Predicting System Usage from Intention and Past Use: Scale Issues in the Predictors

The objective of this study is to provide insights into how the predictive power for computer‐recorded system usage can be improved. Based on 386 responses from actual users of an information system, we examine the predictive power for system usage according to the scales of the predictors used, namely, intention and past use. First, we show that the predictive power of intention can be significantly improved with the choice of an appropriate measure. However, even the desirable intention measure failed to explain two‐thirds of the variance in system usage. Second, the results show that past use as measured by computer‐recorded log data can significantly enhance our ability to predict system usage. Finally, when both intention and past use are controlled for, the explained variance in system usage is shown to vary widely from 20% to 73%, depending on the predictors' scales. Overall, our findings suggest that an accurate prediction of system usage requires a more rigorous approach than that often applied in information systems research.     

Effect of urbanization on the dynamics of ecosystem services: An analysis for decision making in Kolkata urban agglomeration

Urban Ecosystems - Urbanization has profound influence on the changes of land use and land cover, which on the other hand exert significant impact on ecosystem services and their values, especially...     

Projecting populations for major Pacific Island countries with and without COVID-19: pro-active insights for population policy

Journal of Population Research - The paper projects aggregate populations of six Pacific Island countries in both pre- and post-COVID19 scenarios using a Cohort Component Method for the period...     

Bayesian neural tree models for nonparametric regression

Frequentist and Bayesian methods differ in many aspects but share some basic optimal properties. In real-life prediction problems, situations exist in which a model based on one of the above paradigms is preferable depending on some subjective criteria. Nonparametric classification and regression techniques, such as decision trees and neural networks, have both frequentist (classification and regression trees (CARTs) and artificial neural networks) as well as Bayesian counterparts (Bayesian CART and Bayesian neural networks) to learning from data. In this paper, we present two hybrid models combining the Bayesian and frequentist versions of CART and neural networks, which we call the Bayesian neural tree (BNT) models. BNT models can simultaneously perform feature selection and prediction, are highly flexible, and generalise well in settings with limited training observations. We study the statistical consistency of the proposed approaches and derive the optimal value of a vital model parameter. The excellent performance of the newly proposed BNT models is shown using simulation studies. We also provide some illustrative examples using a wide variety of standard regression datasets from a public available machine learning repository to show the superiority of the proposed models in comparison to popularly used Bayesian CART and Bayesian neural network models.