Range Effects in Measuring Attribute Importance

Marketing ZFP ◽  
2009 ◽  
Vol 31 (JRM 1) ◽  
pp. 17-26 ◽  
Author(s):  
Karen Gedenk ◽  
Henrik Sattler
Keyword(s):  
Languages ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 32
Author(s):  
Fanny Forsberg Lundell ◽  
Klara Arvidsson

Adult L2 acquisition has often been framed within research on the Critical Period Hypothesis, and the age factor is one of the most researched topics of SLA. However, several researchers suggest that while age is the most important factor for differences between child and adult SLA, variation in adult SLA is more dependent on social and psychological factors than on age of onset. The present qualitative study investigates the role of migratory experience, language use/social networks, language learning experience, identity and attitudes for high performance among Swedish L1 French L2 users in France. The study constitutes an in-depth thematic analysis of interviews with six high-performing individuals and four low-performing individuals. The main results show that the high performers differ from the low performers on all dimensions, except for attitudes towards the host community. High performers are above all characterized by self-reported language aptitude and an early interest in languages, which appears to have led to rich exposure to French. Also, they exhibit self-regulatory behaviors and attribute importance to being perceived as a native speaker of French—both for instrumental and existential reasons.


2002 ◽  
Vol 39 (2) ◽  
pp. 253-261 ◽  
Author(s):  
Frenkel Ter Hofstede ◽  
Youngchan Kim ◽  
Michel Wedel

The authors propose a general model that includes the effects of discrete and continuous heterogeneity as well as self-stated and derived attribute importance in hybrid conjoint studies. Rather than use the self-stated importances as prior information, as has been done in several previous approaches, the authors consider them data and therefore include them in the formulation of the likelihood, which helps investigate the relationship of self-stated and derived importances at the individual level. The authors formulate several special cases of the model and estimate them using the Gibbs sampler. The authors reanalyze Srinivasan and Park's (1997) data and show that the current model predicts real choices better than competing models do. The posterior credible intervals of the predictions of models with the different heterogeneity specifications overlap, so there is no clear superior specification of heterogeneity. However, when different sources of data are used—that is, full profile evaluations, self-stated importances, or both—clear differences arise in the accuracy of predictions. Moreover, the authors find that including the self-stated importances in the likelihood leads to much better predictions than does considering them prior information.


1986 ◽  
Vol 12 (4) ◽  
pp. 463 ◽  
Author(s):  
James Jaccard ◽  
David Brinberg ◽  
Lee J. Ackerman
Keyword(s):  

2017 ◽  
Vol 15 (03) ◽  
pp. 317-340 ◽  
Author(s):  
Lei Zhao ◽  
Theodor Freiheit

Purpose This paper aims to examine the perceptions of good design attributes and propose a model to estimate their relative importance through fundamental drivers. Design activities must understand and meet customer and producer expectations and deliver products in a profitable manner. Requirements analysis is conducted to understand customer expectations, but in new product development, this information can be available too late in the development cycle. Moreover, customer needs are often unclear to designers at early stages of design, with customers often unable to articulate their requirements or unaware of how a new product may solve problems or create complications. Evaluating non-product-specific drivers to generalized good product design attributes can help designers estimate important factors in early requirements analysis. Design/methodology/approach Quantification of the weight designers place in their mental models of what makes up a good product is determined from linear regression modeling, providing a more concrete evaluation of inherently subjective perceptions. A survey is deployed using Mechanical TurkTM to collect perceptions of good product attributes and drivers through product case studies. Data are analyzed using a utility theory framework and importance of attributes is estimated from the importance of drivers. Findings A generalized model that estimates good design attributes from drivers is presented. This study also demonstrates that non-product-specific attribute importance can be extracted from specific product cases. An application example demonstrating the relative importance of good design attributes is given for different types of watches. Research limitations/implications The approach is intended to supplement ordinary product design and development processes, and is not intended to replace market research and concept testing activities. Model coefficient weights are dependent on the quality of the data that was collected, which has limitations. While the current study included confounding variables, introducing interactions into the model could make attribute importance prediction more accurate. Practical implications While design requirements analysis is now central to modern design practice, these estimates can be available too late in the development cycle, especially when customers have no experience with the product type. The developed model quantifies design attributes that consumers, manufacturers and society as a whole use to distinguish if a product will be considered well designed. Product designers can better focus their development resources toward good design attributes based on guidance generated from generalized drivers. Originality/value Historically, requirements analysis is undertaken specific to the product being designed. This paper provides a model to give designers early guidance in a non-product-specific framework. The framework also considers good design attributes as holistic, including societal and producer concerns. Although all of the proposed good design attributes can be associated with a well-engineered product, it is unnecessary to design a product that performs exceptionally on every attribute. This model provides identification of the handful of attributes that can make the most significant difference for design success.


2014 ◽  
Vol 6 ◽  
pp. 547947 ◽  
Author(s):  
Yaohua Deng ◽  
Qiwen Lu ◽  
Jiayuan Chen ◽  
Sicheng Chen ◽  
Liming Wu ◽  
...  

Through analyzing the flexible material processing (FMP) deformation factors, it is pointed out that without a choice of deformation influence quantity would increase the compensation control predict model system input. In order to reduce the count of spatial dimensions of knowledge, we proposed the method by taking the use of FMP deformation compensation control knowledge extraction, which is based on decision table (DT) attribute reduction, deriving the algorithm that is based on information entropy attribute importance, to find the dependencies between attributes through attribute significance (AS) and to extract the intrinsic attributes which is the most close to deformation compensation control decision making. Finally, through an example presented in this paper to verify the efficiency of RS control knowledge extraction method. Compared with the Pawlak method and genetic extraction algorithm, the prediction accuracy of after reduction data is 0.55% less than Pawlak method and 3.64% higher than the genetic extraction algorithm; however, the time consumption of forecast calculation is 30.3% and 11.53% less than Pawlak method and genetic extraction algorithm, respectively. Knowledge extraction entropy methods presented in this paper have the advantages of fast calculating speed and high accuracy and are suitable for FMP deformation compensation of online control.


Sign in / Sign up

Export Citation Format

Share Document