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Author: Daria Dzyabura
Publisher:
ISBN:
Category :
Languages : en
Pages : 112
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This thesis consists of three related essays which explore new approaches to modeling and measurement of consumer decision strategies. The focus is on decision strategies that deviate from von Neumann-Morgenstern utility theory. Essays 1 and 2 explore decision rules that consumers use to form their consideration sets. Essay 1 proposes disjunctions-of-conjunctions (DOC) decision rules that generalize several well-studied decision models. Two methods are proposed for estimating the model. Consumers' consideration sets for global positioning systems are observed for both calibration and validation data. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of profiles. Essay 2 develops and tests an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a "configurator." Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question the posterior is approximated with a variational distribution and uses belief-propagation. The method runs sufficiently fast to select new queries in under a second and provides significantly and substantially more information per question than existing methods based on random, market-based, or orthogonal questions. The algorithm is tested empirically in a web-based survey conducted by an American automotive manufacturer to study vehicle consideration. Adaptive questions outperform market-based questions when estimating heuristic decision rules. Heuristics decision rules predict validation decisions better than compensatory rules. Essay 3 proposes a model of product search when preferences are constructed during the process of search: consumers learn what they like and dislike as they examine products. Product recommendations, whether made by sales people or online recommendation systems, bring products to the consumer's attention and impact his/her preferences. Changing preferences changes the products the consumer will choose to search; at the same time, the products the consumer chooses to search will determine the future shifts in preferences. Accounting for this two-way relationship between products and preferences is critical in optimizing recommendations.
Author: Daria Dzyabura
Publisher:
ISBN:
Category :
Languages : en
Pages : 112
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Book Description
This thesis consists of three related essays which explore new approaches to modeling and measurement of consumer decision strategies. The focus is on decision strategies that deviate from von Neumann-Morgenstern utility theory. Essays 1 and 2 explore decision rules that consumers use to form their consideration sets. Essay 1 proposes disjunctions-of-conjunctions (DOC) decision rules that generalize several well-studied decision models. Two methods are proposed for estimating the model. Consumers' consideration sets for global positioning systems are observed for both calibration and validation data. For the validation data, the cognitively simple DOC-based methods predict better than the ten benchmark methods on an information theoretic measure and on hit rates. The results are robust with respect to format by which consideration is measured, sample, and presentation of profiles. Essay 2 develops and tests an active-machine-learning method to select questions adaptively when consumers use heuristic decision rules. The method tailors priors to each consumer based on a "configurator." Subsequent questions maximize information about the decision heuristics (minimize expected posterior entropy). To update posteriors after each question the posterior is approximated with a variational distribution and uses belief-propagation. The method runs sufficiently fast to select new queries in under a second and provides significantly and substantially more information per question than existing methods based on random, market-based, or orthogonal questions. The algorithm is tested empirically in a web-based survey conducted by an American automotive manufacturer to study vehicle consideration. Adaptive questions outperform market-based questions when estimating heuristic decision rules. Heuristics decision rules predict validation decisions better than compensatory rules. Essay 3 proposes a model of product search when preferences are constructed during the process of search: consumers learn what they like and dislike as they examine products. Product recommendations, whether made by sales people or online recommendation systems, bring products to the consumer's attention and impact his/her preferences. Changing preferences changes the products the consumer will choose to search; at the same time, the products the consumer chooses to search will determine the future shifts in preferences. Accounting for this two-way relationship between products and preferences is critical in optimizing recommendations.
Author: Sudipt Roy
Publisher:
ISBN:
Category :
Languages : en
Pages : 194
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Author: Taylor Baldwin Bentley
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 150
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Book Description
In this dissertation, I utilize and develop empirical tools to help academics and practitioners model the consumer's choice process. This collection of three essays strives to answer three main research questions in this theme. In the first paper, I ask: how is the consumer's purchase decision impacted by the search for general product-category information prior to search for their match with a retailer or manufacturer ("sellers")? This paper studies the impact of informational organic keyword search results on the performance of sponsored search advertising. We show that, even though advertisers can target consumers who have specific needs and preferences, for many consumers this is not a sufficient condition for search advertising to work. By allowing consumers to access content that satisfies their information requirements, informational organic results can allow consumers to learn about the product category prior to making their purchase decision. We develop a model characterize the situation in which consumers can search for general information about the product category as well as for information about the individual sellers' offerings. We estimate this model using a unique dataset of search advertising in which commercial websites are restricted in the organic listing, allowing us to identify consumer clicks as informational (from organic links) or purchase oriented (from sponsored links). With the estimation results, we show that consumer welfare is improved by 29%, while advertisers generate 19% more sales, and search engines obtain 18% more paid clicks, as compared to the scenario without informational links. We conduct counterfactuals and find that consumers, advertisers, and the search engine are significantly better off when the search engine provides "free" general information about the product. When the search engine provides information about the advertisers' specific offerings, however, there are fewer paid clicks and advertisers at high ad positions will obtain lower sales. We further investigate the implications on the equilibrium advertiser bidding strategy. Results show that advertiser bids will remain constant in the former scenario. When the search engine provides advertiser information, advertisers will increase their bids because of the increased conversion rate; however, the search engine still loses revenue due to the decreased paid clicks. The findings shed important managerial insights on how to improve the effectiveness of search advertising. In the second paper, I ask: how is the consumer's search for information, during their choice process and in an advertising context, influenced by the signaling theory of advertising? Using a dataset of travel-related keywords obtained from a search engine, we test to what extent consumers are searching and advertisers are bidding in accordance with the signaling theory of advertising in literature. We find significant evidence that consumers are more likely to click on advertisers at higher positions because they infer that such advertisers are more likely to match with their needs. Further, consumers are more likely to find a match with advertisers who have paid more for higher positions. We also find strong evidence that advertisers increase their bids when there is an improvement in the likelihood that their offerings match with consumers' needs, and the improvement cannot be readily observed by consumers prior to searching advertisers' websites. These results are consistent with the predictions from the signaling theory. We test several alternative explanations and show that they cannot fully explain the results. Furthermore, through an extension we find that advertisers can generate more clicks when competing against advertisers with higher match value. We offer an explanation for this finding based on the signaling theory. In the third paper, I ask: can we model the consumer's choice of brand as a sequential elimination of alternatives based on shared or unique aspects while incorporating continuous variables, such as price? With aggregate scanner data, marketing researchers typically estimate the mixed logit model, which accounts for non-IIA substitution patterns among brands, which arise due to similarity and dominance effects in demand. Using numerical examples and analytical illustrations, this research shows that the mixed logit model, which is widely believed to be a highly flexible characterization of brand switching behavior, is not well designed to handle non-IIA substitution patterns. The probit allows only for pair-wise inter-brand similarities, and ignores third-order or higher dependencies. In the presence of similarity and dominance effects, the mixed logit model and the probit model yield systematically distorted marketing mix elasticities. This limits the usefulness of mixed logit and probit for marketing decision-making. We propose a more flexible demand model that is an extension of the elimination-by-aspects (EBA) model (Tversky 1972a, 1972b) to handle marketing variables. The model vastly expands the domain of applicability of the EBA model to aggregate scanner data. Using an analytical closed-form that nests the traditional logit model as a special case, the EBA demand model is estimated with marketing variables from aggregate scanner data in 9 different product categories. It is compared to the mixed logit and probit models on the same datasets. In terms of multiple fit and predictive metrics (LL, BIC, MSE, MAD), the EBA model outperforms the mixed logit and the probit in a majority of categories in terms of both in-sample fit and holdout predictions. The results show significant differences in the estimated price elasticity matrices between the EBA model and the comparison models. In addition, a simulation shows that the retailer can improve gross profits up to 34.4% from pricing based on the EBA model rather than the mixed logit model. Finally, the results suggest that empirical IO researchers, who routinely use mixed logit models as inputs to oligopolistic pricing models, should consider the EBA demand model as the appropriate model of demand for differentiated products.
Author: Polykarpos Pavlidis
Publisher:
ISBN:
Category : Consumer behavior
Languages : en
Pages : 254
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"Marketing strategies that firms adopt are based on consumers' response in the marketplace when they face and interact with these strategies. This dissertation examines the tendency of consumers to repeat their last purchase choices and the implications of this type of behavior on pricing related strategies of consumer packaged goods brand manufacturers. The first essay is a theory based empirical investigation about the commonly observed practice of brands offering temporary price promotions. There have been many theories that attempt to explain the popularity of price promotions as a marketing tool but with very few exceptions they are disconnected from choice dynamics. We examine the empirical support of a recent theory that connects price promotions with demand state dependence. In our investigation we measure how much each brand benefits from the consumers' tendency to repeat purchase and we examine the connection between this measure (AMEL) and the brands' price promotional frequencies. Our extensive sample includes all major brands from twenty product categories of frequently purchased goods and twenty stores in two separate geographical markets. Our empirical model accounts explicitly for the dependence of price promotions on demand response and vice versa. In summary, we find significant and robust evidence that brands which gainmore from consumers' repeat purchase behavior are offered on promotion formore weeks on average. We also demonstrate the value of our proposed estimation algorithm over simpler, two-step, approaches. In the second essay we examine consumers' state dependence not only to specific choice alternatives but also to parent brands that cover multiple sub-brands. Using a structural, forward looking, pricing model for multiproduct firms, we explore the implications of parent brand state dependence on equilibrium prices and firm profitability through counterfactual experiments. Empirically, we examine household level choice data from the category of yogurt and estimate state dependence to both the parent brand and the sub-brand level. We find evidence of parent brand state dependence for the category of yogurt. Its impact on the market equilibrium is to push prices downwards, because firms invest in future demand, and increase profitability of multiproduct firms, because per period demand increases"--Leaves iv-v.
Author: Xavi Vidal-Berastain
Publisher:
ISBN:
Category : Calculus of tensors
Languages : en
Pages : 146
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"This dissertation consists of three essays structured in three chapters. Chapter one studies the effect of social media has on consumer decisions. It is well-known that social media has become a major pillar of most brand strategies. We investigate the impact of social media posts on brand choices as well as how social media posts are generated in the context of political candidates (brands). We center our study at the early stages of the 2012 Republican Presidential primary race. This time period is sometimes referred to as the pre-race and collect daily positive, negative, and neutral posts on social media primary race as well as daily polls, news coverage, and candidate debate performance. We construct a model with consumers taking myopic decisions based a preference relation that is correlated across time. In doing so, we shed light on the underlying mechanisms linking the strategic generation of word of mouth media to voting decisions. Our results show that the tone of the social media around brands or candidates are relevant measures to account for. Specifically, for influencing voters' decisions, increasing the level of positive word of mouth around a candidate is more effective than a similar increase in the level of exposure in the mass media. We also show that the positive social media posts decrease more slowly than negative as the candidate's poll rank worsens. Finally, our results of the multiplicative attention model reveal important differences in the speed at which the general level of social media attention is translated into positive and negative social media. Chapters two and three switch gears to join the wave of big data that is rapidly expanding the set of questions that can be approached. Chapter two of this dissertation uses a very large panel of consumers to disentangle how consumers change the way they shop as more options become available. Grocery retailing has witnessed almost continuous disruption over the past several decades. While the traditional supermarket format was an important post-war innovation, it has gradually been refined and replaced by a diverse collection of sophisticated retail platforms, including super-centers, club stores, limited assortment stores, organic specialists, and a small, but growing online channel. Consumers now face a rich array of shopping options that differ by size, location, assortment, and price, alongside other less quantifiable dimensions such as ambiance and convenience. We exploit machine learning and data fusion techniques to create and combine a rich collection of data on consumer choice and store entry decisions into a unified longitudinal panel. We further employ frontier synthetic control methods (Abadie et al. (2010)) to address the issues of selection and heterogeneous response inherent to this context, extending them to our dis-aggregated setting. Grocery retailing is surprisingly complicated, as it involves what is essentially an exercise in joint production between consumer and retailer that takes place over a sophisticated two-sided platform linking consumer product manufacturers (brands) to their downstream shopping base. The last chapter of this dissertation augments chapter two. Specifically, it presents a parallelization strategy that exploits recent improvements in general purpose GPU-computing to improve the performance of the synthetic controls estimator when applied to large and un-balanced data sets. When implemented on unbalanced panels, the synthetic controls estimator suffers from interpolation bias, over-fitting and does not have statistical properties that eases the task of performing causal hypothesis testing. Traditional fixes to these problems such as parameter-regularization (for over-fitting) or permutation tests (inference), helps to mitigate these problems but increase the computational cost exponentially. Chapter shows how to frame the quadratic programming program as a tensor optimizing problem. A tensor is a generalization of a matrix to allow for an arbitrary number of dimensions. We create a tensor containing all the data to optimize and to perform permutation tests and side-loaded into a GPU and run on a modified version of the mirror descent algorithm to perform thousands of optimization problems in parallel. I show the performance of my method and illustrate its power by studying how the entry of different retail platforms changes the way consumers purchase organic product"--Pages ix-xi.
Author: Peter J. Boyle
Publisher: Ann Arbor, Mich. : University Microfilms International
ISBN:
Category :
Languages : en
Pages : 274
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Author: Sandeep R. Chandukala
Publisher: Now Publishers Inc
ISBN: 1601981643
Category : Business & Economics
Languages : en
Pages : 100
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Book Description
Choice Models in Marketing examines recent developments in the modeling of choice for marketing and reviews a large stream of research currently being developed by both quantitative and qualitative researches in marketing. Choice in marketing differs from other domains in that the choice context is typically very complex, and researchers' desire knowledge of the variables that ultimately lead to demand in marketplace. The marketing choice context is characterized by many choice alternatives. The aim of Choice Models in Marketing is to lay out the foundations of choice models and discuss recent advances. The authors focus on aspects of choice that can be quantitatively modeled and consider models related to a process of constrained utility maximization. By reviewing the basics of choice modeling and pointing to new developments, Choice Models in Marketing provides a platform for future research.
Author: Dr. Javaid R. Khwaja
Publisher: iUniverse
ISBN: 147599740X
Category : Business & Economics
Languages : en
Pages : 593
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Book Description
The new economy, under the impetus of the ever-widening outreach of the Internet, is undergoing a transition. In the meantime, theres also been a shift to the information paradigm, with its emphasis on lack of foresight. These processes have almost completely supplanted the concept of market that was once one of the most cardinal features of conventional economic theory. In Toward a General Theory of Exchange: Strategic Decisions and Complexity, author Dr. Javaid R. Khwaja traces the slow melting of the market, the most ubiquitous contraption and the summum bonum of economic science, as an organized manifestation of complexity, with its wide-ranging impact on the flow of funds. Using the historical background of economic theories, this study blends the interdisciplinary range and fills the vacuum that has existed among current conventional economic theory, the theory of strategic decision making, actor-network theory, the domain of law and economics, and the science of complexity. An observer of economic development for several decades, Khwaja shows the relationship between technology and economics and how it affects social exchanges and trends.
Author: Tugrul U. Daim
Publisher: Springer
ISBN: 3319185586
Category : Business & Economics
Languages : en
Pages : 342
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Book Description
This volume, developed in honor of Dr. Dundar F. Kocaoglu, aims to demonstrate the applications of the Hierarchical Decision Model (HDM) in different sectors and its capacity in decision analysis. It is comprised of essays from noted scholars, academics and researchers of engineering and technology management around the world. This book is organized into five parts: Technology Policy Planning, Strategic Technology Planning, Technology Assessment, Application Extensions, and Methodology Extensions. Dr. Dundar F. Kocaoglu is one of the pioneers of multiple decision models using hierarchies, and creator of the HDM in decision analysis. HDM is a mission-oriented method for evaluation and/or selection among alternatives. A wide range of alternatives can be considered, including but not limited to, different technologies, projects, markets, jobs, products, cities to live in, houses to buy, apartments to rent, and schools to attend. Dr. Kocaoglu’s approach has been adopted for decision problems in many industrial sectors, including electronics research and development, education, government planning, agriculture, energy, technology transfer, semiconductor manufacturing, and has influenced policy locally, nationally, and internationally. Moreover, his students developed advanced tools and software applications to further improve and enhance the robustness of the HDM approach. Dr. Kocaoglu has made many contributions to the field of Engineering and Technology Management. During his tenure at Portland State University, he founded the Engineering and Technology Management program, where he served as Program Director and later, Department Chair. He also started the Portland International Conference on Management of Engineering and Technology (PICMET), which organizes an annual conference in international locations such as Korea, Turkey, South Africa, Thailand, and Japan. His teaching has won awards and resulted in a strong sense of student loyalty among his students even decades later. Through his academic work and research, Dr. Kocaoglu has strongly supported researchers of engineering management and has provided tremendous service to the field. This volume recognizes and celebrates Dr. Kocaoglu’s profound contributions to the field, and will serve as a resource for generations of researchers, practitioners and students.
Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 522
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