17 research outputs found

    The diffusion of a new service: Combining service consideration and brand choice

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    We propose an individual-level model of a two-stage service diffusion process. In the first stage, customers decide whether to "consider" joining the service. This (Consideration) stage is modeled by a hazard model. Customers who decide to consider the service move on to the Choice stage, wherein they choose among the service alternatives and an outside No Choice option. This stage is modeled by a conditional Multinomial Logit model. The service provider does not observe the transition in the first stage of potential customers who have yet to choose a brand. Such potential customers may have started to consider joining the service, yet chose the outside alternative in each period thereafter. One of the main contributions of the model is its ability to distinguish between these two non-adopter types. We estimated the model using data on the adoption process of newly introduced service plans offered by a commercial bank. We employed the hierarchical Bayes Monte Carlo Markov Chain procedure to estimate individual as well as population parameters. The empirical results indicate that the model outperforms competing models in breadth of analysis, model fit, and prediction accuracy

    Public Preferences for Forest Ecosystem Management in Japan with Emphasis on Species Diversity

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    We carried out online choice experiments (CE) to investigate what value Japanese individuals assign to rare versus familiar species in forest ecosystem, and to determine how preference heterogeneity arises. CE attributes comprised a forestry charge as the price attribute and rare versus familiar species of animals or plants as the good to be valued. Species numbers in a 5 km-mesh forest area were evaluated without the use of species names to focus purely on responses to numerical changes. Positional effects were also tested to validate results regarding alternatives and attributes other than the price attribute. A random parameter logit model was adopted to capture preferences for species diversity. After confirming that no positional effects existed, we found that (1) rare animals were valued more highly than rare plants, (2) familiar plants were assigned a positive value, but familiar animals were not assigned significant value at the mean parameter estimate, and (3) preference heterogeneities existed for all species. The sources of preference heterogeneity were analyzed with a latent class model having principal components of environmental attitudes. The influence of such attitudes was shown to be significant and suggested that attention should be paid to belief systems rather than solely demographics

    A Model for Inferring Market Preferences from Online Retail Product Information Matrices

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    © 2016 New York University This research extends information display board methods, currently employed to study information processing patterns in laboratory settings, to a field based setting that also yields managerially useful estimates of market preferences. A new model is proposed based on statistical, behavioral, and economic theories, which integrates three decisions consumers must make in this context: which product-attribute to inspect next, when to stop processing, and which, if any, product to purchase. Several theoretical options are considered on how to model product attribute selection and how to treat uninspected attributes. The modeling options are empirically tested employing datasets collected at a popular e-tailer's website, while customers were making product evaluation and purchase decisions. Subsequent to identifying the best model, we show how the resulting attribute preference estimates can be managerially employed to improve customer targeting of abandoned shopping carts for follow up communications aimed at improving sales conversions
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