Innovation diffusion model with interactions and delays in adoption for two competitive products in two different patches

Tuli, Rishi; Dhar, Joydip; Bhatti, Harbax Singh

doi:10.1007/s11587-019-00435-1

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…Using the time lag, Aggarwal et al (2019) proposed a model to capture the time delay between customer's motivation and its final diffusion of adoption into a social group. Some other researches that have addressed the issue of time delay in product diffusion include Singh et al (2014), Kumar et al (2017) and Tuli et al (2019). Similarly, in the present research, we explore the effect of disruption in the values of the coefficients of innovation "p" and coefficient of imitator "q" of Bass model at different time periods during the green product diffusion time horizon.…”

Section: Simchimentioning

confidence: 92%

Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion

Dev

Shankar

Zacharia

et al. 2021

IJPDLM

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PurposeThe purpose of this paper is to examine (1) how the recovery speed using promotional investment and (2) distributed production using additive manufacturing (AM) improve the resilience of the supply chain to manage any disruptions in the diffusion of green products.Design/methodology/approachThe environmental performance, service level performance and economic performance are the measures of interest. These measures are studied through the integration of inventory and production planning (I&PP) of the reverse logistics system and consumer behavior using Bass (1969) model of diffusion of innovation under the paradigm of Industry 4.0 architecture. The Taguchi experimental design framework was used for the simulation analysis.FindingsThe adoption patterns based on the Bass model in conjunction with recovery speed and production on AM during the disruption period suggest that there exist tradeoff decisions between various combinations of information-sharing and I&PP policies.Practical implicationsThe extensive sensitivity analyses provide real-time support for managerial decisions. Besides the potentials of Industry 4.0 capabilities, the present research suggests paying close attention to the recovery speed in conjunction with the inventory management system.Social implicationsThe integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.Originality/valuePrevious research studies have discussed resilience to manage the ripple effect. However, none of them have addressed the changing scope of resilience to manage the ripple effect caused by the disruption in the diffusion of green products in a reverse logistics setup.

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Section: Simchimentioning

confidence: 92%

Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion

Dev

Shankar

Zacharia

et al. 2021

IJPDLM

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“…In recent years, the bifurcation analysis of different innovation diffusion models has been studied [18,19]. Many models regarding innovation diffusion have been developed and analyzed in recent years [7,11,20,26,27,31].…”

Section: Introductionmentioning

confidence: 99%

An innovation diffusion model with two innovations under two simultaneous effects in a competitive market: co-existence through optimal control

Chugh,

Dhar,

Guha

2024

J. Math. Computer Sci.

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Since mass media plays an important role in influencing the non-adopter population, the adoption rate changes with the media awareness rate. Motivated by this concept, in this paper, a model of three non-intersecting classes of non-adopters, adopters for product-I and adopters for product-II is proposed. Under the influence of media coverage and word-of-mouth, the dynamic behaviour of the system is investigated. The basic influence numbers R 0 1 and R 0 2 associated with the first and second innovations help in performing stability analysis. It is observed from stability analysis that adopter-free equilibrium is conditionally stable. Also, the system has no stable interior equilibrium point. The basic influence numbers determine the sustainability of a particular product in the market. The optimal control theory is used to reduce the frustration rate in both adopter classes. The Hamiltonian function is constructed using the extended optimum control model and is then solved according to Pontryagin's maximum principle to get the cost. Also, coexistence is possible with the implementation of optimal control. Sensitivity analysis has been performed for both the basic influence numbers R 0 1 and R 0 2 . Lastly, numerical experimentations have been executed to assist analytical findings with distinct sets of parameters.

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“…Huang and Zhang studied the diffusion dynamics of two competing products with repeated buying behaviors in heterogeneous consumer social networks [24]. Tuli et al proposed how people treat two products in two different patches and proposed a six-unit innovation diffusion model for two different patches [25]. Fu et al proposed the propagation dynamics of competitive information on Internet social networks and proposed an improved SIR model for two types of competitive information [26].…”

Section: Introductionmentioning

confidence: 99%

Maximizing the Influence of the Innovative Products Diffusion considering Advertisement and Promotion Strategies

Huo

et al. 2021

Discrete Dynamics in Nature and Society

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With the acceleration of product updates and the intensification of product competition, product market strategies become the primary consideration for the enterprises, and the advertisement and promotion strategies are considered the two important strategies implemented by enterprises. This paper considers the enterprises of similar products and substitute, their formation of a competition between traditional products and innovative products, and establishes a mixed node-level information diffusion model to describe the dynamic product diffusion process with complex network theories. We implement advertising strategies for potential buyers who have not obtained product information and implement promotional strategies for those who have obtained product information. In accordance with Pontryagin maximization principle, we seek the best strategy to maximize the impact of innovative products and use numerical calculations to simulate the diffusion state of products. We found that the advertisement strategies play a decisive role in the marketing of innovative products. If product promotion strategies are added, the spread of innovative products will be more effective and more influential.

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Innovation diffusion model with interactions and delays in adoption for two competitive products in two different patches

Cited by 6 publications

References 21 publications

Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion

Supply chain resilience for managing the ripple effect in Industry 4.0 for green product diffusion

An innovation diffusion model with two innovations under two simultaneous effects in a competitive market: co-existence through optimal control

Maximizing the Influence of the Innovative Products Diffusion considering Advertisement and Promotion Strategies

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