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In over twenty years of operations the Canadian Innovation Centre has evaluated, through its Inventor's Assistance Program (IAP), the commercial potential of over 12,000 early stage inventions. Prior to 1989, the Canadian Innovation Centre (CIC) used a version of the Preliminary Innovation Evaluation System (PIES), developed by Gerald Udell at the Oregon Innovation Center in 1974, to evaluate the commercial potential of early stage inventions. Since 1989, the CIC has used a modified version of PIES in their evaluation process. I first estimate the ability of this program's analysts to forecast the probability that an invention will become commercialized. I also estimate a model to predict the probability that an invention will become commercialized based on the IAP's evaluation of several underlying early stage characteristics of the invention. I find that such a statistical model is based on a limited set of variables and predicts future commercial success almost as well as the IAP's forecast of success. I then use factor analysis to determine if the ratings provided by the CIC evaluation service are representative of the underlying theoretical variable structure of PIES or their modified version. Factor analysis is applied to two distinct periods that are separated by a distinct alteration of the theoretical variable structure in 1989. While I find that the factor analysis provides evidence that the post 1989 theoretical structure does provide interpretation of some of the dimensions in the ranking variables, when a combination of the post 1989 and the pre 1989 structure are examined interpretability of the extracted factors is significantly improved. Finally, I compare the model estimated on the underlying early stage characteristics with a model estimated on the extracted factors. When the predictive accuracy of the two models is compared, I find that both procedures produce models that predict almost equally well. The models and the IAP perform better than R&D managers' predictions of their own R&D projects' successes. The thesis provides recommendations for the assessment and maintenance of evaluation models for inventions, innovations and R&D projects.

Using seventeen years of data from 1991 to 2008, I derive a pricing model for farmland values. This valuation model is the first using agricultural commodity futures as a proxy for “ex ante” income projections for the crops grown or livestock grazed on United States farmland. While not all inclusive, the model is tested regionally including the Corn Belt, Delta States, Lake States, Mountain, Pacific Northwest, Pacific West and Southeast Regions. Additionally, I test whether interest rate futures contracts have a relationship with farmland values as interest rates have been proven to be a reliable predictor in past research. Farmland capitalization rates and anticipated inflation have hypothesized relationships, but are mainly used as control variables in the study. In general, agricultural commodity futures contracts are a poor predictor of changes in farmland market values. When examining relationships with quarterly percentage change regression models of the included variables, I find the Mountain region provides the most reliable pricing model where both live cattle and Minnesota wheat futures contracts has a positive statistically significant relationships with farmland market values. Also, wheat futures prices have a significant relationship with farmland values in the Corn Belt region. Interest rate futures contracts, farmland capitalization rates and anticipated inflation are not statistically significant in the majority of the regions. As a robustness check, I model the price levels of the variables using Johansen’s cointegration procedure. This time-series econometric methodology provides results in regards to long-run equilibrium relationships between the variables. The results are only slightly better. Corn, orange juice and sugar futures contracts have positive statistically significant relationships with farmland market values in multiple regions. Again, wheat has a statistically significant positive relationship with farmland values in the Corn Belt region. The Mountain region and interest rate futures contracts are not applicable for the cointegration tests as they are not integrated to the order of one.

In over twenty years of operations the Canadian Innovation Centre has evaluated, through its Inventor's Assistance Program (IAP), the commercial potential of over 12,000 early stage inventions. Prior to 1989, the Canadian Innovation Centre (CIC) used a version of the Preliminary Innovation Evaluation System (PIES), developed by Gerald Udell at the Oregon Innovation Center in 1974, to evaluate the commercial potential of early stage inventions. Since 1989, the CIC has used a modified version of PIES in their evaluation process. I first estimate the ability of this program's analysts to forecast the probability that an invention will become commercialized. I also estimate a model to predict the probability that an invention will become commercialized based on the IAP's evaluation of several underlying early stage characteristics of the invention. I find that such a statistical model is based on a limited set of variables and predicts future commercial success almost as well as the IAP's forecast of success. I then use factor analysis to determine if the ratings provided by the CIC evaluation service are representative of the underlying theoretical variable structure of PIES or their modified version. Factor analysis is applied to two distinct periods that are separated by a distinct alteration of the theoretical variable structure in 1989. While I find that the factor analysis provides evidence that the post 1989 theoretical structure does provide interpretation of some of the dimensions in the ranking variables, when a combination of the post 1989 and the pre 1989 structure are examined interpretability of the extracted factors is significantly improved. Finally, I compare the model estimated on the underlying early stage characteristics with a model estimated on the extracted factors. When the predictive accuracy of the two models is compared, I find that both procedures produce models that predict almost equally well. The models and the IAP perform better than R&D managers' predictions of their own R&D projects' successes. The thesis provides recommendations for the assessment and maintenance of evaluation models for inventions, innovations and R&D projects.

¡@¡@According to statistics relesed by Ministry of Economic Affairs (MOEA), non-store retailing market in Taiwan, including TV shopping, online shopping, catalog sales (mail order) and door-to-door selling, grew from NT$112.3 billion in 2005 to NT$152.2 billion in 2009, and the production value has grown more than 30%. The ratio of it to the total retail sales had risen from 3.6% to 4.6%. The performance is incredible. ¡@¡@Among non-store retailing, TV shopping is the fastest growing new method over the past few years. In Taiwan's TV shopping industry, the market size turned from a TV shopping company with NT$0.5 billion of production value in 2000 to three TV shopping companies with NT$51 billion in 2009. In early 2010, the entering of new shopping company, U-Life, caused a new competition among the TV shopping companies. In the meanwhile, sales of TV shopping industry in Mainland China had reached CNY$ 20 billion by 2008. In the next decade, the the growth of total retailing sales, market size of TV shopping in China will account for around 3% of the total, up to 500 CNY$ billion. ¡@¡@TV shopping presents the goods to the audience through the high penetration of television media, just like opening a department store directly in TV, in order to let the audience become the consumers. Comparing to store-based retailing which has been developed, the sales mathod of TV shopping industry created an innovative access to the general consumers. Therefore, this thesis explores among the service processes what infuences the consumer satisfaction while the consumers are undergoing TV shopping based on TV shopping industry in Taiwan and Mainland China, and analyzes the differences of consumer satisfaction toward TV shopping between Taiwan and Mainland China. ¡@¡@According to the reasons above, this research will first discuss the TV shopping industry development and take the consumers who have purchased any goods by TV shopping as the research targets and conduct telephone interviewing with the questionnaire and analyze the result. ¡@¡@The research shows that with the improvement of the service quality, not full-scale, but to reach the customer requirement, the sales performance will be improved with limited resources. Whether in Taiwan and Mainland China, service quality, product quality and delivery quality are the important factors affecting customer satisfaction. In Taiwan, consumer's repeat purchase intention is more influenced by the back-end process services¡Fin Mainland China, consumer's repeat purchase intention is more influenced by the front-end process of service.

Rolling technology can control the thickness precisely. But it isn¡¦t available in width control. FEM software DEFORM 3D is adopted to simulate one stand of the hot finishing rolling process using carbon steel as sheet material. Stress, strain and width spread are discussed. Regression analysis is used to find an empirical equation for rolling load and width spread. Experiment and simulation of cold strip rolling using aluminum as sheet material are conducted,discussing the width spread and detail of width transformation in DEFORM 3D.From the comparisons between analytical and experimental width spread, the analytical model and prediction equation are validated.

The distance of sound propagation in water is inversely proportional to the frequency; there for the low frequency noise generated by distance shipping contribute most to the low frequency components of the ocean ambient noise. In order to improve the performance of sonar application, understanding and predication shipping noise is important. In this study, noise data was collected by vertical line array (VLA), in South China Sea of Asian Seas International Acoustics Experiment (ASIEX) in 2001. Due to the limited access to the satellite imagery, and based on the assumption that fishing vessels in South China Sea has similar operational pattern and lighting in recent years, the night time satellite image data was acquired by Defense Meteorological Satellite Program DMSP - Operational Linescan System(DMSP-OLS) of 2005. Linear regression was analyzed between fishing light pixel distribution and low frequency noise level variation, fishing light was expressed in terms of total pixel number and total light intensity, and distance from fishing vessel to VLA was assumed by spherical and cylindrical spread. The results show high correlation between total light intensity and noise level, and cylindrical spreading is better assumption in shallow water on the continental shelf at low frequency. As a conclusion, this study successfully prove that by using the night satellite image, the low frequency fishing vessel noise can be predicted with reasonable accuracy.

The research of corporate financial distress prediction model is always one of the important topics in financial management; and mostly people do the research and extract sample companies based on the definition for corporate default by Taiwan Economic Journal. However, we think the timing to observe the potential corporate financial distress is extremely vital; the actual benefit will not be good even with high accuracy if relevant counterparties recognize it too late to undertake certain action for mitigating loss. The main purpose of this study is trying to alert potential corporate financial distress as early as possible, and then could contribute some to this topic. This study extracts 34 financial alerted sample companies with share prices plumped by 50% dramatically or alternatively with share prices diminished below their face value while the stock market index rose in 2007. We matched each sample company by another financially healthy company from the same industry, chose 25 financial ratios to be the variables, and running through each year by adopting logistic regression analysis. We put all variables into the regression formula and weeded out insignificant prediction variables one by one by Wald Backward Elimination, and then sieved out relatively meaningful ones. The first conclusion of this study is that we should use quarter as the financial intervals for this type of sample companies. Secondly, we found that in December and September 2007 there were three significant variables, i.e. Return on Equity (ROE), net income, operational profit ratio, inventory and account receivable to equity ratio. Thirdly, there were three significant variables in June 2007, i.e. earning before tax ratio, growth ratio of operational profit and total liability/ total equity.

This paper provides a credit risk quantification system for banks to estaminate the credit risk of loans to small and mediume nterprises(SMEs). As we know, the most difficult thing for banks to handle SME loans is whose financial reporting lacks transparency and no valuable reference. We use non-financial variables and employ the logisitic regression to develop the credit risk predict model. We concludet: first, when construct a SMEs credit rating system, non-financial factors should be seriously considered and adopted. Second, because of positioned different stage of firm life cycle, the credit rating model should be set up differently by different stage of firm. Third, SME loans should to make much of establishing ¡§relationship-based¡¨ in order to meet the various demands of risk management.

<p>The real estate market has been of great interest since the rise in home foreclosures in</p><p>US, which started in the late 2006. The purpose of this thesis is to examine a possible</p><p>relationship between the factors presented in DiPasquale and Wheaton’s (1996) model</p><p>which explains the market linkages between the property market and asset market, and</p><p>the Swedish real estate companies listed on the Swedish stock market OMX. The real</p><p>estate stock market is, divided in to groups of 3, which represented the dependent</p><p>variable. The repo rate, CPI, expected inflation, macro index, disposable income, GDP</p><p>and a real estate price index are the explanatory variables. Stockholm Stock Market All-</p><p>Share Index (OMXSPI) is also included as a possible explanatory variable.</p><p>The main findings in most of the estimations for the groups and years, is that the</p><p>OMXSPI is of significance at the 10 percent level. The other variables did not show any</p><p>significant result based on the 10 percent significance level,</p><p>According to the results it seems like the volatility has increased over time in the real</p><p>estate stock market with respect to the OMXSPI. That is; the risk has increased</p><p>significantly from the period 1996-1999 to the later periods.</p>