Project (M.A.) - Simon Fraser University, 2005. / Project (Dept. of Economics) / Simon Fraser University. Also issued in digital format and available on the World Wide Web.

Thesis (Ph. D.)--University of Washington, 1997. / Vita. Includes bibliographical references (leaves [55]-58).

Thesis (Ph. D.)--University of Texas at Austin, 1998. / Vita. Includes bibliographical references (leaves 161-173). Available also in a digital version from Dissertation Abstracts.

Thesis (Ph. D.)--University of Chicago Graduate School of Business, August 1999. / Includes bibliographical references. Also available on the Internet.

Market conditions are constantly changing. Enterprises try to furnish the best products and services, to be competitive and sustainable. Constantly new businesses are coming to the market which compete the existing ones. Customers demand quality products and services and want to fulfil their wishes. And it is a big challenge for a company's strategic management which must respond efficiently to stimuli from the external environment and shape a strategy to achieve corporate goals. This thesis offers a theoretical perspective on strategy, strategic management and strategic analysis. Theoretical knowledge is applied in a practical part and the company's draft strategy has been formulated using the strategic analysis results in the form Business model Canvas.

Flow propagators have been frequently used in characterisation of porous media and the study of fluid transport behaviour. Previous work considered the shape of measured flow propagators using Nuclear Magnetic Resonance (NMR) discussed the influence of pore geometry, dispersion, relaxation and internal gradients. In addition, numerically simulated flow propagators were also reported. However, a uantitative numerical analysis of local contributions to flow propagators has not been considered in the literature, yet may provide significant new insights into the flow behaviour through complex porous media. In this work we use two types of beads to realize a dual-scale bead pack consisting of micro- and macropore regions for the NMR experiments. A low-field NMR system (2 MHz) was used to measure flow propagators for this sample. We further generated a dual-scale Gaussian Random Field (GRF) image based on porosity, beads diameters and volume fraction of each type of bead for numerical simulations. A Lattice Boltzmann Method (LBM) and Random Walk (RW) technique were combined to derive the simulated flow propagators and validated against experiments. We carry out a local analysis of the flow propagators showing a significant difference in bandwidth of displacements in micro- and macro-pore regions. In addition, the local flow propagators indicate a linear relationship between mixing (the fluid exchange on regions' boundaries) and flow velocities as well as a non-linear correlation between mixing and evolution times.

This study investigates the tool wear of advanced PVD TiALN/TiN multilayer coated end mill inserts when dry and semi-dry machining 4340 low alloy medium carbon steel. A factorial design of experiment setup consisting of two levels of speed, three levels of feed, two levels of depth of cut, and two levels of cutting conditions (semi-dry and dry) was used for the study. The combination of cutting conditions that gave the best response for different components of cutting force, cutting power, surface roughness and tool life were determined using MANOVA & ANOVA analysis and Tukey comparison of means test using MINITAB statistical software package. From a study of the Energy Dispersive X ray (EDX) analysis and primary back scatter images obtained from the worn out crater surface of the insert, it was observed that diffusion wear prevailed under both dry and semi-dry machining conditions. A tool life model was developed using multiple regression analysis within the range of cutting conditions selected. A model for flank wear progression was also developed using mixed effects modeling technique using S Plus statistical software package. This technique takes into account between and within work piece variations during end milling and produces a very accurate model for tool wear progression. This is the first time application of the mixed effects modeling technique in metal cutting literature.

In this thesis, we examine labor market outcomes for the youth (ages 15-29) using microdata from several rounds of the Turkish Household Labor Force Survey (HLFS). We begin by examining demographic trends. We then rely on synthetic cohorts. The fact that the HLFS sample frame targets the civilian non-institutional population brings about difficulties in interpreting labor market indicators. We show that a more reasonable picture of schooling and work choices emerges when a simple correction for &lsquo / missing males&rsquo / who are doing their CMS and examine the effect of Compulsory Military Service (CMS) on the transition from school to work by using discrete hazard models. We also investigate the time it takes to find the first permanent job to shed light on the recent evolution of the transition from school to work.Using Cox Proportional Hazard Model, we examine the effects of structural reforms and macro-economic conditions, and the permanence of these effects. We are able to study the differences in the hazard of obtaining the first permanent job by education levels non-parametrically. Finally, we investigate the changes in the cumulative baseline hazards over time and test for the presence of gender differences in the hazard rates by using time varying covariates. With the help of these covariates, we are able to compute the time needed for the closure of the gender gap.

Suicide has drawn much attention from both the scientific community and the public. Examining the impact of socio-environmental factors on suicide is essential in developing suicide prevention strategies and interventions, because it will provide health authorities with important information for their decision-making. However, previous studies did not examine the impact of socio-environmental factors on suicide using a spatial analysis approach. The purpose of this study was to identify the patterns of suicide and to examine how socio-environmental factors impact on suicide over time and space at the Local Governmental Area (LGA) level in Queensland. The suicide data between 1999 and 2003 were collected from the Australian Bureau of Statistics (ABS). Socio-environmental variables at the LGA level included climate (rainfall, maximum and minimum temperature), Socioeconomic Indexes for Areas (SEIFA) and demographic variables (proportion of Indigenous population, unemployment rate, proportion of population with low income and low education level). Climate data were obtained from Australian Bureau of Meteorology. SEIFA and demographic variables were acquired from ABS. A series of statistical and geographical information system (GIS) approaches were applied in the analysis. This study included two stages. The first stage used average annual data to view the spatial pattern of suicide and to examine the association between socio-environmental factors and suicide over space. The second stage examined the spatiotemporal pattern of suicide and assessed the socio-environmental determinants of suicide, using more detailed seasonal data. In this research, 2,445 suicide cases were included, with 1,957 males (80.0%) and 488 females (20.0%). In the first stage, we examined the spatial pattern and the determinants of suicide using 5-year aggregated data. Spearman correlations were used to assess associations between variables. Then a Poisson regression model was applied in the multivariable analysis, as the occurrence of suicide is a small probability event and this model fitted the data quite well. Suicide mortality varied across LGAs and was associated with a range of socio-environmental factors. The multivariable analysis showed that maximum temperature was significantly and positively associated with male suicide (relative risk [RR] = 1.03, 95% CI: 1.00 to 1.07). Higher proportion of Indigenous population was accompanied with more suicide in male population (male: RR = 1.02, 95% CI: 1.01 to 1.03). There was a positive association between unemployment rate and suicide in both genders (male: RR = 1.04, 95% CI: 1.02 to 1.06; female: RR = 1.07, 95% CI: 1.00 to 1.16). No significant association was observed for rainfall, minimum temperature, SEIFA, proportion of population with low individual income and low educational attainment. In the second stage of this study, we undertook a preliminary spatiotemporal analysis of suicide using seasonal data. Firstly, we assessed the interrelations between variables. Secondly, a generalised estimating equations (GEE) model was used to examine the socio-environmental impact on suicide over time and space, as this model is well suited to analyze repeated longitudinal data (e.g., seasonal suicide mortality in a certain LGA) and it fitted the data better than other models (e.g., Poisson model). The suicide pattern varied with season and LGA. The north of Queensland had the highest suicide mortality rate in all the seasons, while there was no suicide case occurred in the southwest. Northwest had consistently higher suicide mortality in spring, autumn and winter. In other areas, suicide mortality varied between seasons. This analysis showed that maximum temperature was positively associated with suicide among male population (RR = 1.24, 95% CI: 1.04 to 1.47) and total population (RR = 1.15, 95% CI: 1.00 to 1.32). Higher proportion of Indigenous population was accompanied with more suicide among total population (RR = 1.16, 95% CI: 1.13 to 1.19) and by gender (male: RR = 1.07, 95% CI: 1.01 to 1.13; female: RR = 1.23, 95% CI: 1.03 to 1.48). Unemployment rate was positively associated with total (RR = 1.40, 95% CI: 1.24 to 1.59) and female (RR=1.09, 95% CI: 1.01 to 1.18) suicide. There was also a positive association between proportion of population with low individual income and suicide in total (RR = 1.28, 95% CI: 1.10 to 1.48) and male (RR = 1.45, 95% CI: 1.23 to 1.72) population. Rainfall was only positively associated with suicide in total population (RR = 1.11, 95% CI: 1.04 to 1.19). There was no significant association for rainfall, minimum temperature, SEIFA, proportion of population with low educational attainment. The second stage is the extension of the first stage. Different spatial scales of dataset were used between the two stages (i.e., mean yearly data in the first stage, and seasonal data in the second stage), but the results are generally consistent with each other. Compared with other studies, this research explored the variety of the impact of a wide range of socio-environmental factors on suicide in different geographical units. Maximum temperature, proportion of Indigenous population, unemployment rate and proportion of population with low individual income were among the major determinants of suicide in Queensland. However, the influence from other factors (e.g. socio-culture background, alcohol and drug use) influencing suicide cannot be ignored. An in-depth understanding of these factors is vital in planning and implementing suicide prevention strategies. Five recommendations for future research are derived from this study: (1) It is vital to acquire detailed personal information on each suicide case and relevant information among the population in assessing the key socio-environmental determinants of suicide; (2) Bayesian model could be applied to compare mortality rates and their socio-environmental determinants across LGAs in future research; (3) In the LGAs with warm weather, high proportion of Indigenous population and/or unemployment rate, concerted efforts need to be made to control and prevent suicide and other mental health problems; (4) The current surveillance, forecasting and early warning system needs to be strengthened, to trace the climate and socioeconomic change over time and space and its impact on population health; (5) It is necessary to evaluate and improve the facilities of mental health care, psychological consultation, suicide prevention and control programs; especially in the areas with low socio-economic status, high unemployment rate, extreme weather events and natural disasters.

This study attempts to answer the research question ‘Can a novel model of health information system strengthen process for conducting research to understand the effects of air pollution on CVD in developing countries?’ There is limited research output from Asia and in particular, from India on studies of the deleterious effects of air pollution on CVD. This research aimed to investigate the barriers in developing countries and proposed the use of a spatiotemporal methodology to assess the effects of air pollution on CVD by developing an application based on a GIS platform. Choosing Bangalore as a case study area, secondary data from various governmental departments that included demographic data, air pollution data and mortality data were obtained. An Environmental Health Information system application based on GIS platform was developed specifically for Bangalore and with the characteristics of the datasets available. Data quality assessment was carried out on these datasets that resulted in the recommendation of a generalisable data quality framework to enable better data collection that will aid in strengthening health development policies. The data was analysed using spatial and non-spatial techniques. Results showed that levels of PM10 were of concern to the city with all areas having either high or critical levels of pollution. CVD deaths also were of concern contributing to almost 40% of total mortality. The potential years of life lost (PYLL), which is an estimate of the average years a person would have lived if he or she had not died prematurely was calculated for the years from 2010 to 2013; this revealed that 2.1 million person years were lost in Bangalore due to CVD alone. These potential years lost is an important factor to consider, as preventive measures taken by the Government will result in a significant economic impact on the city. The limitations of few monitoring stations were overcome by using spatial interpolation techniques such as Inverse Distance Weighted interpolation technique. The performance of the interpolation was tested using cross-validation techniques and the results revealed that Bangalore city would benefit from increased measuring stations for PM10. The logistic regression conducted showed that pollution especially PM10 was a likely predictor of CVD in the city. Spatial analysis was conducted and included buffering, overlay maps, queries and Hotspot analysis highlighting the zone hotspots. The results from the research guided the development of the novel 5-I model that would assist other similar developing cities to assess the effects of air pollution on CVD. The impetus is that based on evidence, intervention policies and programs may be implemented to inform research and practice which will ultimately have social, economic and health impact on the population. On implementation of the model, hotspots will be identified in order to roll out interventions to priority areas and populations most at risk that will ultimately prevent millions of deaths and enhance overall quality of life.