The applications of conjoint analysis and their possible uses in Sensometrics

Moskowitz, Howard R.; Silcher, Matthias

doi:10.1016/j.foodqual.2005.07.006

Cited by 62 publications

(49 citation statements)

References 33 publications

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“…Each row had columns corresponding to the 36 elements, with an additional column containing the rating for that concept, which was the dependent variable. When an element appeared in a concept, a 1 was placed in the column for that element; if the element was absent from a concept, a 0 was placed in the corresponding column [18]. The rating of each concept was then transformed to a binary scale: a score of 1 to 6 was recoded as 0, while a score of 7 to 9 was recoded as 100.…”

Section: Data Transformation and Analysis Proceduresmentioning

confidence: 99%

Identifying the Drivers of a Foliage Plant Purchasing Decision via Contemporary Psychophysics

Dewar¹,

Keene²,

Kalk³

et al. 2016

Horticulture

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Using modified conjoint analysis techniques, two separate yet sequential studies were conducted to gain a better understanding of foliage plant consumers and their plant/purchasing preferences. The first study primarily focused on consumers' perceptions of a foliage plant and various purchasing conditions. The second study focused on accessories featured with a foliage plant in a retail setting. These studies revealed elements that appealed to each study's population as a whole, to specific demographic groups, and to different segments of the overall populations. Through both studies, the concept of "buy one, get one free" was of strong interest to the majority of participants. Elements related to consumer benefits, such as "this plant has been proven to improve the indoor air quality of your home," received the highest overall interest in study one, while elements related to plant containers, sleeves, and value garnered high overall interest in study two. Sorting the data by various demographic groups revealed a multitude of differences in consumer preference for foliage plants and plant purchasing experiences, which allow for targeted marketing by foliage plant retailers. K-clustering revealed separate and unique segments in each study. Study one resulted in a larger segment of the sample population (61%) primarily interested in consumer benefits, while the smaller segment (39%) was chiefly interested in plant care. Study two produced a segment (57%) interested in monetary value, as well as a segment (43%) drawn to plants bred by renowned breeders and promoted by sources such as Better Homes and Gardens. These observations will allow nurseries and retailers to customize the plant purchasing experience according to specific consumer preferences, thereby increasing consumer engagement and reinforcing purchasing decisions.

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Section: Data Transformation and Analysis Proceduresmentioning

confidence: 99%

Identifying the Drivers of a Foliage Plant Purchasing Decision via Contemporary Psychophysics

Dewar¹,

Keene²,

Kalk³

et al. 2016

Horticulture

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“…A range of 150 to 1200 respondents is suggested for experimentation [10]. [27] Mentions a sample size for full factorial designs between 50-100 respondents to achieve meaningful, robust, and projectable data. Other examples include the work performed by Torres, Paz & Salazar that resulted in a sample size of 246 people using a mathematical formulation.…”

Section: Sample Sizementioning

confidence: 99%

“…However, given the low rate response of 3% for data collection using Internet [27], the use of tablets was employed to collect data on the field as well.…”

Section: Construction Of the Survey And Data Collectionmentioning

confidence: 99%

Mas Allá de la Suposición de Efectos Principales en Análisis Conjunto: Comparación Entre Análisis Conjunto Tradicional vs. Análisis Conjunto Basado en Elección. Enfoque Estadístico y Construcción de Diseños Aplicado al Desarrollo de Nuevos Productos

Paredes

Enríquez

Navarrete

2015

Av. Cienc. Ing. (Quito)

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The assumption of only main effects in Conjoint Analysis methods has created a debate whether to focus or not on the impact of interactions in determining the most prefered combination of attributes of a product. In this research a comparison of Conjoint Value Analysis CVA and Choice-Based Conjoint CBC surveys were undertaken to contrast them through utility scores, importance values of attributes and goodness-of-fit using ready to drink beverages as the subject. The main effects assumption in the CVA composition rule was compared to the interaction terms in the CBC one. Two scenarios were developed; the first one considered inner characteristics of the subject and a sample size of 250 respondents. The second one considered the presentation characteristics of the subject and a sample size of 150 respondents. The two higher total utility scores were obtained in the CBC using an interactive composition rule. In Scenario 1 a higher goodness-of-fit was found in the CBC, including significant interactions, in contrast with Scenario 2, where no interactions were found, and CVA had a higher goodness-of-fit.Keywords. Conjoint Analysis, New Product Design, Fractional Factorial Design, Hierarchical Bayes Estimation, Design of Experiments.El supuesto de considerar solo efectos principales en el Analisis Conjunto ha creado un debate si enfocarse o no en el impacto de las interacciónes para determinar la combinación con mayor preferencia en los atributos del producto. La comparación se realizó entre las encuestas del Análisis Conjunto Tradicional CVA y el Análisis Conjunto Basado en la Elección CBC para contrastarlas a través de los valores de utilidad, valores de importancia de los atributos y bondad de ajuste en ambas metodologías, usando una bebida lista para tomar como sujeto de prueba. La suposición de efectos principales en la regla de com-posición del CVA fue comparada con la inclusión de términos de interacción significativos en el CBC. Se desarrollaron dos escena-rios; en el primero de consideró características internas del sujeto de prueba y se utilizó un tamaño de muestra de 250 encuestados. En el segundo escenario consideró características de presentación del sujeto de prueba y un tamaño de muestra de 150 encuestados. Los dos valores de utilidad más altos se obtuvieron del CBC, usando una regla de composición con interacciones, acabó considerando a la Cerveza, en cambio en el CVA este nivel reporto una utilidad negativa. En el Escenario 1 se encontró una bondad de ajus-te más alta para el CBC, incluyendo interacciones significativas, en contraste con el Esce-nario 2, donde no se encontraron interacciones significativas y en ese caso el CVA tuvo una bondad de ajuste mayor.Palabras Clave. Análisis Conjunto, Diseño de nuevos productos, Diseño factorial fraccionado, Estimación Jerárquica Bayes, Diseño de experimentos.

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“…MOANOVA is used for measuring the part worth value of different factors to the total evaluation based on preference ranking data of a group of commercial products designed by presorted various factors. Moskowitz and Silcher (2006) applied the applications of conjoint analysis and their possible uses in Sensometrics.…”

Section: Introductionmentioning

confidence: 99%

Customer preferences using conjoint analysis: A case study of Auto industry

Hondori¹,

Javanshir²,

Rabani³

2013

10.5267/j.msl

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One of the primary concerns in product development is to meet customers' wishes and this could be accomplished through detecting the most popular characteristics of products. In other words, the fulfillment of customers' preferences in a profitable way needs that companies recognize which specifications of their product and service are most valued by the customer. Conjoint analysis is believed to be one of the most popular techniques for achieving this purpose. Conjoint analysis includes generating and conducting specific experiments among customers for modeling their purchasing decision. This paper presents an empirical investigation on detecting appropriate customer preferences in an auto-industry. The results of the survey indicate that price, braking system and having airbag are the most important characteristics for selling cars in Iranian market.

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The applications of conjoint analysis and their possible uses in Sensometrics

Cited by 62 publications

References 33 publications

Identifying the Drivers of a Foliage Plant Purchasing Decision via Contemporary Psychophysics

Identifying the Drivers of a Foliage Plant Purchasing Decision via Contemporary Psychophysics

Mas Allá de la Suposición de Efectos Principales en Análisis Conjunto: Comparación Entre Análisis Conjunto Tradicional vs. Análisis Conjunto Basado en Elección. Enfoque Estadístico y Construcción de Diseños Aplicado al Desarrollo de Nuevos Productos

Customer preferences using conjoint analysis: A case study of Auto industry

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