Is prosocial behavior egocentric? The “invisible hand” of emotions," Post-Print halshs-01968213, HAL.
    14. Freeman, Steven F., 1997. "Good decisions : reconciling human rationality, evolution, and ethics," Working papers WP 3962-97., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    15. Zhang, Feng & Jiang, Guohua & Cantwell, John A., 2015. "Subsidiary exploration and the innovative performance of large multinational corporations," International Business Review, Elsevier, vol. 24(2), pages 224-234.
    16. Avimanyu Datta, 2016. "Antecedents To Radical Innovations: A Longitudinal Look At Firms In The Information Technology Industry By Aggregation Of Patents," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(07), pages 1-31, October.
    17. Liu, Zhiqiang & Yan, Miao & Fan, Youqing & Chen, Liling, 2021. "Ascribed or achieved? The role of birth order on innovative behaviour in the workplace," Journal of Business Research, Elsevier, vol. 134(C), pages 480-492.
    18. Nicolai J. Foss, 1996. "Firms, Incomplete Contracts and Organizational Learning," DRUID Working Papers 96-2, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    19. Boeker, Warren & Howard, Michael D. & Basu, Sandip & Sahaym, Arvin, 2021. "Interpersonal relationships, digital technologies, and innovation in entrepreneurial ventures," Journal of Business Research, Elsevier, vol. 125(C), pages 495-507.
    20. Keegan, A. & Turner, J.R., 2000. "Quantity versus Quality in Project Based Learning Practices," ERIM Report Series Research in Management ERS-2000-55-ORG, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.

    More about this item

    Keywords

    Marketing resource allocation; Exploration–exploitation algorithm; Monte Carlo simulation; Optimization;
    All these keywords.

    JEL classification:

    • M30 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:jbecon:v:92:y:2022:i:6:d:10.1007_s11573-021-01077-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.