Perceptions Towards Adoption of Mobile Self-services: A Case Study (Available in Hanken intranet only)

This thesis is an exploratory case study that aims to understand the attitudes affecting adoption of mobile self-services. This study used a demo mobile self-service that could be used by consumers for making address changes. The service was branded with a large and trusted Finnish brand. The theoretical framework that was used consisted of adoption theories of technology, adoption theories of self-service and literature concerning mobile services. The reviewed adoption theories of both technology and self-service had their foundation in IDT or TRA/TPB. Based on the reviewed theories an initial framework was created. The empirical data collection was done through three computer aided group interview sessions with a total of 32 respondents. The data analysis started from the premises of the initial framework. Based on the empirical data the framework was constantly reviewed and altered and the data recoded accordingly. The result of this thesis was a list of attitudinal factors that affect the adoption of a mobile self-service either positively or negatively. The factors that were found to affect the attitudes towards adoption of mobile self-services positively were: that the service was time & place independent and saved time. Most respondents, but not all, also had a positive attitude towards adoption due to ease of use and being mentally compatible with the service. Factors that affected adoption negatively were lack of technical compatibility, perceived risk for high costs and risk for malicious software. The identified factors were triangulated in respect to existing literature and general attitudes towards mobile services.

Comparative study on damage identification from lso-Eigen-Value-Change contours and smeared damage model

The paper proposes two methodologies for damage identification from measured natural frequencies of a contiguously damaged reinforced concrete beam, idealised with distributed damage model. The first method identifies damage from Iso-Eigen-Value-Change contours, plotted between pairs of different frequencies. The performance of the method is checked for a wide variation of damage positions and extents. The method is also extended to a discrete structure in the form of a five-storied shear building and the simplicity of the method is demonstrated. The second method is through smeared damage model, where the damage is assumed constant for different segments of the beam and the lengths and centres of these segments are the known inputs. First-order perturbation method is used to derive the relevant expressions. Both these methods are based on distributed damage models and have been checked with experimental program on simply supported reinforced concrete beams, subjected to different stages of symmetric and un-symmetric damages. The results of the experiments are encouraging and show that both the methods can be adopted together in a damage identification scenario.

Reliability models for existing structures based on dynamic state estimation and data based asymptotic extreme value analysis

The problem of time variant reliability analysis of existing structures subjected to stationary random dynamic excitations is considered. The study assumes that samples of dynamic response of the structure, under the action of external excitations, have been measured at a set of sparse points on the structure. The utilization of these measurements m in updating reliability models, postulated prior to making any measurements, is considered. This is achieved by using dynamic state estimation methods which combine results from Markov process theory and Bayes' theorem. The uncertainties present in measurements as well as in the postulated model for the structural behaviour are accounted for. The samples of external excitations are taken to emanate from known stochastic models and allowance is made for ability (or lack of it) to measure the applied excitations. The future reliability of the structure is modeled using expected structural response conditioned on all the measurements made. This expected response is shown to have a time varying mean and a random component that can be treated as being weakly stationary. For linear systems, an approximate analytical solution for the problem of reliability model updating is obtained by combining theories of discrete Kalman filter and level crossing statistics. For the case of nonlinear systems, the problem is tackled by combining particle filtering strategies with data based extreme value analysis. In all these studies, the governing stochastic differential equations are discretized using the strong forms of Ito-Taylor's discretization schemes. The possibility of using conditional simulation strategies, when applied external actions are measured, is also considered. The proposed procedures are exemplifiedmby considering the reliability analysis of a few low-dimensional dynamical systems based on synthetically generated measurement data. The performance of the procedures developed is also assessed based on a limited amount of pertinent Monte Carlo simulations. (C) 2010 Elsevier Ltd. All rights reserved.