Annals of Operations Research

, Volume 264, Issue 1–2, pp 435–458 | Cite as

Accelerating the diffusion of innovations under mixed word of mouth through marketing–operations interaction

  • Fouad El OuardighiEmail author
  • Gustav Feichtinger
  • Gila E. Fruchter
Original Paper


In this paper, an extension of the Bass model is suggested that accounts for the influence of conformance quality on mixed (i.e., positive and negative) word-of-mouth in the diffusion of a new product. A primary goal is to determine how an active operational policy seeking to continuously improve conformance quality affects the optimal leveraging of marketing instruments used to diffuse new products, and the resulting sales and profits. To do so, an optimal tradeoff by a monopolistic firm between advertising effort and price, on the one hand, and conformance quality, on the other hand, is analyzed, along with the implications for word of mouth effectiveness. Our results can be summarized as follows. Price and advertising levels are respectively lower and higher under an operations–marketing policy than under a marketing policy only. As a result, the market potential and the innovation effect are higher under an operations–marketing policy than under a marketing policy only, as is the imitation effect due to conformance quality improvements over time. Also, greater cumulative sales and cumulative profits are obtained. However, higher design quality results in a lower price and greater advertising effort under an operations–marketing policy than under a marketing policy only. Finally, for lower design quality, the two policies result in different patterns (non-monotonic vs. monotonic) for price and advertising yet cumulative sales and profits are of quite similar magnitude.


Diffusion process Word-of-mouth Conformance quality Design quality Price Advertising effort 



The authors acknowledge helpful comments by an anonymous reviewer. They also thank Konstantin Kogan and Peter Kort for constructive suggestions on an early draft presented at the XIIIth Viennese Workshop on Deterministic Optimal Control and Differential Games, Vienna, Austria, May 2015. The usual disclaimer applies. This research was supported by the Centre for Research of ESSEC Business School (France) and the Austrian Science Fund (FWF) under Grant No. P25979-N25. The first author dedicates this paper to the memory of Professor Hervé Mathe, a wonderful colleague and friend.


  1. Ahluwalia, R. (2002). How prevalent is the negativity effect in consumer environments. Journal of Consumer Research, 29(2), 270–279.CrossRefGoogle Scholar
  2. Anderson, E. W. (1998). Customer satisfaction and word of mouth. Journal of Service Research, 1(1), 5–17.CrossRefGoogle Scholar
  3. Armelini, G., & Villanueva, J. (2010). Marketing expenditures and word-of-mouth communication: Complements or substitutes?. Hanover, MA: Now Publishers Inc.Google Scholar
  4. Bass, F. M. (1969). A new product growth model for consumer durables. Management Science, 15(5), 215–227.CrossRefGoogle Scholar
  5. Bass, F. M. (1980). The relationship between diffusion rates, experience curves, and demand elasticities for consumer durable technological innovations. Journal of Business, 53(3), 51–67.CrossRefGoogle Scholar
  6. Chand, S., Moskowitz, H., Novak, A., Rekhi, I., & Sorger, G. (1996). Capacity allocation for dynamic process improvement with quality and demand considerations. Operations Research, 44(6), 964–975.CrossRefGoogle Scholar
  7. Chevalier, J. A., & Mayzlin, D. (2003). The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 44(3), 345–354.Google Scholar
  8. Crosby, P. B. (1979). Quality is free. New York: McGraw-Hill.Google Scholar
  9. De Palma, A., Droesbeke, J.-J., & Lefèvre, C. (1987). Implications of the learning curve for the diffusion of new consumer durables. International Journal of Systems Sciences, 18(6), 997–1005.CrossRefGoogle Scholar
  10. Dockner, E. J., & Jørgensen, S. (1988). Optimal advertising policies for diffusion models of new product innovation in monopolistic situations. Management Science, 34(1), 119–130.CrossRefGoogle Scholar
  11. El Ouardighi, F., Jørgensen, S., & Pasin, F. (2008). A dynamic game model of operations and marketing management in a supply chain. International Game Theory Review, 10(4), 373–397.CrossRefGoogle Scholar
  12. El Ouardighi, F., & Kogan, K. (2013). Dynamic conformance and design quality in a supply chain: An assessment of contracts’ coordination power. Annals of Operations Research, 211(1), 137–166.CrossRefGoogle Scholar
  13. El Ouardighi, F., & Tapiero, C. S. (1998). Quality and the diffusion of innovations. European Journal of Operational Research, 106(1), 31–38.CrossRefGoogle Scholar
  14. Ettlie, J. E. (1995). Product-process development integration in manufacturing. Management Science, 41, 1224–1237.CrossRefGoogle Scholar
  15. Feichtinger, G., Hartl, R. F., & Sethi, S. P. (1994). Dynamic optimal control methods in advertising: Recent developments. Management Science, 40(2), 195–226.CrossRefGoogle Scholar
  16. Fynes, B., & De Búrca, S. (2005). The effects of design quality on quality performance. International Journal of Production Economics, 96(1), 1–14.CrossRefGoogle Scholar
  17. Garvin, D. (1988). Managing quality. New York: Free Press.Google Scholar
  18. Goldenberg, J., Libai, B., Moldovan, S., & Muller, E. (2007). The NPV of bad news. International Journal of Research in Marketing, 24(3), 186–200.CrossRefGoogle Scholar
  19. Grass, D., Caulkins, J.P., Feichtinger, G., Tragler, G., & Behrens, D.A. (2008). Optimal Control of Nonlinear Processes with Applications in Drugs, Corruption, and Terror. Springer.Google Scholar
  20. Hayes, R. H., & Wheelwright, S. C. (1979). Link manufacturing process and product life cycles. Harvard Business Review, 57, 133–140.Google Scholar
  21. Hayes, R. H., & Wheelwright, S. C. (1979). The dynamics of process-product life cycles. Harvard Business Review, 57, 127–136.Google Scholar
  22. Horsky, D., & Simon, L. S. (1983). Advertising and the diffusion of new products. Marketing Science, 2(1), 1–17.CrossRefGoogle Scholar
  23. Huang, J., Leng, M., & Liang, L. (2012). Recent developments in dynamic advertising research. European Journal of Operational Research, 220, 591–609.CrossRefGoogle Scholar
  24. Ittner, C. D., Nagar, V., & Rajan, M. (2001). An empirical examination of dynamic quality-based learning models. Management Science, 47(4), 563–578.CrossRefGoogle Scholar
  25. Jørgensen, S. (1983). Optimal control of a diffusion model of new product acceptance with price-dependent total market potential. Optimal Control Applications and Methods, 4(3), 269–276.CrossRefGoogle Scholar
  26. Jørgensen, S., Kort, P. M., & Zaccour, G. (2006). Advertising an event. Automatica, 42(8), 1349–1355.CrossRefGoogle Scholar
  27. Kalish, S. (1983). Monopolist pricing with dynamic demand and production cost. Marketing Science, 2(2), 135–159.CrossRefGoogle Scholar
  28. Kalish, S. (1985). A new product adoption model with price, advertising and uncertainty. Management Science, 31(12), 1569–1585.CrossRefGoogle Scholar
  29. Luo, X. (2009). Quantifying the long-term impact of negative word of mouth on cash flows and stock prices. Marketing Science, 28(1), 148–165.CrossRefGoogle Scholar
  30. Mahajan, V., Muller, E., & Kerin, R. A. (1984). Introduction strategy for new products with positive and negative word-of-mouth. Management Science, 39(12), 1389–1404.CrossRefGoogle Scholar
  31. Mahajan, V., & Peterson, R. A. (1978). Innovation diffusion in a dynamic potential adopter population. Management Science, 24(15), 1398–1597.CrossRefGoogle Scholar
  32. Mittal, V., Ross, W. T., & Baldasare, P. M. (1998). The asymmetric impact of negative and positive attribute-level performance on overall satisfaction and repurchase intentions. Journal of Marketing, 62(1), 33–47.CrossRefGoogle Scholar
  33. Moldovan, S., & Goldenberg, J. (2004). Cellular automata modeling of resistance to innovation: Effects and solutions. Technological Forecasting and Social Change, 71(5), 425–442.CrossRefGoogle Scholar
  34. Nelson, P. (1970). Information and consumer behavior. Journal of Political Economy, 78(2), 311–329.CrossRefGoogle Scholar
  35. Orbach, Y., & Fruchter, G. (2008). A utility-based dynamic model used to predict abnormalities in diffusion over time. Innovative Marketing, 4(1), 37–45.Google Scholar
  36. Richins, M. (1983). Negative word of mouth by dissatisfied customers: A pilot study. Journal of Marketing, 47(1), 68–78.CrossRefGoogle Scholar
  37. Rust, R. T., Ambler, T., Carpenter, G. S., Kumar, V., & Srivastava, R. K. (2004). Measuring marketing productivity: Current knowledge and future directions. Journal of Marketing, 68(4), 76–89.CrossRefGoogle Scholar
  38. Sethi, S. P., Prasad, A., & He, X. (2008). Optimal advertising and pricing in a new-product adoption model. Journal of Optimization Theory and Applications, 139(2), 351–360.CrossRefGoogle Scholar
  39. Swami, S., & Khairnar, P. J. (2006). Optimal normative policies for marketing of products with limited availability. Annals of Operations Research, 143(1), 107–121.CrossRefGoogle Scholar
  40. Teng, J.-T., & Thompson, G. L. (1984). Oligopoly models for optimal advertising for new product oligopoly models. Marketing Science, 3(2), 148–168.CrossRefGoogle Scholar
  41. Teng, J.-T., & Thompson, G. L. (1996). Optimal strategies for general price–quality decision models of new products with learning production costs. European Journal of Operational Research, 93(3), 476–489.CrossRefGoogle Scholar
  42. Weerahandi, S., & Dalal, S. R. (1992). A choice-based approach to the diffusion of a service: Forecasting fax penetration by market segments. Marketing Science, 11(1), 39–53.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Fouad El Ouardighi
    • 1
    Email author
  • Gustav Feichtinger
    • 2
  • Gila E. Fruchter
    • 3
  1. 1.ESSEC Business SchoolCergy-PontoiseFrance
  2. 2.Vienna University of TechnologyViennaAustria
  3. 3.Bar-Ilan UniversityRamat GanIsrael

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