scholarly journals EVALUATION OF MULTIPLE RETAILERS’ MARKET POSITIONS ON THE BASIS OF IMAGE ATTRIBUTES MEASUREMENT

2005 ◽  
Vol 6 (4) ◽  
pp. 199-206 ◽  
Author(s):  
Sigitas Urbonavičius ◽  
Robertas Ivanauskas
Keyword(s):  
Factor Analysis ◽  
Price Competition ◽  
Perceived Value ◽  
Image Management ◽  
Perceptual Space ◽  
Latent Factors ◽  
Intense Competition ◽  
Customer Perceived Value ◽  
Retail Company

Intense competition in retailing sector requires searching for new and more effective tools of competing with rivals. One of the possible ways seems to go through applying positioning concept in retailing. Positioning in retailing refers to strategy for development of a desirable image, which would help to differentiate a retail company and move away from direct price competition. Besides that, image management provides possibilities for increasing customer perceived value and/or increasing prices. The paper presents methodology for establishing multiple retailers’ positions. This methodology is based on evaluation of image attributes’ importance for customers. Factor analysis allows revealing more general latent factors that are used to evaluate retailers’ positions in a perceptual space. This allows drawing conclusions on how much Lithuanian multiple retailers are similar or differentiated from the standpoint of their customers.

Feature Extraction of Robot Sensor Data Using Factor Analysis for Behavior Learning

2004 ◽  
Vol 8 (3) ◽  
pp. 284-294
Author(s):  
Wai-keung Fung ◽  
◽  
Yun-hui Liu
Keyword(s):  
Factor Analysis ◽  
Feature Extraction ◽  
Sensor Data ◽  
Range Data ◽  
Perceptual Space ◽  
Latent Factors ◽  
New Approach ◽  
Robot Behavior ◽  
Behavior Learning ◽  
Sensor Information

The paper addresses feature extraction of sensor data for robot behavior learning using factor analysis. Redundancies in sensor types and quantities are common in sensing competence of robots. The redundancies cause the high dimensionality of the perceptual space. It is impractical to incorporate all available sensor information in decision-making and learning of robots due to the huge memory and computational requirements. This paper proposes a new approach to extract important knowledge from sensor data based on the inter-correlation of sensor data using factor analysis and construct logical perceptual space for robot behavior learning. The logical perceptual space is constructed by hypothetical latent factors extracted using factor analysis. Since the latent factors extracted have fewer dimensions than raw sensor data, using the logical perceptual space in behavior learning would significantly simplify the learning process and architecture. Experiments have been conducted to demonstrate the process of logical perceptual space extraction from ultrasonic range data for robot behavior learning.

scholarly journals INVESTIGATING PASSENGERS’ PERCEIVED VALUE OF FULL SERVICE AIRLINES AND LOW-COST CARRIERS

2014 ◽  
Vol 5 (2) ◽  
pp. 23-34
Author(s):  
Jin-Long Lu
Keyword(s):  
Factor Analysis ◽  
Regression Model ◽  
Survey Data ◽  
Low Cost ◽  
Perceived Value ◽  
Trade Off ◽  
Customer Perceived Value ◽  
Low Cost Carriers ◽  
Low Cost Carrier ◽  
Service Contact

This study investigates the customer perceived value seen by Taiwanese passengers who flew between Taipei and Singapore regarding full service airlines and low-cost carriers. We collected a sample of passenger survey data at Taiwan Taoyuan International Airport. Five constructs, forming into customer perceived value, namely, nonmonetary cost, perception of monetary cost, reputation, service quality, and service contact are identified based on the results of factor analysis. A regression model is then adopted to measure the relationships between customer perceived value and potential determinants. Passengers from traditional airlines determined their perceived value based mostly on what they gained from the airlines. Nevertheless, passengers formed their perceived value for the low-cost carrier, Jetstar in this study, according to the trade-off between what they gave, especially the nonmonetary cost, and what they received.

The Cross-Cultural Equivalence of Parental Rearing Measure: EMBU-C

2006 ◽  
Vol 22 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Maja Deković ◽  
Margreet ten Have ◽  
Wilma A.M. Vollebergh ◽  
Trees Pels ◽  
Annerieke Oosterwegel ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Cultural Differences ◽  
Cross Cultural ◽  
Child Rearing ◽  
Latent Factors ◽  
Immigrant Adolescents ◽  
Confirmatory Factor ◽  
Parental Rearing ◽  
The Cross ◽  
Multigroup Confirmatory Factor Analysis

We examined the cross-cultural equivalence of a widely used instrument that assesses perceived parental rearing, the EMBU-C, among native Dutch and immigrant adolescents living in The Netherlands. The results of a multigroup confirmatory factor analysis indicated that the factor structure of the EMBU-C, consisting of three latent factors (Warmth, Rejection, and Overprotection), and reliabilities of these scales are similar in both samples. These findings lend further support for the factorial and construct validity of this instrument. The comparison of perceived child rearing between native Dutch and immigrant adolescents showed cultural differences in only one of the assessed dimensions: Immigrant adolescents perceive their parents as more overprotective than do Dutch adolescents.

A decision making model based on fuzzy inference to predict the impact of SCOR® indicators on customer perceived value

2020 ◽  
Vol 223 ◽  
pp. 107520
Author(s):  
Lucas Gabriel Zanon ◽  
Rafael Ferro Munhoz Arantes ◽  
Lucas Daniel Del Rosso Calache ◽  
Luiz Cesar Ribeiro Carpinetti
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Perceived Value ◽  
Model Based ◽  
Customer Perceived Value ◽  
Decision Making Model ◽  
The Impact

scholarly journals Bayesian cumulative shrinkage for infinite factorizations

Biometrika ◽  
2020 ◽  
Vol 107 (3) ◽  
pp. 745-752 ◽  
Author(s):  
Sirio Legramanti ◽  
Daniele Durante ◽  
David B Dunson
Keyword(s):  
Monte Carlo ◽  
Factor Analysis ◽  
Monte Carlo Algorithm ◽  
Model Complexity ◽  
Latent Factors ◽  
Shrinkage Process ◽  
Shrinkage Prior ◽  
Performance Gains ◽  
Shrinkage Priors ◽  
Model Dimension

Summary The dimension of the parameter space is typically unknown in a variety of models that rely on factorizations. For example, in factor analysis the number of latent factors is not known and has to be inferred from the data. Although classical shrinkage priors are useful in such contexts, increasing shrinkage priors can provide a more effective approach that progressively penalizes expansions with growing complexity. In this article we propose a novel increasing shrinkage prior, called the cumulative shrinkage process, for the parameters that control the dimension in overcomplete formulations. Our construction has broad applicability and is based on an interpretable sequence of spike-and-slab distributions which assign increasing mass to the spike as the model complexity grows. Using factor analysis as an illustrative example, we show that this formulation has theoretical and practical advantages relative to current competitors, including an improved ability to recover the model dimension. An adaptive Markov chain Monte Carlo algorithm is proposed, and the performance gains are outlined in simulations and in an application to personality data.

Mobile communications: a comprehensive hierarchical modelling approach

2014 ◽  
Vol 26 (1) ◽  
pp. 114-146 ◽  
Author(s):  
Michael Daniel Clemes ◽  
Xin Shu ◽  
Christopher Gan
Keyword(s):  
Customer Satisfaction ◽  
Service Quality ◽  
Customer Loyalty ◽  
Mobile Communications ◽  
Switching Costs ◽  
Important Determinant ◽  
Perceived Value ◽  
Corporate Image ◽  
Content Type ◽  
Customer Perceived Value

Purpose – Global mobile communication is one of the most dynamic and important service markets. Several researchers suggest using a theoretical approach to develop a much deeper insight into key marketing constructs such as service quality, customer perceived value, customer satisfaction, perceived switching costs, corporate image, and customer loyalty is of vital importance to the mobile communications market. This study aims to develop and test a comprehensive hierarchical model of these six important constructs. The model also incorporates the retailing function of a major mobile communication provider. Design/methodology/approach – The research sample of 516 was drawn from customers of one of the largest mobile communications service providers in China. The data were analysed using exploratory factor analysis, confirmatory factor analysis and structural equation modelling. Findings – The results of the study support using a hierarchical and multidimensional approach for conceptualising and measuring customers' perceptions of service quality in the mobile communications market. In addition, the findings illustrate that service quality is an important determinant of customer perceived value, customer satisfaction, corporate image, and perceived switching costs. Customer perceived value is also an antecedent of customer satisfaction. Corporate image, customer satisfaction, and perceived switching costs are three key drivers of customer loyalty. However, the findings also indicate that corporate image is not an important determinant of customer satisfaction and that customer perceived value is not a key driver of customer loyalty. Originality/value – This is the first paper that has developed and tested a comprehensive hierarchical model of the mobile communications market.

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