Pairwise Conjoint Analysis of Activity Engagement Choice

10.1068/a3267 ◽  
2000 ◽  
Vol 32 (5) ◽  
pp. 805-816 ◽  
Author(s):  
Donggen Wang ◽  
Harmen Oppewal ◽  
Harry Timmermans
Keyword(s):  
Conjoint Analysis ◽  
Information Overload ◽  
Design Strategy ◽  
Choice Models ◽  
New Approach ◽  
Activity Engagement ◽  
Conjoint Choice ◽  
Attribute Levels ◽  
Preference Models ◽  
Modelling Approach

Information overload is a well-known problem of conjoint choice models when respondents have to evaluate a large number of attributes and/or attribute levels. In this paper we develop an alternative conjoint modelling approach, called pairwise conjoint analysis. It differs from conventional conjoint choice and preference models in that the attributes of choice alternatives or choice contexts are not varied simultaneously, but in pairs. Properties of the design strategy are discussed. The new approach is illustrated by using activity engagement choice as an example.

We Know Exactly what you want: The Development of a Completely Individualised Conjoint Analysis

2013 ◽  
Vol 55 (3) ◽  
pp. 437-458 ◽  
Author(s):  
Markus Voeth ◽  
Uta Herbst ◽  
Frank Liess
Keyword(s):  
Data Collection ◽  
Empirical Study ◽  
Conjoint Analysis ◽  
Predictive Validity ◽  
Important Research ◽  
New Approach ◽  
Preference Modelling ◽  
Attribute Levels ◽  
Product Presentation ◽  
Modelling Data

Improving the predictive validity of conjoint analysis has been an important research objective for many years. Whereas the majority of attempts have been different approaches to preference modelling, data collection or product presentation, only a few scholars have tried to improve predictive validity by individualising conjoint designs. This comes as a surprise because many markets have observed an augmented demand for customised products and highly heterogeneous customers' preferences. Against this background, the authors develop a conjoint variant based on a completely individualised conjoint design. More concretely, the new approach not only individualises the attributes, but also the attribute levels. The results of a comprehensive empirical study yield a significantly higher validity than existing standardised-level conjoint approaches. Consequently, they help marketers to gain deeper insights into their customers' preferences.

eWOM

2019 ◽  
pp. 143-173 ◽  
Author(s):  
Juan Pedro Mellinas ◽  
Sofía Reino
Keyword(s):  
Information Overload ◽  
Online Reviews ◽  
Influential Factors ◽  
Online Review ◽  
Hotel Management ◽  
Crucial Aspect ◽  
New Approach ◽  
Tourism Professionals ◽  
Hotel Managers ◽  
Dynamic Relationships

It is difficult to find a traveler who has not written and/or read an online review at any stage of their travel. Most people will not book a hotel if this has no reviews and/or will not choose a destination before reading some opinions from other users. Tourism professionals can gain a comprehensive understanding of the dynamic relationships and key influential factors which are relevant to online reviews. A single business can have thousands of reviews. This creates a situation of information overload for hotel managers, who encounter themselves with increasingly larger numbers of information to analyze and act upon. The ability to effectively analyze data, using in occasions dedicated software becomes a crucial aspect of hotel management. The chapter ends with a reflection on how eWOM is leading to the generation of a new approach to business management.

New Conjoint Approaches to Scaling Brand Equity and Optimising share of Preference Prediction

2015 ◽  
Vol 57 (5) ◽  
pp. 701-725 ◽  
Author(s):  
Hervé Guyon ◽  
Jean-François Petiot
Keyword(s):  
Structural Equation Modeling ◽  
Conjoint Analysis ◽  
Brand Equity ◽  
Repeated Measures ◽  
Latent Variable ◽  
Structural Equation ◽  
Probabilistic Models ◽  
Equation Modeling ◽  
New Approach ◽  
First Choice

Ratings-based conjoint analysis suffers two problems: the distortion raised by consumer perceptions of brand equity, and the lack of efficiency of probabilistic models for estimating preference shares. This article proposes two new approaches to scale customer-based brand equity using repeated measures and structural equation modeling and to estimate the share of preferences on the basis of a randomized first choice. The outcome is a new tool to predict accurate preference shares, taking into account product utilities (estimated by rating-based conjoint analysis) and the brand equity related to product attributes (estimated as a latent variable with structural equation modeling). An example with three products illustrates this new approach.

scholarly journals Bootstrapping Swarm and observatory data to generate candidates for the DGRF and IGRF-13

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
F. Javier Pavón-Carrasco ◽  
Santiago Marsal ◽  
J. Miquel Torta ◽  
Manuel Catalán ◽  
Fátima Martín-Hernández ◽  
...  
Keyword(s):  
Time Window ◽  
International Association ◽  
Temporal Data ◽  
Reference Field ◽  
Bootstrap Analysis ◽  
Group V ◽  
New Approach ◽  
Observatory Data ◽  
Geomagnetic Field Modelling ◽  
Modelling Approach

Abstract As posted by the Working Group V of the International Association of Geomagnetism and Aeronomy (IAGA), the 13th generation of the International Geomagnetic Reference Field (IGRF) has been released at the end of 2019. Following IAGA recommendations, in this work we present a candidate model for the IGRF-13, for which we have used the available Swarm satellite and geomagnetic observatory ground data for the last year. In order to provide the IGRF-13 candidate, we have extrapolated the Gauss coefficients of the main field and its secular variation to January 1st, 2020. In addition, we have generated a Definitive Geomagnetic Reference Field model for 2015.0 using the same modelling approach, but focussed on a 1-year time window of data centred on 2015.0. To jointly model both satellite and ground data, we have followed the classical protocols and data filters applied in geomagnetic field modelling. Novelty arrives from the application of bootstrap analysis to solve issues related to the inhomogeneity of the spatial and temporal data distributions. This new approach allows the estimation of not only the Gauss coefficients, but also their uncertainties.

IMPROVING CUSTOMERS TARGETING WITH SHORT INTERVENTION TESTING

2010 ◽  
Vol 14 (03) ◽  
pp. 435-448 ◽  
Author(s):  
ALEX GOFMAN ◽  
HOWARD R. MOSKOWITZ
Keyword(s):  
Conjoint Analysis ◽  
Small Subset ◽  
New Approach ◽  
Test Elements ◽  
The Creation ◽  
Innovation Approach ◽  
Short Intervention

The proposed consumer-driven innovation approach allows for the creation of individual communications with heterogeneous customers. It utilizes Rule Developing Experimentation (a modified conjoint analysis-based approach) to create a database of messages specific to the product and to segment consumers based on the patterns of individual utilities assigned to the different test elements. The paper introduces a new approach to identify a small subset of classification messages that allows for an actionable and parsimonious classification of any new population into pattern-based segments to achieve better targeting. The approach is demonstrated by a case study of identifying segment membership for better messages targeting prospective customers of a KIA car dealership.

scholarly journals Construction of Heterogeneous Conjoint Choice Designs: A New Approach

2015 ◽  
Vol 34 (3) ◽  
pp. 346-366 ◽  
Author(s):  
Qing Liu ◽  
Yihui (Elina) Tang
Keyword(s):  
New Approach ◽  
Conjoint Choice

On Conjoint Analysis and Quantal Choice Models

1980 ◽  
Vol 53 (S3) ◽  
pp. S37 ◽  
Author(s):  
Albert Madansky
Keyword(s):  
Conjoint Analysis ◽  
Choice Models

scholarly journals Assessing the Importance of Apple Attributes: An Agricultural Application of Conjoint Analysis

1990 ◽  
Vol 19 (2) ◽  
pp. 118-124 ◽  
Author(s):  
Alberto B. Manalo
Keyword(s):  
Conjoint Analysis ◽  
Agricultural Application ◽  
Attribute Levels

The use of conjoint analysis in assessing consumers’ preferences for attributes is demonstrated with the apple as an example. Conjoint analysis may be used to estimate the importance of attributes and attribute levels through decomposition of consumers’ ranking of alternative attribute combinations. It is shown that conjoint analysis provides results that may not be obtained from a survey where respondents are asked to directly state their assessment of the importance of attributes.

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