scholarly journals Factors for Marketing Innovation in Portuguese Firms CIS 2014

2019 ◽  
Vol 24 (4) ◽  
pp. 99 ◽  
Author(s):  
Patrícia Monteiro ◽  
Aldina Correia ◽  
Vítor Braga
Keyword(s):  
Discriminant Analysis ◽  
Intellectual Property Rights ◽  
Organizational Innovation ◽  
Academic Research ◽  
Statistical Techniques ◽  
Technological Innovations ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Marketing Innovation ◽  
Dummy Variable

Globalization, radical and frequent changes as well as the increasing importance of applying knowledge through the efficient implementation of innovation is critical under the current circumstances. Innovation has been the source of businesses competitive advantage, but it is not restricted to technological innovations, and thus marketing innovation also plays a central role. This is a significant topic in the marketing field and not yet deeply analysed in academic research. The main objective of this study is to understand what factors influence marketing innovation and to establish a business profile of firms that innovate or do not in marketing. We used multivariate statistical techniques, such as, multiple linear regression (with the Marketing Innovation Index as dependent variable) and discriminant analysis where the dependent variable is a dummy variable indicating if the firm innovates or not in marketing. The results suggest that there are several factors explaining marketing innovation, although in this study, we find that the factors contributing the most for marketing innovation are: the Organizational Innovation Index, customer and/or user suggestions, and intellectual property rights and licensing (IPRL). Most of the literature has studied these factors separately. This research studied such factors together, and it is clear that both organizational innovation and IPRL play an important role that drives firms to innovate in marketing, which differs from some literature; customer suggestions help in the process of marketing innovation, as some authors argue that customers do not always know what they want until they have it. In parallel, this study proved to be useful in understanding that the different values for the Marketing Innovation Index display no influence on the results, since they were equivalent when a dummy variable (innovated/not innovated in marketing) was used as a dependent variable. In practice, we realize that the factors are useful to clarify what Portuguese firms innovate or not in marketing, with no different results when we the four marketing innovation levels (design, distribution, advertising and price) are considered.

Multivariate statistic for researchers

2016 ◽  
Author(s):  
Shamshuritawati Sharif ◽  
Hazlina Ali ◽  
Sharipah Soaad Syed Yahaya
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Valuable Resource ◽  
Multivariate Statistical ◽  
Multivariate Statistic ◽  
Linear Discriminant ◽  
New Ideas ◽  
Young Researchers

This book is a valuable resource for those engaged in multivariate statistical techniques. Most chapters include a set of problems and solution that enable readers to overcome the drawback of the classical techniques.It covers a theoretical disadvantage of correlation and covariance test, Hotellings T2 statistic, local influence, and linear discriminant analysis to inspire new or young researchers with new ideas for theoretical improvement.This book is also worthy for people who want to learn multivariate statistics extensively.

Multivariate Statistical Analysis of a Regional Subsistence-Settlement Model for Owens Valley

1979 ◽  
Vol 44 (3) ◽  
pp. 455-470 ◽  
Author(s):  
Robert L. Bettinger
Keyword(s):  
Factor Analysis ◽  
Statistical Analysis ◽  
Discriminant Analysis ◽  
Human Ecology ◽  
Multivariate Statistical Analysis ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Owens Valley ◽  
Multivariate Statistical

Despite their growing importance in the study of prehistoric human ecology, regional subsistence-settlement models continue to be developed and justified largely on intuitive grounds. This shortcoming can be at least partially overcome by using multivariate statistical techniques to clarify and refine these models. Such an approach is illustrated using classical factor analysis and discriminant analysis to explicate and improve a regional subsistence-settlement model previously developed for Owens Valley, eastern California.

scholarly journals Water quality assessment and source identification of the Shuangji River (China) using multivariate statistical methods

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245525
Author(s):  
Junzhao Liu ◽  
Dong Zhang ◽  
Qiuju Tang ◽  
Hongbin Xu ◽  
Shanheng Huang ◽  
...  
Keyword(s):  
Water Quality ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Water Quality Assessment ◽  
Temporal Analysis ◽  
Statistical Techniques ◽  
Complex Data ◽  
Monitoring Networks ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Multivariate statistical techniques, including cluster analysis (CA), discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA), were used to evaluate temporal and spatial variations in and to interpret large and complex water quality datasets collected from the Shuangji River Basin. The datasets, which contained 19 parameters, were generated during the 2 year (2018–2020) monitoring programme at 14 different sites (3192 observations) along the river. Hierarchical CA was used to divide the twelve months into three periods and the fourteen sampling sites into three groups. Discriminant analysis identified four parameters (CODMn, Cu, As, Se) loading more than 68% correct assignations in temporal analysis, while seven parameters (COD, TP, CODMn, F, LAS, Cu and Cd) to load 93% correct assignations in spatial analysis. The FA/PCA identified six factors that were responsible for explaining the data structure of 68% of the total variance of the dataset, allowing grouping of selected parameters based on common characteristics and assessing the incidence of overall change in each group. This study proposes the necessity and practicality of multivariate statistical techniques for evaluating and interpreting large and complex data sets, with a view to obtaining better information about water quality and the design of monitoring networks to effectively manage water resources.

Assessing exercises recommended for women at risk of pelvic floor disorders using multivariate statistical techniques

2017 ◽  
Vol 29 (10) ◽  
pp. 1447-1454 ◽  
Author(s):  
Tania Tian ◽  
Stephanie Budgett ◽  
Jackie Smalldridge ◽  
Lynsey Hayward ◽  
James Stinear ◽  
...  
Keyword(s):  
At Risk ◽  
Pelvic Floor ◽  
Pelvic Floor Disorders ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical

Interactions of 2-PPM Lead in the Water Supply with Food Deprivation upon Maze-Swimming Behavior of Mice

1982 ◽  
Vol 55 (2) ◽  
pp. 515-519 ◽  
Author(s):  
S. Kowalski ◽  
G. H. Parker ◽  
M. A. Persinger
Keyword(s):  
Drinking Water ◽  
Water Supply ◽  
Food Deprivation ◽  
Tap Water ◽  
Swimming Behavior ◽  
Statistical Techniques ◽  
Multivariate Statistical Techniques ◽  
Multivariate Statistical ◽  
Food Condition ◽  
Ad Libitum

Mice that had been given either tap water or 2 ppm lead in their drinking water and either severely food deprived (3 days before testing) or allowed food ad libitum demonstrated significant interactions of lead treatment by day by food condition and lead by block. Although not statistically significant, the food deprived-lead treated mice displayed more errors and longer latencies than the ad libitum-water controls. The food deprived-water controls and ad libitum-lead-treated mice displayed intermediate values. The importance of using multivariate statistical techniques that can evaluate dynamic repeated behavioral measurements is emphasized.

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