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Licensed Unlicensed Requires Authentication Published by De Gruyter September 28, 2020

Sales – Response Model in Marketing Revisited in the Times of Uncertainty and Global Turmoil

Debasish Roy ORCID logo EMAIL logo

Abstract

The marketing performance models, regardless of their nature and applications, should ultimately lead to creation of cash flows efficiently. This common objective emphasizes on a basic proposition: the output (dependent) variable must be intrinsically correlated to the financial behavior of the firm at the micro level. The four criteria for marketing performance and evaluation are Financial relevance, Actionable, Stable behavior, and Reliable long-term guidance respectively. By using those four criteria as the cornerstone, the Core Sales – Response Model was formulated under the Process perspective (the marketing procedure which helps to generate cash flows along with other antecedents of financial performance). This research paper is aimed at restructuring the fundamental Sales – Response model with the dependent variable Sales and three independent variables, namely, Marketing Support, Firm – controlled factors, and Uncontrolled factors in view of uncertainties related to global turmoil and widespread economic recession into a three – dimensional model by dropping ‘Marketing Support’ to fit the foundation of mathematical chaos theory and try to test its impact in the real world scenario by two ways: first, whether it can accurately define the current nature of functioning of a business firm under chaotic business environment, and second, given the condition of chaos; if the firm fails to prove its stability, what actions should be taken to stabilize its position in the feasible space. In order to serve the purposes, the manufacturing giant Apple, Inc. ® has been considered as the sample firm for the time – series study of 10 years (2009–2018).

JEL Classification: B23; C32; C65; M31

Corresponding author: Debasish Roy, Department of Management, Sikkim University, Gangtok, Sikkim, India, E-mail:

  1. Data Availability Statement: https://fortune.com/fortune500/2020/search/; https://investor.apple.com/investor-relations/default.aspx.

  2. Conflicts of Interests Statement: The author has NO interests with/or involvement in any organization or entity with any financial interest (such as honoraria; educational grants; participation in speakers’ bureaus; membership, employment, consultancies, stock ownership, or other equity interest; and expert testimony or patent-licensing arrangements), or non-financial interest (such as personal or professional relationships, affiliations, knowledge or beliefs) in the subject matter or materials discussed in this manuscript.

References

Ambler, T., and J. H. Roberts. 2008. “Assessing Marketing Performance: Don’t Settle for a Silver Metric.” Journal of Marketing Management 24 (7/8): 733–50. https://doi.org/10.1362/026725708X345498.Search in Google Scholar

Arrow, K. J. 1971. “The Theory of Risk Aversion.” In Essays in the Theory of Risk Bearing, edited by K. J. Arrow, 90–109. Amsterdam, Netherlands: North-Holland.Search in Google Scholar

Berry, S., J. Levinsohn, and A. Pakes. 1995. “Automobile Prices in Market Equilibrium.” Econometrica 63 (4): 841–90. https://doi.org/10.2307/2171802.Search in Google Scholar

Blackmore, D. 1986. “The Mathematical Theory of Chaos.” Computers & Mathematics with Applications 12 (3/4): 1039–45. https://doi.org/10.1016/0898-1221(86)90439-6.Search in Google Scholar

Chakravorty, J. G., and P. R. Ghosh. 2001. Higher Algebra Including Modern Algebra, 16th ed. Calcutta, India: U. N. Dhur & Sons Private Limited.Search in Google Scholar

Das, B. C., and B. N. Mukherjee. 2000. Integral Calculus, 50th ed. Calcutta, India: U. N. Dhur & Sons Private Limited.Search in Google Scholar

Forrester, J. W. 1961. Industrial Dynamics. Cambridge, MA: MIT Press.Search in Google Scholar

Granger, C. W. J., and P. Newbold. 1974. “Spurious Regressions in Econometrics.” Journal of Econometrics 2 (2): 111–20. https://doi.org/10.1016/0304-4076(74)90034-7.Search in Google Scholar

Hanssens, D. M., and M. G. Dekimpe. 2017. “Models for the Financial-Performance Effects of Marketing.” In Handbook of Marketing Decision Models, edited by B. Wierenga, and R. van der Lans, 2nd ed. 117–42. Cham, Switzerland: Springer International Publishing AG.10.1007/978-3-319-56941-3_4Search in Google Scholar

Hanssens, D. M., L. J. Parsons, and R. L. Schultz. 2001. Market Response Models, 2nd ed. Boston, MA: Kluwer Academic Publishers.Search in Google Scholar

Johansson, J. K. 1979. “Advertising and the S-Curve: A New Approach.” Journal of Marketing Research 16 (3): 346–54. https://doi.org/10.2307/3150709.Search in Google Scholar

Joshi, A., and D. M. Hanssens. 2010. “The Direct and Indirect Effects of Advertising Spending on Firm Value.” Journal of Marketing 74 (1): 20–33. https://doi.org/10.1509/jmkg.74.1.20.Search in Google Scholar

Klapper, D., and G. Zenetti. 2012. “Combining Micro and Macro Data to Study Retailer Pricing in the Presence of State Dependence.” In Quantitative Marketing and Marketing Management: Marketing Models and Methods in Theory and Practice, edited by A. Diamantopoulos, W. Fritz, and L. Hildebrandt, 379–400. Weisbaden, Germany: Springer Gabler.10.1007/978-3-8349-3722-3_18Search in Google Scholar

Leeflang, P. S. H. 2017. “Modeling Competitive Responsiveness and Game Theoretic Models.” In Advanced Methods for Modeling Markets, edited by P. S. H. Leeflang, J. E. Wieringa, T. H. A. Bijmolt, and K. H. Pauwels, 265–97. Cham, Switzerland: Springer International Publishing AG.10.1007/978-3-319-53469-5_9Search in Google Scholar

Lorenz, E. N. 1963. “Deterministic Nonperiodic Flow.” Journal of the Atmospheric Sciences 20 (2): 130–41. https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2Search in Google Scholar

Luenberger, D. G. 1998. Investment Science, 228–33. New York, NY: Oxford University Press.Search in Google Scholar

Pratt, J. W. 1964. “Risk Aversion in the Small and in the Large.” Econometrica 32 (1/2): 122–36. https://doi.org/10.2307/1913738.Search in Google Scholar

Quelch, J. A., and G. J. McGovern. 2006. “Boards Must Measure Marketing Effectiveness.” Directors & Boards 30 (3): 53–6.Search in Google Scholar

Rust, R. T., T. Ambler, G. S. Carpenter, V. Kumar, and R. K. Srivastava. 2004. “Measuring Marketing Productivity: Current Knowledge and Future Directions.” Journal of Marketing 68 (4): 76–89. https://doi.org/10.1509/jmkg.68.4.76.42721.Search in Google Scholar

Srivastava, R. K., T. A. Shervani, and L. Fahey. 1998. “Market-Based Assets and Shareholder Value: A Framework for Analysis.” Journal of Marketing 62 (1): 2–18. https://doi.org/10.1177/002224299806200102.Search in Google Scholar

Strogatz, S. H. 1995. Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering. Reading, MA: Perseus Books Publishing, L.L.C.Search in Google Scholar

Villas-Boas, J. M., and Y. Zhao. 2005. “Retailer, Manufacturers, and Individual Customers: Modeling the Supply-Side in the Ketchup Marketplace.” Journal of Marketing Research 42 (1): 83–95. https://doi.org/10.1509/jmkr.42.1.83.56959.Search in Google Scholar

Villas-Boas, S. B. 2007. “Vertical Relationships between Manufacturers and Retailers: Inference with Limited Data.” The Review of Economic Studies 74 (2): 625–52. https://doi.org/10.1111/j.1467-937X.2007.00433.x.Search in Google Scholar

Received: 2020-07-21
Accepted: 2020-09-08
Published Online: 2020-09-28

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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