Search results

Taiwan is one of several leading countries in the mobile music context. Accordingly, Taiwan's experiences in promoting mobile music service diffusion are of importance and interest to international practitioners and researchers. Applying Rogers' innovation diffusion theory, this study aims to employ econometric models to investigate whether the diffusion of mobile music service adoption is affected by external influences (e.g. mass media advertising, salespeople, and service providers), internal influences (e.g. interactions and imitations among acquaintances), or a combination of such influences.

To determine which influence best explained the diffusion of mobile music adoptions, the external, internal, and Von Bertalanffy mixed influence diffusion models were tested in this study. GNUS, a strongly functional language and environment to statistically explore data sets, was used to estimate the parameters of each model. The performance of each diffusion model was then examined using the Akaike AIC and Schwarz BIC statistics.

Findings indicated that the Von Bertalanffy mixed influences model best describes the diffusion pattern of mobile music service adoption and that acquaintances' influence in terms of interactions is the dominant factor influencing mobile music service adoption decision in Taiwan.

How managers of a mobile music service provider can use the internal and external influences interchangeably to effectively accelerate the mobile music diffusion at the different stage of product lifecycle is presented in this study. Indeed, the mobile music service is one of the most important industries worldwide not only because its penetration rate in many countries is over 50 percent, but also because of its killer applications. In light of this, the study contributes highly to theoretical and empirical examinations because the diffusion of the mobile music services within a society is the essence of the development/usage of the m‐commerce or music industries.

A simple approach is developed for modelling the distribution of dopant in crystals grown by the Czochralski technique. Allowance is made for the temperature dependence of the diffusivity and for the diffusion of dopant behind the moving boundary. It is found that the effect can make a significant difference to the final dopant distribution. The diffusion process is modelled using a discrete method, previously developed for situations in which the boundary is static.

This chapter aims to assess how effective loyalty programs are in contributing to retaining guests for hotels. The effectiveness is measured by means of a Bass model which allows the measurement of the diffusion patterns of adopters within potential adopters. The data used to perform this model allow the depiction of the effect of geographical localization over a time frame of three years. Results suggest that the loyalty card’s acceptance was measured from the internal and external parameters, based on the concept of diffusion theory. The results indicated a need for innovation of the loyalty program from 2019. Due to the existence of several hotels with different typologies in different countries, a segmentation of clients by nationalities is suggested with a “waterfall” strategy being placed in the hotel chain loyalty program.

We develop a model to analyze the growth of social organizations as a spatially nested mixed-influence diffusion process. Drawing on gravity models and threshold models, we split the social system into social units and model the diffusion process as a system of differential equations. The diffusion of a new organizational form in a social unit is a function of an internal process within the unit and external processes in the surrounding units. The model is confronted with data on the growth of trade unions in Stockholm, Sweden, between 1890 and 1940.

Some functional properties of engineering materials, i.e. physical, mechanical and thermal ones, depend directly on the microstructure, which is a result of processes occurring in the material during the forming and thermomechanical processing. The proper microstructure can be obtained in many cases by the phase transformation. This phenomenon is one of the most important processes during hot forming and heat treatment. The purpose of this paper is to develop a new comprehensive hybrid model for modeling diffusion phase transformations. A problem has been divided into several tasks and is carried out on several stages. The purpose of this stage is a development of the structure of a hybrid model, development of an algorithm used in the diffusion module and one-dimensional heat flow and diffusion modeling. Generally, the processes of phase transformations are studied well enough but there are not many tools for their complex simulations. The problems of phase transformation simulation are related to the proper consideration of diffusion, movement of phase boundaries and kinetics of transformation. The proposed new model at the final stage of development will take into account the varying grain growth rate, different shape of growing grains and will allow for proper modeling of heat flow and carbon diffusion during the transformation in many processes, where heating, annealing and cooling can be considered (e.g. homogenizing and normalizing).

One of the most suitable methods for modeling of microstructure evolution during the phase transformation is cellular automata (CA), while lattice Boltzmann method (LBM) suits for modeling of diffusion and heat flow. Then, the proposed new hybrid model is based on CA and LBM methods and uses high performing parallel computations.

The first simulation results obtained for one-dimensional modeling confirm the correctness of interaction between LBM and CA in common numerical solution and the possibility of using these methods for modeling of phase transformations. The advantages of the LBM method can be used for the simulation of heat flow and diffusion during the transformation taking into account the results obtained from the simulations. LBM creates completely new possibilities for modeling of phase transformations in combination with CA.

The studies are focused on diffusion phase transformations in solid state in condition of low cooling rate (e.g. transformation of austenite into ferrite and pearlite) and during the heating and annealing (e.g. transformation of the ferrite-pearlite structure into austenite, the alignment of carbon concentration in austenite and growth of austenite grains) in carbon steels within a wide range of carbon content. The paper presents the comprehensive modeling system, which can operate with the technological processes with phase transformation during heating, annealing or cooling.

A brief review of the modeling of phase transformations and a description of the structure of a new CA and LBM hybrid model and its modules are presented in the paper. In the first stage of model implementation, the one-dimensional LBM model of diffusion and heat flow was developed. The examples of simulation results for several variants of modeling with different boundary conditions are shown.

The purpose of this paper is to analyze the relationship between new product diffusion and consumer internet search patterns using big data and to investigate whether such data can be used in forecasting new product diffusion.

This research proposes a new product diffusion model based on the Bass diffusion model by incorporating consumer internet search behavior. Actual data from search engine queries and new vehicle sales for each vehicle class and region are used to estimate the proposed model. Statistical analyses are used to interpret the estimated results, and the prediction performance of the proposed method is compared with other methods to validate the usefulness of data for internet search engine queries in forecasting new product diffusion.

The estimated coefficients of the proposed model provide a clear interpretation of the relationship between new product diffusion and internet search volume. In 83.62 percent of 218 cases, analyzing the internet search pattern data are significant to explain new product diffusion and that internet search volume helps to predict new product diffusion. Therefore, marketing that seeks to increase internet search volume could positively affect vehicle sales. In addition, the demand forecasting performance of the proposed diffusion model is superior to those of other models for both long-term and short-term predictions.

As search queries have only been available since 2004, comparisons with data from earlier years are not possible. The proposed model can be extended using other big data from additional sources.

This research directly demonstrates the relationship between new product diffusion and consumer internet search pattern and investigates whether internet search queries can be used to forecast new product diffusion by product type and region. Based on the estimated results, increasing internet search volume could positively affect vehicle sales across product types and regions. Because the proposed model had the best prediction power compared with the other considered models for all cases with large margins, it can be successfully utilized in forecasting demand for new products.

To provide an explicit model to address the relationships between the structural characteristics of a network and the diffusion of innovations through it. Further, based on the above relationships, this research tries to provide a way to infer diffusion curve parameters (innovation coefficient and imitation coefficient) from network structure (e.g. centralization).

Based on the network and innovation literatures, we develop a model explicitly relating the structural properties of the network to its innovation and imitation potential, and in turn to the observed diffusion parameters (innovation and imitation coefficients). We first employ current theoretical and empirical results to develop postulates linking six key network properties to innovation and imitation outcomes, and then seek to model their effects in an integrative manner. We argue that the innovation and imitation potentials of a network may be increased by strategically re‐designing the underlying network structure. We validated the model by searching the published empirical literature for available published data on network properties and innovation and imitation coefficients.

We validated the model by searching the published empirical literature for available published data on network properties and innovation and imitation coefficients. The results reported from various relevant research papers support our model.

This research shows that the innovation and imitation potentials of a network may be increased by strategically re‐designing the underlying network structure; hence, provide guidelines for new product managers to enhance the performance of innovative products by re‐design the underlying network structure.

The model developed in this paper is a breaking through result of synthesizing various traditions of diffusion research, ranging from anthropology and economics to marketing which were developed independently. The research explicitly modeled the diffusion process in terms of the underlying network structure of the relevant population allowing managers and researchers to directly link the diffusion parameters to the structural properties of the network. By doing so, it added value by making it possible to infer diffusion potential from directly measurable network properties. Vis‐à‐vis the network diffusion literature in particular, we added value by “unpacking” the diffusion process into innovation and imitation processes that form the building blocks of contagion. Moreover, we developed a holistic structural model of network diffusion which integrates the several network properties that have hitherto been studied separately.

This paper aims to develop a diffusion model that can be used to understand and forecast the market growth of service products in a competitive environment. Despite the fast growth of the service sector, the existing literature has dedicated little effort to modeling the market growth of service products.

The authors propose a choice-type diffusion model that links the issues of service product utility, customers’ choice preference, customer switching behavior and the market growth of service products. The authors use the market data of one online product and assess the performance of the proposed model using this case.

The results demonstrate the model’s good fitting and forecasting performance. Specifically, the proposed model has better performance than the benchmarks the authors choose from the existing literature.

This study shows that market growth of service products can have different diffusion patterns with that of durable goods, which is evidence of the needs for specific models for service diffusion. Further, this study demonstrates the important role of customer switching in service diffusion. Also for marketing practitioners, this study provides an explanation and forecasting tool for the market growth of service products, which can be used for marketing planning in the service industry.

The objective of this study was two fold: first, we wanted to test the goodness of fit of different diffusion models that include the variable “distribution”, and then analyze the existence of the effects of “country” and “time” in the parameters of diffusion currently employed in different European countries. The literature on the matter suggests, on the one hand, that the diffusion models applied so far that employ multi‐equation systems are not obviously better than other single‐equation models that ignore marketing variables; and, on the other hand, that the socio‐economic environment and the time‐lag are both factors of the differences that the diffusion process offers among different geographical areas. As a novelty, our methodology proposes single‐equation models that include the distribution, to which several indicators of the goodness of fit are applied, along with statistical tests on the differences in parameters among countries. The empirical application carried out in Spain, France and Italy demonstrates the superiority of the model proposed here, based on the Generalized Bass model. It also indicates a “country” effect, between Spain and France as well as between Italy and France