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This paper aims to develop a parsimonious and innovative model that captures the dynamics of new product diffusion in the recent high-technology markets and thus assist both academicians and practitioners who are eager to understand the diffusion phenomena. Accordingly, this study develops a novel diffusion model to forecast the demand by centering on the dynamic state of the product’s adoption rate. The proposed study also integrates the consumer’s psychological point of view on price change and goodwill of the innovation in the diffusion process.

In this study, a two-dimensional distribution function has been derived using Cobb–Douglas’s production function to combine the effect of price change and continuation time (goodwill) of the technology in the market. Focused on the realistic scenario of sales growth, the model also assimilates the time-to-time variation in the adoption rate (hazard rate) of the innovation owing to companies changing marketing and pricing strategies. The time-instance upon which the adoption rate alters is termed as change-point.

For validation purpose, the developed model is fitted on the actual sales and price data set of dynamic random access memory (DRAM) semiconductors, liquid crystal display (LCD) monitors and room air-conditioners using non-linear least squares estimation procedure. The results indicate that the proposed model has better forecasting efficiency than the conventional diffusion models.

The developed model is intrinsically restricted to a single generation diffusion process. However, technological innovations appear in generations. Therefore, this study also yields additional plausible directions for future analysis by extending the diffusion process in a multi-generational environment.

This study aims to assist marketing managers in determining the long-term performance of the technology innovation and examine the influence of fluctuating price on product demand. Besides, it also incorporates the dynamic tendency of adoption rate in modeling the diffusion process of technological innovations. This will support the managers in understanding the practical implications of different marketing and promotional strategies on the adoption rate.

This is the first attempt to study the value-based diffusion model that includes key interactions between goodwill of the innovation, price dynamics and change-point for anticipating the sales behavior of technological products.

The purpose of this study is twofold. First, an integrated model will be developed based on task-technology fit, innovation diffusion theory and the new product adoption model in order to explore the factors that affect smartwatch adoption. Second, the differences in the factors that affect users’ intention to adopt the Apple Watch and other smartwatches will be examined.

The data for this study were collected via an online survey questionnaire. The responses of 341 potential adopters of smartwatches were used to test the hypotheses in the research model. The casual model was assessed using partial least squares techniques.

The model can account for more than 50 percent of the variance in adoption intention. The research results affirm prior findings that perceived product attributes have relatively strong influence on adoption intention. Among these attributes, relative advantage has the strongest effect. Moreover, this study revealed differences between the antecedents of Apple watches and those of non-Apple watches.

The insights provided by this study can help smartwatch providers formulate better growth strategies. The findings also provide some directions for further development.

This study provides a better understanding of how the factors in the theories influence the adoption intentions of Apple watches and non-Apple watches.

The adoption process for new products varies from one individual to another according to socio‐economic and demographic characteristics. This article focuses on cultural values because an individual’s inclination to adopt a new product is also influenced by his system of values. The advantage of using values to explain innovativeness is that this variable transcends national, cultural and social boundaries. In order to determine the influence of values on adoption, this study utilizes a multicultural research framework consisting of consumers living in a large metropolitan area and coming from three distinct cultural groups: business school students from Quebec, France and North Africa. The results of this study suggest that individual values have a significant impact on consumers’ inclinations to adopt new products. In multi‐ethnic heterogeneous markets, segmenting consumers according to their values should be an important tool in the strategic kit of marketing managers.

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

When considering whether to adopt a network technology, how does uncertainty about whom a potential adopter might interact with affect their adoption choice? On the one hand, uncertainty about potential network partners might enhance adoption incentives, as increased uncertainty induces the potential for economies of scope across the potential network. On the other hand, uncertainty may reduce the expected value of any particular connection, and reduce adoption incentives. Since this is a theoretical puzzle, this chapter presents empirical evidence to help illuminate it. It presents evidence the destabilizing of a social network may increase the scope of network externalities, using data on sales of a video-calling system made to an investment bank’s employees and subsequent usage by these customers. The terrorist attacks of 2001 led potential customers in New York to start communicating with a new and less predictable set of people when their work teams were reorganized as a result of the physical displacement that resulted from the attacks. This did not happen in other comparable cities. These destabilized communication patterns were associated with potential adopters in New York being more likely to take into account a wider spectrum of the user base when deciding whether to adopt relative to those in other cities. Empirical analysis suggests that the aggregate effect of network externalities on adoption was doubled by this instability, and that for those with diffuse networks, this more than compensated for the negative baseline effects of the instability.

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.

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.

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.

The idea has been validated on product. It would be interesting to know how the methodology works on service.

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.

The 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.

The 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.

The 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.

The 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.

The integration of consumers' behavior (Bass model) to digital technologies is an additional contribution of the present research toward sustainability issues from the social perspective.

Previous 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.

Internet-based intangible network good (IING) has revolutionized multiple industries in recent years. This paper aims to reveal the laws of consumer’s decision-making on IING from a perspective of kinetic energy and potential energy.

In this paper, 4 aspects and 17 factors influencing IING adoption were generalized. Based on the theory of social physics, an agent-based simulation model, introducing physical energy theory to depict consumer’s decision-making, was built. An agent’s kinetic energy reflects the agent’s perceived effect of mass media on the agent’s decision-making on IING adoption. An agent’s potential energy reflects the agent’s perceived effect of social interactions on the agent’s decision-making on the adoption of IING. An agent’s final energy is the sum of the kinetic energy and potential energy, which reflects the agent’s final decision.

Some factors mainly influence the diffusion velocity, while other factors have a dramatic impact on both diffusion velocity and diffusion scale. The agent’s personality can make a difference at the early and middle stages of IING adoption, but a faint impact at the later stage because of the effects of network externalities and word of mouth. There is a critical value of the number of initial adopters which can dramatically speed up IING adoption.

This study provides new insights for firms on the effects of factors influencing consumers’ decision-making on IING adoption.

This paper defines a new kind of innovation, IING, and generalizes IING’s special characteristics. As a new application of social physics, the physical energy theory has been creatively introduced to depict consumer’s decision-making on IING adoption. A kinetic and potential energy model of IING adoption has been built. Based on simulation experiments, new insights of IING adoption have been gained.

The purpose of this paper is to provide insightful advice that can improve the practice of using consumers’ pre-launch awareness and preference (AP) changes to predict the sales of new movies.

This paper applies a new movie box-office revenue forecasting model based on consumers’ weekly AP measures, to take advantage of the industry’s practice of using weekly survey data containing the AP measures of upcoming new movies. Specifically, a sales forecasting model is developed on the basis of the theory that the combination of the nature of new product preference (positive vs negative) and the timing of new product awareness (early vs recent) influences entertainment product sales.

This paper shows that early awareness consumers are as important as late awareness consumers in determining new product sales, suggesting that more marketing resources need to be allocated earlier than currently practiced. This paper also shows that when negative preferences dominate positive preferences well ahead of a product’s release, marketing efforts cannot overcome the negative sentiment of the market. Finally, the empirical application illustrates that three consumer segments varying in product expertise and consumption frequency reveal different AP patterns among high-, medium- and low-performance products.

This paper is intended to provide insightful advice that can improve the AP-based approach in entertainment industries. Toward that end, the authors emphasize two major aspects in association with new entertainment product sales: rethinking survey-based AP measures and examining heterogeneous consumer segments’ differential AP patterns.