Dynamic optimal control model for profit maximization of software product under the influence of promotional effort

Kapur, P. K.; Pham, Hoang; Kumar, Vijay; Anand, Adarsh

doi:10.1016/j.hitech.2012.06.002

Cited by 16 publications

(5 citation statements)

References 22 publications

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“…the buying behavior of all individuals is the same (Aggrawal, 2014). Many researchers, including Midgley (1976), Dodson and Muller (1978), Mahajan and Muller (1982), Sharif and Ramanathan (1982), Mahajan et al (1984), Kalish (1985), Bayus (1987), Jain et al (1991), Parker (1992Parker ( , 1993, Ho et al (2002), Bemmaor and Lee (2002), Bruyn and Lilien (2008), Kapur et al (2012) and Aggrawal (2014), have extended the basic diffusion model and relaxed its assumption and have also included parameters that facilitate the heterogeneity in models. Midgley (1976) presented a mathematical theory of the adoption in which he considered that a new product is derived from extant behavioral knowledge and tested against first purchase data on low-risk, non-durable products.…”

Section: Literature Reviewmentioning

confidence: 99%

Unified approach for modeling innovation adoption and optimal model selection for the diffusion process

Anand

Agarwal

Aggrawal

et al. 2016

Journal of Advances in Management Research

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Purpose – Mathematical modeling of innovation diffusion is a constantly evolving field within marketing science. The diffusion process explains the dispersion of an innovation among potential buyers. Prior research on innovation diffusion has been based on modeling varied aspects of real life situations in marketing. One such aspect is studying the adoption process depending on the awareness and motivation level among the customers. Awareness is having knowledge of an innovation, whereas motivation is about the perception of an individual. In line with these aspects, the purpose of this paper is to propose a unified modeling framework for the adoption process based on the awareness and motivation about the product. Design/methodology/approach – When the market is well informed about the product, there are some people who are motivated and some, who have adopted the product earlier and shall now influence others in their buying behavior. It is very much similar to queuing system in which some units are waiting in a queue for the service, service for some units are being processed and some units have already been served. This analogous behavior between two approaches has motivated the use of infinite server queuing theory in modeling adoption of the product. Thereafter, the authors have proposed a unification scheme to model different market scenarios. Findings – From analyzing the values of comparison criteria, it was not clear that which among them is performing best. Thus there was a need for an approach which can judiciously find the optimal model. For this very purpose the authors applied distance-based approach which was capable of computing the optimal model based on the distance of attribute value from the optimal. The analysis performed on two real life sales data sets depict that model in which awareness is following logistic pattern and motivation and adoption are following a constant pattern is ranked one. Research limitations/implications – The idea has been validated on product. It would be interesting to know how the methodology works on service. Originality/value – The modeling framework discussed in this paper can be helpful to know from the available set of alternative, which among them is performing better in capturing the spread of the product in the market. The proposed framework offer some managerial guidance by highlighting the unusual aspects of diffusion process and also present an approach to judge the best among a set of different models.

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Section: Literature Reviewmentioning

confidence: 99%

Unified approach for modeling innovation adoption and optimal model selection for the diffusion process

Anand

Agarwal

Aggrawal

et al. 2016

Journal of Advances in Management Research

Self Cite

View full text Add to dashboard Cite

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“…The value R 2 as obtained here for all three data sets is significantly close to one, depicting the close approximation to real data points and perfect fit to the data under consideration. On assessing Table 5, 6, and 7, it can be inferred that for DS I, the Proposed Model I and Bass Kapur et al (2012), Ackerberg (2003), Dodson and Muller (1978). Model (Bass 1969) has performed equally well, whereas the same can be visualized for DS II and DS III.…”

Section: Numerical Illustrationmentioning

confidence: 92%

“…De Bruyn and Lilien (2008) presented multi-stage dynamic models as components to consider and refine the comprehension of spontaneous, electronic references. In 2012, Kapur et al (2012) built up a model based on the sales of the software to uncover the benefits and discover advertising strategies.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of negative WOM on diffusion process

Aggrawal

Agarwal

Mittal

et al. 2021

Int J Syst Assur Eng Manag

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The diffusion process has been considered as the propagation of messages associated with new ideas that lead to innovations; be it products, processes, or technology. With the anticipation of the change in receptor behavior, this diffusion process tends to bring out the adoption of the innovation. Most of the literature on innovation diffusion modeling is based on market growth however, very less work is available that focuses on how a new product penetrates a market under the effect of attrition on its growth. The intended purpose here is to study the dynamic behind the growth of an innovative product. The impact that past adopters of an innovation exercise on potential adopters by convincing them to imitate them in their choice to accept/reject the advancement (communication impact, impersonation impact), assists in explaining the acceleration of the diffusion process. With this objective, we have formulated and investigated an innovation diffusion model to include both adoption and disadoption behavior. The proposed framework has been validated and empirically analyzed on three real sales data sets.

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“…to solve the dynamic optimization problems. In order to solve the above control problem, we start with the Hamiltonian (Sethi & Thompson, 2005;Kapur et al, 2012;2013) which can be written as…”

Section: Optimal Solutionmentioning

confidence: 99%

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

Kumar¹,

Sahni²

2016

10.5267/j.dsl

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Fault detection process (FDP) and Fault correction process (FCP) are important phases of software development life cycle (SDLC). It is essential for software to undergo a testing phase, during which faults are detected and corrected. The main goal of this article is to allocate the testing resources in an optimal manner to minimize the cost during testing phase using FDP and FCP under dynamic environment. In this paper, we first assume there is a time lag between fault detection and fault correction. Thus, removal of a fault is performed after a fault is detected. In addition, detection process and correction process are taken to be independent simultaneous activities with different budgetary constraints. A structured optimal policy based on optimal control theory is proposed for software managers to optimize the allocation of the limited resources with the reliability criteria. Furthermore, release policy for the proposed model is also discussed. Numerical example is given in support of the theoretical results.Growing Science Ltd. All rights reserved. 6

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Dynamic optimal control model for profit maximization of software product under the influence of promotional effort

Cited by 16 publications

References 22 publications

Unified approach for modeling innovation adoption and optimal model selection for the diffusion process

Unified approach for modeling innovation adoption and optimal model selection for the diffusion process

Assessing the impact of negative WOM on diffusion process

An effort allocation model considering different budgetary constraint on fault detection process and fault correction process

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