Abstract
In the present study, we proposed an innovation diffusion model with four-compartments to investigate the interaction and diffusion of two competitive products in a particular region. Herein, Positivity, Boundedness and Basic influence numbers (BINs) are examined. Asymptotic stability analysis is carried out for all feasible steady-states. It is investigated that the adopter free steady-state is stable if BINs are less than one for both the competitive products. Hopf bifurcation analysis is also carried out by taking the adoption experience period of the adopters, i.e., \(\tau _1, \tau _2\) as the bifurcation parameter and obtained the threshold values. Further, when \(\tau _1>0, \tau _2>0\), the interior steady-state \(E^*\) is stable for specific threshold parameters \(\tau _1<\tau _{10^{*}}^{+},\tau _2>\tau _{20^{*}}^{+}\) or \(\tau _1>\tau _{10^*}^{+},\tau _2<\tau _{20^{*}}^{+}\). If both \(\tau _1, \tau _2\) crosses the threshold parameters, i.e., \(\tau _1>\tau _{10^{*}}^{+},\tau _2>\tau _{20^{*}}^{+}\) system perceived oscillating behavior and Hopf bifurcation occurs. Moreover, sensitivity analysis is carried out for the system parameter used in the interior steady-state. Exhaustive numerical simulation supports analytical results. Finally, it exhibited that in light of the impact of media, non-adopter joins the adopter class rapidly as the effect of the media increases in the region.
Similar content being viewed by others
References
Bass, F.M.: A new product growth model for consumer durable. Manag. Sci. 15(5), 215–227 (1969)
Rogers, E.M.: Diffusion of Innovation, 4th edn. Free Press, New York (1995)
Tenneriello, C., Fergola, P., Ma, Z., Wang, W.: Stability of competitive innovation diffusion model. Ric. Mat. 51(2), 185–199 (2002)
Mckeown, M.: The Truth About Innovation. Pearson Financial Times (2008)
Day, G.S.: Analysis for strategic market decisions. MN.West, St. Paul (1986)
Centrone, F., Goia, A., Salinelli, E.: Demographic process in a model of innovation diffusion with dynamic market. Technol. Forcasting Soc. Change 74(3), 27–266 (2007)
Sharma, A., Sharma, A.K., Agnihotri, K.: The dynamics of plankton-nutrient interaction with delay. Appl. Math. Comput. 231, 503–515 (2014)
Shukla, J.B., Kushwah, H., Agarwal, A., Shukla, A.: Modeling the effects of variable external influences and demographic processes on innovation diffusion. Nonlinear Anal. Real World Appl. 13, 186–196 (2012)
Hale, J.K.: Ordinary Differential Equations. Wley, New York (1969)
Wang, W., Fergola, P., Tenneriello, C.: Innovation diffusion model in patch environment. Appl. Math. Comput. 134(1), 51–67 (2003)
Fergola, P., Tenneriello, C., Ma, Z., Petrillo, F.: Delayed innovation diffusion processes with positiveand negative word-of-mouth. Int. J. Differ. Equ. Appl. 1, 131–147 (2000)
Yumei, Y., Wendi, W.: Global stability of an innovation diffusion model for n products. Appl. Math. Lett. 19, 1198–1201 (2006)
Yu, Y., Wang, W.: Stability of innovation diffusion model with nonlinear acceptance. Acta Math. Sci. 27, 645–655 (2007)
Kang, Y.: Deley Differential Equations with Applications in Population Dynamics. Academic Press, London (1993)
Fanelli, V., Maddalena, L.: A time delay model for the diffusion of a new technology. Nonlinear Anal. Real World Appl. 13(2), 643–649 (2012)
Yumei, Y., Wendi, W., Yong, Z.: An innovation diffusion model for three competitive products. Comput. Math. Appl. 46, 1473–1481 (2003)
Dhar, J., Tyagi, M., Sinha, P.: An innovation diffusion model for the survival of a product in a competitive market: basic influence numbers. Int. J. Pure Appl. Math. 89(4), 439–448 (2013)
Singh, H., Dhar, J., Bhatti, H.S.: Bifurcation in disease dynamics with latent period of infection and media awareness. Int. J. Bifurc. Chaos 26(06), 1650097 (2016)
Tuli, R., Dhar, J., Bhatti, H.S., Singh, H.: Dynamical response by the instant buyer and thinker buyer in an innovation diffusion marketing model with media coverage. J. Math. Comput. Sci. 7(6), 1022–1045 (2017)
Kumar, R., Sharma, A.K., Agnihotri, K.: Stability and bifurcation analysis of a delayed innovation diffusion model. Acta Math. Sci. 38(2), 709–732 (2018)
Wang, L., Xu, R., Feng, G.: A stage-structured predator-prey system with the delay. J. Appl. Math. Comput. 33, 267–281 (2010)
Sahu, G.P., Dhar, J.: Analysis of an SVEIS epidemic model with partial temporary immunity and saturation incidence rate. Appl. Math. Model. 36(3), 908–923 (2012)
Driwssche, P.V., Watmough, J.: Reproduction numbers and subthreshold endemic equilibria for compartmental models of disease transmission. Math. Biosci. 180, 29–48 (2002)
Ruan, S.: Absolute stabilty, conditional stability and bifurcation in kolmogrov-type predator-prey systems with discrete delays. Q. App. Math. 59(1), 159–174 (2001)
Singh, H., Dhar, J., Bhatti, H.S.: Dynamics of a prey generalized predator system with disease in prey and gestation delay for predator. Model. Earth Syst. Environ. (2016)
Song, Y., Han, M., Wei, J.: Stability and hopf bifurcation analysis on a simplified bem neural network with delays. Phys. D 200(3), 184–204 (2005)
Lin, X., Wang, H.: Stability analysis of delay differential equations with two discrete delays. Can. Appl. Math. Q. 20(4), 519–533 (2012)
Acknowledgements
I express my warm thanks to I.K.G. Punjab Technical University, Punjab for providing me the facilities for the research being required.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Tuli, R., Dhar, J. & Bhatti, H.S. Sustaining of two competing products under the impact of the media including the experience of adopters. J. Appl. Math. Comput. 60, 343–367 (2019). https://doi.org/10.1007/s12190-018-01217-y
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12190-018-01217-y