Innovativeness is one of the fundamental instruments of growth strategies that provide companies with a competitive edge. Only a few recent studies have examined marketing innovation and the factors that might encourage its adoption. This study investigates the factors that motivate marketing innovation and examines whether the occurrence of marketing innovation varies across industry sectors. This study uses data from surveys and a nationwide census conducted by Statistics Canada. They include: the Survey of Innovation and Business Strategies (SIBS) 2009, the Survey of Innovation and Business Strategies (SIBS) 2012, the Business Registry (BR) and the General Index of Financial Information (GIFI). Multilevel (random-intercept) logistic regression modelling is employed. The results show that if a firm has a strategic focus on new marketing practices, maintains marketing within its enterprise, acquires or expands marketing capacity, has competitor and customer orientations, and adopts advanced technology then it is more likely to carry out marketing innovation. However, breadth of long-term strategic objectives and competitive intensity do not have significant impacts on marketing innovation. In addition, product innovation and organizational innovation occur simultaneously with marketing innovation, but process innovation may not. Lastly, the occurrence of marketing innovation is found to vary across industry sectors. The theoretical and empirical implications of the results are discussed within this study.

The purpose of this thesis was to compare two estimation procedures, the method of least squares and the method of maximum likelihood, on sample data obtained from a Poisson distribution. Point estimates of the slope and intercept of the regression line and point estimates of the mean squared error for both the slope and intercept were obtained. It is shown that least squares, the preferred method due to its simplicity, does yield results as good as maximum likelihood. Also, confidence intervals were computed by Monte Carlo techniques and then were tested for accuracy. For the method of least squares, confidence bands for the regression line were computed under two different assumptions concerning the variance. It is shown that the assumption of constant variance produces false confidence bands. However, the assumption of the variance equal to the mean yielded accurate results.

Intercept feeding was tested for its efficacy in reducing deer-vehicle collision frequency by diverting deer movement patterns away from highways using alfalfa hay as an attractant. Ratios of road kills in control vs. treatment zones of three highway segments indicated that feeding reduced collision frequency. Spotlighting counts of live deer were significantly higher (P

Early-successional bird species are showing alarming declines across the Northeast and particularly in New England. Utilizing limited resources to the best advantage of these declining bird species is a vital task for land managers. In 2006 and 2007, I collected bird abundance and habitat information from 87 points in early-successional habitat in Connecticut. The objective of this effort was to evaluate the relationships between the habitat variables collected at a plot using the point intercept method and the associated bird abundance at the plot. A second objective was to compare two different methods of characterizing early-successional habitat in explaining the variance in bird abundance. A plot-based method based on the BBIRD protocol from Montana Cooperative Wildlife Research Unit and the point intercept method were compared. Finally, I designed and created a database written in Microsoft Access which was used to standardize data entry, aid in the sharing of data and to calculate summary statistics to assist habitat managers in making conservation decisions. The habitat variables were grouped according to composition and structure to analyze bird-habitat relationships. Low broadleaved shrubs, broadleaved shrubs, fern/forbs, conifers, broadleaved trees and invasives, as well as average height for shrubs and trees were used for the analysis. Nine focal early-successional species that are showing general trends of decline were chosen from the list of all birds seen or heard. Bird abundance and detectibility covariates were modeled with the habitat variables using N-mixture models (2004). Up to 24% of the variation of the best models (based upon AICc) was explained by the predictors I investigated. Five of the 9 birds showed a positive correlation to a shrub category variable. Fern/forbs, graminoids and invasives were found to exert less influence on the abundance of these scrub-shrub birds. Results indicated that the date of the survey affected the detectibility of only 5 of the species, and vegetation height only affected one of the species. Overall correlations indicate that these nine shrubland dependent species utilize a structurally complex habitat including broadleaved shrubs less 2 meters in height and than 2-5 meters in height and herbaceous forbs and graminoids. Invasive plants were found to be positively correlated to 2 of the 9 species possibly warranting additional work on the affects of these species on early-successional birds. Thirty-one of the total 87 point count points were selected for the comparison between the BBIRD and point intercept method. I choose six focal early-successional species for the analysis: indigo bunting, blue-winged warbler, chestnut-sided warbler, yellow warbler, prairie warbler and the common yellowthroat. The point intercept and BBIRD methods explained on average the same amount of variability in the data, and models from each data set included nearly the same number of variables, on average. Thus, we conclude these two vegetation sampling methodologies were essentially equivalent in summarizing important characteristics of scrub-shrub bird habitats. In the field, the BBIRD method took on average almost twice as long to complete as the point intercept method. Because in this study the two methods were similar in the amount of the bird abundance variance they explained and because the BBIRD method takes substantially longer to complete, I recommend that the point intercept method be considered an acceptable method for managers to use to characterize the relationships between early-successional bird species and their habitat. An important step in the successful conservation of declining early-successional bird species is the creation of database management systems and the coordination and cooperation amongst agencies that can stem from the use of these databases. The database I created ensures standardized data entry for data collected from multiple sites over many years. The database takes this data and can be queried for whatever particular information a manager needs. Percent cover of vegetation and invasives, average height of vegetation, and bird abundance are summarized and graphically displayed by the database. Ease of operation, ability to query and ability to share the information makes this database an important tool in the successful conservation of declining species

Three common methods have been used to assess the existence of rating bias in performance ratings: the total association approach, the differential constructs approach and the direct effects approach. One purpose of this study was to examine how the direct effects approach, and more specifically differential prediction analysis, is more useful than the other two approaches in examining the existence of rating bias. However, the usefulness of differential prediction depends on modeling the full rater race X ratee race interaction. Therefore, the second purpose of this study was to examine the conditions where differential prediction has sufficient power to detect this interaction. This was accomplished using monte carlo simulations. Total sample size, magnitude of rating bias, validity of predictor scores, rater race proportion and ratee race proportion were manipulated to identify which conditions of these parameters provided acceptable power to detect the rater race X ratee race interaction; in the conditions where power levels are acceptable, differential prediction is a useful tool in examining the existence of rating bias. The simulation results suggest that total sample size, magnitude of rating bias and rater race proportion have the most impact on power levels. Furthermore, these three parameters interact to effect power. Implications of these results are discussed. / Master of Science

Electronic Support Measures is a discipline of electronic warfare. In electronic support measures, receivers must maintain surveillance over the very wide portion of the electromagnetic spectrum in which threat emitters operate. In current receiver technology, it is not possible to have a receiver which is at once both able to discriminate multiple simultaneous emissions and highly sensitive. A common approach is to use a receiver with a relatively narrow bandwidth that sweeps its centre frequency over the threat bandwidth to search for emitters. The sequence and timing of changes in the centre frequency constitute a search strategy or sensor scheduling problem. A good electronic support receiver should observe the threat emitters, usually radars, very soon after it first begins transmitting, so in designing search strategy we would like to ensure that the intercept time is low or the probability of intercept after a specified time is high. In this thesis, we study the search strategies used in electronic support measures receivers. Moreover, a search strategy based on probability of intercept of the threats is proposed. The performances of the search strategies are compared at the end of the thesis.

Today, there is a demand for reliable ways to perform automatic modulation recognition of Low Probability of Intercept (LPI) radar signals, not least in the defense industry. This study explores the possibility of performing automatic modulation recognition on these signals through clustering and more specifically how to learn representations of input signals for this task. A semi-supervised approach using a bootstrapped convolutional neural network classifier for representation learning is proposed. A comparison is made between training the representation learner on raw time-series and on spectral representations of the input signals. It is concluded that, overall, the system trained on spectral representations performs better, though both approaches show promise and should be explored further. The proposed system is tested both on known modulation types and on previously unseen modulation types in the task of novelty detection. The results show that the system can successfully identify known modulation types with adjusted mutual information of 0.86 for signal-to-noise ratios ranging from -10 dB to 10 dB. When introducing previously unseen modulations, up to six modulations can be identified with adjusted mutual information above 0.85. Furthermore, it is shown that the system can learn to separate LPI radar signals from telecom signals which are present in most signal environments. / Idag finns ett behov av pålitlig automatiserad modulationsigenkänning (AMR) av Low Probability of Inercept (LPI)-radarsignaler, inte minst hos försvarsindustrin. Denna studie utforskar möjligheten att utföra AMR av dessa signaler genom klustring och mer specifikt hur man bör lära in representationer av signalerna i detta syfte. En halvövervakad inlärningsmetod som använder en klassificerare baserad på faltningsnätverk föreslås. En jämförelse görs mellan ett system som tränar för representationsinlärning på råa tidsserier och ett system som tränar på spektrala representationer av signalerna. Resultaten visar att systemet tränat på spektrala representationer på det stora hela presterar bättre, men båda metoderna visar lovande resultat och bör utforskas vidare. Systemet testas på signaler från både kända och för systemet tidigare okända modulationer i syfte att pröva förmågan att upptäcka nya typer av modulationer. Systemet identifierar kända modulationer med adjusted mutual information på 0.86 i brusnivåer från -10 dB till 10 dB. När tidigare okända modulationer introduceras till systemet ligger adjusted mutual information över 0.85 för upp till sex modulationer. Studien visar dessutom att systemet kan lära sig skilja LPI-radarsignaler från telekommunikationssignaler som är vanliga i de flesta signalmiljöer.

After the U.S. Civil Rights Movement of the 1960s and the desegregation of public transportation and facilities and with the advancements that African Americans have gained in education, income, and employment, African Americans have greater access to travel opportunities. Today's African Americans travel in greater numbers than ever before and represent a dynamic and growing travel market segment that according to the Travel Industry Association of America (TIA) (2003) generated 75 million person trips in 2002. Although there have been several studies conducted on the differences between ethnic or racial groups with regard to their participation in outdoor recreation, research on differences between African American and White traveler behaviors in the urban tourism context is sparse. This study examined the differences between African American and White travelers who visited the city of New Orleans. Specifically, the study investigated demographic variables (income and gender) for their contribution to the differences between African American and White travelers in the modes of travel, activities participated in, sources of travel information, importance of destination activities, satisfaction with destination attributes, and spending patterns. Significant differences were found in modes of travel, activities participated in, sources of information, information of destination attributes findings (popular, African American Values, and sport and recreation), satisfaction with New Orleans on the destination attributes entertainment, African American Values, and spending. Although the findings of this study reflect the trip characteristics of travelers to New Orleans, future research should examine the applicability to other urban tourism destinations.

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This paper examines the forecast accuracy of an unrestricted Vector Autoregressive (VAR) model for GDP, relative to a comparable Vector Error Correction (VEC) model that recognizes that the data is characterized by co-integration. In addition, an alternative forecast method, Intercept Correction (IC), is considered for further comparison. Recursive out-of-sample forecasts are generated for both models and forecast techniques. The generated forecasts for each model are objectively evaluated by a selection of evaluation measures and equal accuracy tests. The result shows that the VEC models consistently outperform the VAR models. Further, IC enhances the forecast accuracy when applied to the VEC model, while there is no such indication when applied to the VAR model. For certain forecast horizons there is a significant difference in forecast ability between the VEC IC model compared to the VAR model.