|
on Information and Communication Technologies |
| By: | Pousttchi, Key; Thurnher, Bettina |
| Abstract: | The integration of mobile workplaces in the (electronically mapped) intra-enterprise value chain is a major and still increasing corporate IT issue. Although the usage of mobile technologies for this purpose is far behind expectations and numerous failures can be observed,still little work has been done on theory building in this area. In this contribution we identify and compare existing frameworks for adoption and impact of mobile technology to support mobile business processes. The hypotheses underlying these frameworks are challenged with experiences from three long-term case studies which are diverse in industry, company size and other factors in order to scrutinize their potential for generalization. The outcome is a set of hypotheses that show robustness against variation of major parameters and thus may be suitable to serve as a basis for a generalized and unified framework on mobile-integrated business processes. |
| JEL: | M21 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:3243&r=ict |
| By: | Adhya Sumanta; Banerjee Tathagata; Chattopadhyay Gaurangadeb |
| Abstract: | We consider the estimation of finite population proportions of categorical survey responses obtained by probability sampling. The customary design-based estimator does not make use of the auxiliary data available for all the population units at the estimation stage. We adopt a model-based predictive approach to incorporate this information and make the estimates more efficient. In the first part of our paper we consider a multinomial logit type model when logit function is a known parametric function of the covariates. We then use it for the prediction of non-sampled responses. This together with sampled responses is used to obtain the estimates of the proportions. The asymptotic biases and variances of these estimators are obtained. The main drawback of this approach is, being a parametric model it may suffer from model misspecification and thus, may lose it’s efficiencies over the usual design-based estimates. To overcome this drawback, in the next part of this paper we replace the multinomial logit type model by a nonparametric model using recently developed random coefficients splines models. Finally, we carry out a simulation study. It shows that the nonparametric approach may lead to an appreciable improvement over both parametric and design-based approaches when the regression function is quite different from multinomial logit. |
| Keywords: | Auxiliary information, Model-based inference, Finite population estimation, Multinomial logit, Random coefficients splines models, Laplace approximation |
| Date: | 2007–05–14 |
| URL: | http://d.repec.org/n?u=RePEc:iim:iimawp:2007-05-07&r=ict |
| By: | Mika Meitz; Pentti Saikkonen |
| Abstract: | This paper studies a class of Markov models which consist of two components. Typically, one of the components is observable and the other is unobservable or `hidden`. Conditions under which geometric ergodicity of the unobservable component is inherited by the joint process formed of the two components are given. This implies existence of initial values such that the joint process is strictly stationary and ?-mixing. In addition to this, conditions for the existence of moments are also obtained and extensions to the case of nonstationary initial values are provided. All these results are applied to a general model which includes as special cases various first order generalized autoregressive conditional heteroskedasticity (GARCH) and autoregressive conditional duration (ACD) models with possibly complicated non-linear structures. The results only require mild moment assumptions and in some cases provide necessary and sufficient conditions for geometric ergodicity. |
| Keywords: | Generalized Autoregressive Conditional Heteroskedasticity, Autoregressive Conditional Duration, GARCH-in-mean, Nonlinear Time Series Models, Geometric Erogidicity, Mixing, Strict Stationarity, Existence of Moments, Markov Models |
| JEL: | C10 C22 |
| Date: | 2007 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:327&r=ict |