Penalizing Neural Network and Autoencoder for the Analysis of Marketing Measurement Scales in Service Marketing Applications

2021 ◽  
Author(s):  
Toshikuni Sato
Keyword(s):  
Neural Network ◽  
Service Marketing ◽  
Measurement Scales ◽  
Marketing Measurement

Scales in services marketing research: a critique and way forward

2009 ◽  
Vol 43 (5/6) ◽  
pp. 640-651 ◽  
Author(s):  
Audrey Gilmore ◽  
Rosalind McMullan
Keyword(s):  
Design Methodology ◽  
Best Practice ◽  
Marketing Research ◽  
Service Marketing ◽  
Research Problem ◽  
Measurement Scale ◽  
Complex Nature ◽  
Measurement Scales ◽  
Content Type ◽  
Scale Design

PurposeThe purpose of this paper is to discuss the use of measurement scales and to illustrate some of the drawbacks of using scales for measuring service quality without due recognition of the limitations and rigidity of such scales, especially when they are applied to the complexity of service marketing situations and contexts.Design/methodology/approachA review of the most widely used scales in services measurement, including SERVQUAL and SERVPERF is provided, along with some of the conceptual issues surrounding scale design and use in service contexts. Then some qualitative research techniques are considered in terms of their adaptability and flexibility for carrying out research regarding the complex nature of services.FindingsMeasurement scales are evaluated and discussed. The key criticisms of best‐known scales used for services situations are presented. Then consideration is given to what might be a “best practice” scenario for measuring and assessing service‐related issues in a service context.Originality/valueThe discussion draws attention to the importance of recognising the most suitable research method for a service‐specific research problem/question rather than imposing a well known measurement scale or technique that may not suit the purpose.

Autofluorescence spectroscopy of oral leukoplakia using an artificial neural network

2000 ◽  
Vol 25 (4) ◽  
pp. 325-325
Author(s):  
J.L.N. Roodenburg ◽  
H.J. Van Staveren ◽  
N.L.P. Van Veen ◽  
O.C. Speelman ◽  
J.M. Nauta ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Oral Leukoplakia ◽  
Autofluorescence Spectroscopy ◽  
Artificial Neural

1902: Is Steinstrasse After ESWL of Renal Stones Predictable? Artificial Neural Network Analysis

2004 ◽  
Vol 171 (4S) ◽  
pp. 502-503
Author(s):  
Mohamed A. Gomha ◽  
Khaled Z. Sheir ◽  
Saeed Showky ◽  
Khaled Madbouly ◽  
Emad Elsobky ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Renal Stones ◽  
Neural Network Analysis ◽  
Artificial Neural Network Analysis ◽  
Artificial Neural

D-test: A New Test for Analyzing Scale Invariance Using Symbolic Dynamics and Symbolic Entropy

Methodology ◽  
2011 ◽  
Vol 7 (3) ◽  
pp. 88-95 ◽  
Author(s):  
Jose A. Martínez ◽  
Manuel Ruiz Marín
Keyword(s):  
Symbolic Dynamics ◽  
Scale Invariance ◽  
Rating Scales ◽  
Rating Scale ◽  
Marketing Research ◽  
Response Patterns ◽  
Measurement Scales ◽  
Improve Measurement ◽  
Entropy Measures ◽  
The Difference

The aim of this study is to improve measurement in marketing research by constructing a new, simple, nonparametric, consistent, and powerful test to study scale invariance. The test is called D-test. D-test is constructed using symbolic dynamics and symbolic entropy as a measure of the difference between the response patterns which comes from two measurement scales. We also give a standard asymptotic distribution of our statistic. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. We applied D-test to a real marketing research to study if scale invariance holds when measuring service quality in a sports service. We considered a free-scale as a reference scale and then we compared it with three widely used rating scales: Likert-type scale from 1 to 5 and from 1 to 7, and semantic-differential scale from −3 to +3. Scale invariance holds for the two latter scales. This test overcomes the shortcomings of other procedures for analyzing scale invariance; and it provides researchers a tool to decide the appropriate rating scale to study specific marketing problems, and how the results of prior studies can be questioned.

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