IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v23y2017i2d10.1007_s10588-016-9225-1.html
   My bibliography  Save this article

Integrating accounting and multiplicative calculus: an effective estimation of learning curve

Author

Listed:
  • Hasan Özyapıcı

    (Eastern Mediterranean University)

  • İlhan Dalcı

    (Eastern Mediterranean University)

  • Ali Özyapıcı

    (Cyprus International University)

Abstract

Numerical interpolation methods are essential for the estimation of nonlinear functions and they have a wide range of applications in economics and accounting. In this regard, the idea of using interpolation methods based on multiplicative calculus for suitable accounting problems is self-evident. The purpose of this study, therefore, is to develop a way to better estimate the learning curve, which is an exponentially decreasing function, based on multiplicative Lagrange interpolation. The results of this study show that the proposed multiplicative method of learning curve provides more accurate estimates of labour costs when compared to the conventional methods. This is because the exponential functions are linear in multiplicative calculus. Furthermore, the results reveal that using the proposed method enables cost and managerial accountants to better calculate both cost of unused capacity and product cost in a cumulative production represented by a nonlinear function. The results of this study are also expected to help researchers, practitioners, economists, business managers, and cost and managerial accountants to understand how to construct a multiplicative based learning curve to improve such decisions as pricing, profit planning, capacity management, and budgeting.

Suggested Citation

  • Hasan Özyapıcı & İlhan Dalcı & Ali Özyapıcı, 2017. "Integrating accounting and multiplicative calculus: an effective estimation of learning curve," Computational and Mathematical Organization Theory, Springer, vol. 23(2), pages 258-270, June.
  • Handle: RePEc:spr:comaot:v:23:y:2017:i:2:d:10.1007_s10588-016-9225-1
    DOI: 10.1007/s10588-016-9225-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-016-9225-1
    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/s10588-016-9225-1?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. Jaber, Mohamad Y. & Guiffrida, Alfred L., 2008. "Learning curves for imperfect production processes with reworks and process restoration interruptions," European Journal of Operational Research, Elsevier, vol. 189(1), pages 93-104, August.
    2. Diana Filip & Cyrille Piatecki, 2014. "An overview on the non-newtonian calculus and its potential applications to economics," Working Papers hal-00945788, HAL.
    3. Abdulaziz Jarkas & Malcolm Horner, 2011. "Revisiting the applicability of learning curve theory to formwork labour productivity," Construction Management and Economics, Taylor & Francis Journals, vol. 29(5), pages 483-493.
    4. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
    5. McDonald, John, 1987. "A New Model for Learning Curves, DARM: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 338-338, July.
    6. Womer, N Keith & Patterson, J Wayne, 1983. "Estimation and Testing of Learning Curves," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(4), pages 265-272, October.
    7. McDonald, John, 1987. "A New Model for Learning Curves, DARM," Journal of Business & Economic Statistics, American Statistical Association, vol. 5(3), pages 329-335, July.
    8. Marvin B. Lieberman, 1984. "The Learning Curve and Pricing in the Chemical Processing Industries," RAND Journal of Economics, The RAND Corporation, vol. 15(2), pages 213-228, Summer.
    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. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    2. Harashima, Taiji, 2012. "A Theory of Intelligence and Total Factor Productivity: Value Added Reflects the Fruits of Fluid Intelligence," MPRA Paper 43151, University Library of Munich, Germany.
    3. Nadeau, Marie-Claude & Kar, Ashish & Roth, Richard & Kirchain, Randolph, 2010. "A dynamic process-based cost modeling approach to understand learning effects in manufacturing," International Journal of Production Economics, Elsevier, vol. 128(1), pages 223-234, November.
    4. Harashima, Taiji, 2009. "A Theory of Total Factor Productivity and the Convergence Hypothesis: Workers’ Innovations as an Essential Element," MPRA Paper 15508, University Library of Munich, Germany.
    5. Harashima, Taiji, 2011. "A Model of Total Factor Productivity Built on Hayek’s View of Knowledge: What Really Went Wrong with Socialist Planned Economies?," MPRA Paper 29107, University Library of Munich, Germany.
    6. Harashima, Taiji, 2014. "Division of Work and Fragmented Information: An Explanation for the Diminishing Marginal Product of Labor," MPRA Paper 56301, University Library of Munich, Germany.
    7. Todd D. Gerarden & Richard G. Newell & Robert N. Stavins, 2017. "Assessing the Energy-Efficiency Gap," Journal of Economic Literature, American Economic Association, vol. 55(4), pages 1486-1525, December.
    8. Guido Fioretti, 2007. "A connectionist model of the organizational learning curve," Computational and Mathematical Organization Theory, Springer, vol. 13(1), pages 1-16, March.
    9. Carlos Ocaña Pérez de Tudela, 1993. "Modelos dinámicos de competencia estratégica y cambio técnico: una panorámica," Investigaciones Economicas, Fundación SEPI, vol. 17(1), pages 43-63, January.
    10. Anelí Bongers, 2017. "Learning and forgetting in the jet fighter aircraft industry," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-19, September.
    11. Acemoglu, Daron & Cao, Dan, 2015. "Innovation by entrants and incumbents," Journal of Economic Theory, Elsevier, vol. 157(C), pages 255-294.
    12. Greaker, Mads & Lund Sagen, Eirik, 2008. "Explaining experience curves for new energy technologies: A case study of liquefied natural gas," Energy Economics, Elsevier, vol. 30(6), pages 2899-2911, November.
    13. M. Jaber & Z. Givi, 2015. "Imperfect production process with learning and forgetting effects," Computational Management Science, Springer, vol. 12(1), pages 129-152, January.
    14. Prasanna Tambe & Lorin M. Hitt, 2014. "Job Hopping, Information Technology Spillovers, and Productivity Growth," Management Science, INFORMS, vol. 60(2), pages 338-355, February.
    15. Mallik, Suman & Chhajed, Dilip, 2006. "Optimal temporal product introduction strategies under valuation changes and learning," European Journal of Operational Research, Elsevier, vol. 172(2), pages 430-452, July.
    16. Papineau, Maya, 2006. "An economic perspective on experience curves and dynamic economies in renewable energy technologies," Energy Policy, Elsevier, vol. 34(4), pages 422-432, March.
    17. Karsten Neuhoff, 2008. "Learning by Doing with Constrained Growth Rates: An Application to Energy Technology Policy," The Energy Journal, , vol. 29(2_suppl), pages 165-183, December.
    18. Pillai, Unni, 2015. "Drivers of cost reduction in solar photovoltaics," Energy Economics, Elsevier, vol. 50(C), pages 286-293.
    19. Carolyn D. Egelman & Dennis Epple & Linda Argote & Erica R.H. Fuchs, 2013. "Learning by Doing in a Multi-Product Manufacturing Environment: Product Variety, Customizations, and Overlapping Product Generations," NBER Working Papers 19674, National Bureau of Economic Research, Inc.
    20. Linda Argote & Sunkee Lee & Jisoo Park, 2021. "Organizational Learning Processes and Outcomes: Major Findings and Future Research Directions," Management Science, INFORMS, vol. 67(9), pages 5399-5429, September.

    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:comaot:v:23:y:2017:i:2:d:10.1007_s10588-016-9225-1. 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.