• Abstract and Figures
• Public Full-text

TESIS DOCTORAL Three essays on conjoint analysis: optimal design and estimation of endogenous consideration sets Autor: Agata Leszkiewicz Director/es: Mercedes Esteban-Bravo, PhD Jose M. Vidal-Sanz, PhD DEPARTAMENTO ECONOMÍA DE LA EMPRESA Getafe, January 2014 TESIS DOCTORAL Three essays on conjoint analysis: optimal design and estimation of endogenous consideration sets Autor: Agata Leszkiewicz Director/es: Mercedes Esteban-Bravo, PhD José M. Vidal-Sanz, PhD Firma del Tribunal Calificador: Firma Presidente: Vocal: Secretario: Calificación: Getafe, de de Acknowledgments First and foremost, I owe a debt of gratitude to my PhD advisors, Mercedes Esteban-Bravo and Jose M. Vidal-Sanz. The influence of their guidance and motivation on the final form of this dis- sertation cannot be overstated. I feel very fortunate to have had the opportunity to work closely with Mercedes and Jose in many areas of the academic life. During these years I have gotten to know them not only as brilliant marketing modelers, but also as organized and trustworthy pro- fessionals. Collaboration with Mercedes and Jose shaped me into a mature researcher. I would also like to thank Mercedes for inviting me to participate in her research project, which provided financial support to this thesis. I also wish to thank Don Lehmann for his hospitality and mentorship during my research visit at Columbia Business School. It was an invaluable opportunity to learn from him and I deeply appreciate his support, feedback and career advice. My thanks go to Leonard Lee, Oded Netzer and Nicholas Reinholtz for their warm reception in New York and comments on my work. I cannot emphasize enough how much I gained from (and enjoyed) my research stay at Columbia. I am obliged to the entire marketing team at Carlos 3. I appreciate the support and feedback of Nora Lado and Alicia Barroso during the first years of my teaching experience. My thanks go to James Nelson, Lola Duque and Fabrizio Cesaroni for their words of encouragement. I am grateful to Goki and Vardan for clearing the paths for me on the PhD journey, for their sincere advice and for their friendship. I wish to thank Manuel Bagües and Encarna Guillamón, with whom I worked closely in the Department, for many challenging discussions, advice and for being my referees. I would like to mention other faculty members who supported me at different moments at Carlos 3: Josep Tribó, Jaime Ortega, Pablo Ruiz-Verdú and Esther Ruíz. I warmly thank Agnieszka Szczepanska-´ Álvarez from the Poznan´ University of Life Sciences for the friendly review of my paper. i During the years I spent in Madrid I was fortunate to have met many extraordinary people. I enjoyed the friendship of Ana Laura, Dilan, Juliana and Su-Ping, who have been there for me through all the ups and downs. My special thanks go to Adolfo, Agnieszka, Ana Maria, Argyro, Borbala, Emanuele, Han-Chiang and Jonatan. I leave Madrid hoping that soon our paths will cross again. I am grateful to Lukas for his patience. He has been a true partner on this journey, always supporting me in my objectives. I thank my whole family for their unlimited love, inspiration and constant encouragement. Finally, I am indebted to my mother for proofreading of parts of my work. I thankfully acknowledge the financial support of the Department of Business Administration at the University Carlos III of Madrid, the University Carlos III of Madrid (Programa Propio de Investigación), the Ministry of Science and Innovation in Spain (research grant ECO2011- 30198), and the Education Council of the Autonomous Community of Madrid (research grant S2009/ESP-1594). I dedicate this dissertation to my family. Agata ii Abstract Over many years conjoint analysis has become the favourite tool among marketing practition- ers and scholars for learning consumer preferences towards new products or services. Its wide acceptance is substantiated by the high validity of conjoint results in numerous successful im- plementations among a variety of industries and applications. Additionally, this experimental method elicits respondents’ preference information in a natural and effective way. One of the main challenges in conjoint analysis is to efficiently estimate consumer preferences towards more and more complex products from a relatively small sample of observations because respondent’s wear-out contaminates the data quality. Therefore the choice of sample products to be evaluated by the respondent (the design) is as much as relevant as the efficient estimation. This thesis contributes to both research areas, focusing on the optimal design of experiments (essay one and two) and the estimation of random consideration sets (essay three). Each of the essays addresses relevant research gaps and can be of interest to both marketing managers as well as academicians. The main contributions of this thesis can be summarized as follows: • The first essay proposes a general flexible approach to build optimal designs for linear conjoint models. We do not compute good designs, but the best ones according to the size (trace or determinant) of the information matrix of the associated estimators. Additionally, we propose the solution to the problem of repeated stimuli in optimal designs obtained by numerical methods. In most of comparative examples our approach is faster than the existing software for Conjoint Analysis, while achieving the same efficiency of designs. This is an important quality for the applications in an online context. This approach is also more flexible than traditional design methodology: it handles continuous, discrete and mixed attribute types. We demonstrate the suitability of this approach for conjoint analysis with rank data and ratings (a case of an individual respondent and a panel). Under certain assumptions this approach can also be applied in the context of discrete choice experiments. • In the essay 2 we propose a novel method to construct robust efficient designs for conjoint iii experiments, where design optimization is more problematic, because the covariance ma- trix depends on the unknown parameter. In fact this occurs in many nonlinear models commonly considered in conjoint analysis literature, including the preferred choice-based conjoint analysis. In such cases the researcher is forced to make strong assumptions about unknown parameters and to implement an experimental design not knowing its true effi- ciency. We propose a solution to this puzzle, which is robust even if we do not have a good prior guess about consumer preferences. We demonstrate that benchmark designs perform well only if the assumed parameter is close to true values, which is rarely the case, oth- erwise there is no need to implement the experiment. On the other hand, our worst-case designs perform well under a variety of scenarios and are more robust to misspecification of parameters. • Essay 3 contributes with a method to estimate consideration sets which are endogenous to respondent preferences. Consideration sets arise when consumers use decision rules to simplify difficult choices, for example when evaluating a wide assortment of complex products. This happens because rationally bounded respondents often skip potentially in- teresting options, for example due to lack of information (brand unawareness), perceptual limitations (low attention or low salience), or halo effect. Research in consumer behaviour established that consumers choose in two stages: first they screen off products whose at- tributes do not satisfy certain criteria, and then select the best alternative according to their preference order (over the considered options). Traditional CA focuses on the second step, but more recently methods incorporating both steps were developed. However, they are always considered to be independent, while the halo effect clearly leads to endogeneity. If the cognitive process is influenced by the overall affective impression of the product, we cannot assume that the screening-off is independent from the evaluative step. To test this behavior we conduct an online experiment of lunch menu entrees using Amazon MTurk sample. iv Resumen A lo largo de los años, el “Análisis Conjunto” se ha convertido en una de las herramientas más ex- tendidas entre los profesionales y académicos de marketing. Se trata de un método experimental para estudiar la función de utilidad que representa las preferencias de los consumidores sobre productos o servicios definidos mediante diversos atributos. Su enorme popularidad se basa en la validez y utilidad de los resultados obtenidos en multitud de estudios aplicados a todo tipo de industrias. Se utiliza regularmente para problemas tales como diseño de nuevos productos, análisis de segmentación, predicción de cuotas de mercado, o fijación de precios. En el análisis conjunto, se mide la utilidad que uno o varios consumidores asocian a diversos productos, y se estima un modelo paramétrico de la función de utilidad a partir de dichos datos usando métodos de regresión en sus diversas variantes. Uno de los principales retos del análisis conjunto es estimar eficientemente los parámetros de la función de utilidad del consumidor hacia productos cada vez más complejos, y hacerlo a partir de una muestra relativamente pequeña de observaciones debido a que en experimentos prolongados la fatiga de los encuestados contamina la calidad de los datos. La eficiencia de los estimadores es esencial para ello, y dicha eficiencia depende de los productos evaluados. Por tanto, la elección de los productos de la muestra que serán evaluados por el encuestado (el diseño) es clave para el éxito del estudio. La primera parte de esta tesis contribuye al diseño óptimo de experimentos (ensayos uno y dos, que se centran respectivamente en modelos lineales en parámetros, y modelos no lineales). Pero la función de utilidad puede presentar discontinuidades. A menudo el consumidor simplifica la decisión apli- cando reglas heurísticas, que de facto introducen una discontinuidad. Estas reglas se denominan conjuntos de consideración: los productos que cumplen la regla son evaluados con la función de utilidad usual, el resto son descartados o evaluados con una utilidad diferente (especialmente baja) que tiende a descartarlos.

Load more

  188

Copy Link