The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Rudolf Vetschera - One of the best experts on this subject based on the ideXlab platform.
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Innovation Diffusion of repeat purchase products in a competitive market an agent based simulation approach
European Journal of Operational Research, 2015Co-Authors: Christian Stummer, Markus Günther, Elmar Kiesling, Rudolf VetscheraAbstract:Abstract When introducing a new product into market, substantial amounts of resources are put at stake. Innovation managers therefore seek for reliable predictions of the respective Innovation Diffusion process. Making such predictions, however, is challenging, because the Diffusion trajectory is affected by various factors such as the type of Innovation, its perceived attributes, marketing activities and their impact, or consumers’ individual communication and adoption behaviors. Modeling the Diffusion of Innovations accordingly is of interest for both practitioners and management scholars. An agent-based model can overcome many limitations of traditional approaches. It accounts for heterogeneity in consumer preferences as well as in the social structure of their interactions and allows for modeling consumers as boundedly rational agents who make decisions under uncertainty and are influenced by micro-level drivers of adoption. We introduce an agent-based model that deals with repeat purchase decisions, addresses the competitive Diffusion of multiple products, and takes into consideration both the temporal and the spatial dimension of Innovation Diffusion. The corresponding simulation tool can support decision makers in analyzing the prospective Diffusion of an Innovation in scenarios that differ in pricing strategy, distribution strategy, and/or communication strategy. Its applicability is illustrated by means of an empirically grounded example for a second-generation biofuel.
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Innovation Diffusion of repeat purchase products in a competitive market an agent based simulation approach
European Journal of Operational Research, 2015Co-Authors: Christian Stummer, Markus Günther, Elmar Kiesling, Rudolf VetscheraAbstract:When introducing a new product into market, substantial amounts of resources are put at stake. Innovation managers therefore seek for reliable predictions of the respective Innovation Diffusion process. Making such predictions, however, is challenging, because the Diffusion trajectory is affected by various factors such as the type of Innovation, its perceived attributes, marketing activities and their impact, or consumers’ individual communication and adoption behaviors. Modeling the Diffusion of Innovations accordingly is of interest for both practitioners and management scholars.
Christian Stummer - One of the best experts on this subject based on the ideXlab platform.
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Innovation Diffusion of repeat purchase products in a competitive market an agent based simulation approach
European Journal of Operational Research, 2015Co-Authors: Christian Stummer, Markus Günther, Elmar Kiesling, Rudolf VetscheraAbstract:When introducing a new product into market, substantial amounts of resources are put at stake. Innovation managers therefore seek for reliable predictions of the respective Innovation Diffusion process. Making such predictions, however, is challenging, because the Diffusion trajectory is affected by various factors such as the type of Innovation, its perceived attributes, marketing activities and their impact, or consumers’ individual communication and adoption behaviors. Modeling the Diffusion of Innovations accordingly is of interest for both practitioners and management scholars.
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Innovation Diffusion of repeat purchase products in a competitive market an agent based simulation approach
European Journal of Operational Research, 2015Co-Authors: Christian Stummer, Markus Günther, Elmar Kiesling, Rudolf VetscheraAbstract:Abstract When introducing a new product into market, substantial amounts of resources are put at stake. Innovation managers therefore seek for reliable predictions of the respective Innovation Diffusion process. Making such predictions, however, is challenging, because the Diffusion trajectory is affected by various factors such as the type of Innovation, its perceived attributes, marketing activities and their impact, or consumers’ individual communication and adoption behaviors. Modeling the Diffusion of Innovations accordingly is of interest for both practitioners and management scholars. An agent-based model can overcome many limitations of traditional approaches. It accounts for heterogeneity in consumer preferences as well as in the social structure of their interactions and allows for modeling consumers as boundedly rational agents who make decisions under uncertainty and are influenced by micro-level drivers of adoption. We introduce an agent-based model that deals with repeat purchase decisions, addresses the competitive Diffusion of multiple products, and takes into consideration both the temporal and the spatial dimension of Innovation Diffusion. The corresponding simulation tool can support decision makers in analyzing the prospective Diffusion of an Innovation in scenarios that differ in pricing strategy, distribution strategy, and/or communication strategy. Its applicability is illustrated by means of an empirically grounded example for a second-generation biofuel.
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Agent-based simulation of Innovation Diffusion: a review
Central European Journal of Operations Research, 2012Co-Authors: Elmar Kiesling, Markus Günther, Christian Stummer, Lea M. WakolbingerAbstract:Mathematical modeling of Innovation Diffusion has attracted strong academic interest since the early 1960s. Traditional Diffusion models have aimed at empirical generalizations and hence describe the spread of new products parsimoniously at the market level. More recently, agent-based modeling and simulation has increasingly been adopted since it operates on the individual level and, thus, can capture complex emergent phenomena highly relevant in Diffusion research. Agent-based methods have been applied in this context both as intuition aids that facilitate theory-building and as tools to analyze real-world scenarios, support management decisions and obtain policy recommendations. This review addresses both streams of research. We critically examine the strengths and limitations of agent-based modeling in the context of Innovation Diffusion, discuss new insights agent-based models have provided, and outline promising opportunities for future research. The target audience of the paper includes both researchers in marketing interested in new findings from the agent-based modeling literature and researchers who intend to implement agent-based models for their own research endeavors. Accordingly, we also cover pivotal modeling aspects in depth (concerning, e.g., consumer adoption behavior and social influence) and outline existing models in sufficient detail to provide a proper entry point for researchers new to the field.
Peyton H Young - One of the best experts on this subject based on the ideXlab platform.
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the speed of Innovation Diffusion in social networks
LSE Research Online Documents on Economics, 2019Co-Authors: Itai Arieli, Yakov Babichenko, Ron Peretz, Peyton H YoungAbstract:New ways of doing things often get started through the actions of a few innovators, then diffuse rapidly as more and more people come into contact with prior adopters in their social network. Much of the literature focuses on the speed of Diffusion as a function of the network topology. In practice the topology may not be known with any precision, and it is constantly in flux as links are formed and severed. Here we establish an upper bound on the expected waiting time until a given proportion of the population has adopted that holds independently of the network structure. Kreindler and Young [38, 2014] demonstrated such a bound for regular networks when agents choose between two options: the Innovation and the status quo. Our bound holds for directed and undirected networks of arbitrary size and degree distribution, and for multiple competing Innovations with different payoffs. Revised November 2019.
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rapid Innovation Diffusion in social networks
Proceedings of the National Academy of Sciences of the United States of America, 2014Co-Authors: Gabriel E Kreindler, Peyton H YoungAbstract:Social and technological Innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid Diffusion. Here we derive bounds that are independent of network structure and size, such that Diffusion is fast whenever the payoff gain from the Innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the Innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the Innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks.
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Innovation Diffusion in heterogeneous populations contagion social influence and social learning
The American Economic Review, 2009Co-Authors: Peyton H YoungAbstract:New ideas, products, and practices take time to diffuse, a fact that is often attributed to some form of heterogeneity among potential adopters. This paper examines three broad classes of Diffusion models-contagion, social influence, and social learning-and shows how to incorporate heterogeneity into each at a high level of generality without losing analytical tractability. Each type of model leaves a characteristic "footprint" on the shape of the adoption curve which provides a basis for discriminating empirically between them. The approach is illustrated using the classic study of Ryan and Gross (1943) on the Diffusion of hybrid corn.
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Innovation Diffusion in heterogeneous populations contagion social influence and social learning
The American Economic Review, 2009Co-Authors: Peyton H YoungAbstract:New ideas, products, and practices take time to diffuse, a fact that is often attributed to some form of heterogeneity among potential adopters. This paper examines three broad classes of Diffusion models -- contagion, social influence, and social learning -- and shows how to incorporate heterogeneity into each at a high level of generality without losing analytical tractability. Each type of model leaves a characteristic "footprint" on the shape of the adoption curve which provides a basis for discriminating empirically between them. The approach is illustrated using the classic study of Ryan and Gross (1943) on the Diffusion of hybrid corn. (JEL D83, O33, Q16, Z13)
Nuno Bento - One of the best experts on this subject based on the ideXlab platform.
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calling for change Innovation Diffusion and the energy impacts of global mobile telephony
Energy research and social science, 2016Co-Authors: Nuno BentoAbstract:Abstract Few technologies in history diffused as intensively and fast as mobile phones, to the point where they have become the most democratic technology. The article analyzes historical patterns of mobile phone growth and their effects in energy needs. Through an empirical analysis employing Diffusion models on data for 227 countries between 1980 and 2010, it is concluded that global demand may saturate at around one subscription per person and the Diffusion of mobile-broadband connection has contributed to sustain growth. Demand has already showed signs of saturation in developed countries, while there is still potential for growth in developing countries. Impacts on energy consumption are assessed with the help of a field trial. Even though the energy consumed in phone charging was not very significant (6–8 TWh) in 2010, it becomes substantially higher when infrastructural needs are included (93 TWh). The actual trends suggest that mobile communication might have a sizeable direct effect on energy consumption—although the net impact on energy demand is more difficult to estimate. This can become an issue in developing countries, where the adoption of mobile phones is catching-up rapidly with the world average, in a context of generalized increasing electricity demand.
Yichuan Hsieh - One of the best experts on this subject based on the ideXlab platform.
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adding Innovation Diffusion theory to the technology acceptance model supporting employees intentions to use e learning systems
Educational Technology & Society, 2011Co-Authors: Yichuan HsiehAbstract:This study intends to investigate factors affecting business employees’ behavioral intentions to use the elearning system. Combining the Innovation Diffusion theory (IDT) with the technology acceptance model (TAM), the present study proposes an extended technology acceptance model. The proposed model was tested with data collected from 552 business employees using the e-learning system in Taiwan. The results show that five perceptions of Innovation characteristics significantly influenced employees’ e-learning system behavioral intention. The effects of the compatibility, complexity, relative advantage, and trialability on the perceived usefulness are significant. In addition, the effective of the complexity, relative advantage, trialability, and complexity on the perceived ease of use have a significant influence. Empirical results also provide strong support for the integrative approach. The findings suggest an extended model of TAM for the acceptance of the e-learning system, which can help organization decision makers in planning, evaluating and executing the use of e-learning systems.
