Process and context in choice models

We develop a general framework that extends choice models by including an explicit representation of the process and context of decision making. Process refers to the steps involved in decision making. Context refers to factors affecting the process, focusing in this paper on social networks. The extended choice framework includes more behavioral richness through the explicit representation of the planning process preceding an action and its dynamics and the effects of context (family, friends, and market) on the process leading to a choice, as well as the inclusion of new types of subjective data in choice models. We discuss the key issues involved in applying the extended framework, focusing on richer data requirements, theories, and models, and present three partial demonstrations of the proposed framework. Future research challenges include the development of more comprehensive empirical tests of the extended modeling framework.     

Combining revealed and stated preferences data

Our objective is to develop a unifying framework for the incorporation of different types of survey data in individual choice models. We describe statistical methodologies that combine multiple sources of data in the estimation of individual choice models and summarize the current state of the art of data combination methods that have been used with market research data. The most successful applications so far have combined revealed and stated preference data. We discuss different types of market and survey data and provide examples of research contexts in which one might wish to combine them. Although these methods show a great deal of promise and have already been used successfully in a number of applications, several important research issues remain. A discussion of these issues and directions for further research conclude the paper.     

The Impact of Online and Offline Information Sources on Automobile Choice Behavior

The Internet has significantly impacted the information search behavior of consumers. Many consumers regularly consult Internet sources for information on product categories, brands, manufacturers, and retailers, particularly when making a purchase decision about major durable goods. Automobiles are one example of such goods. The Internet has become a major source for information on automobile brands, attributes, and dealers. While much research has been done on the impact of the Internet on automobile information search behavior and search costs, there is limited work on the relationship between Internet use and the ultimate automobile choice. This type of relationship may have interesting managerial implications for both manufacturers of automobiles and firms that provide information on automobiles. This paper attempts to address this gap. It examines whether Internet use is associated with different choice patterns for automobiles. Using discrete choice analysis on automobile choice data, we explore whether there will be differences in the salience of specific information types for online versus offline consumers. We find that Internet users rely more on ratings while non-Internet users rely more on recommendations when making automobile choices. Our findings have several useful managerial implications for information provision, both online and offline.     

Decision neuroscience and consumer decision making

This article proposes that neuroscience can shape future theory and models in consumer decision making and suggests ways that neuroscience methods can be used in decision-making research. The article argues that neuroscience facilitates better theory development and empirical testing by considering the physiological context and the role of constructs such as hunger, stress, and social influence on consumer choice and preferences. Neuroscience can also provide new explanations for different sources of heterogeneity within and across populations, suggest novel hypotheses with respect to choices and underlying mechanisms that accord with an understanding of biology, and allow for the use of neural data to make better predictions about consumer behavior. The article suggests that despite some challenges associated with incorporating neuroscience into research on consumer decision processes, the use of neuroscience paradigms will produce a deeper understanding of decision making that can lead to the development of more effective decision aids and interventions.     

Multiple-Category Decision-Making: Review and Synthesis

In many purchase environments, consumers use information from a number of product categories prior to making a decision. These purchase situations create dependencies in choice outcomes across categories. As such, these decision problems cannot be easily modeled using the single-category, single-choice paradigm commonly used by researchers in marketing. We outline a conceptual framework for categorization, and then discuss three types of cross-category dependence: cross-category consideration cross-category learning, and product bundling. We argue that the key to modeling choice dependence across categories is knowledge of the goals driving consumer behavior.     

Business curriculum redesign: Integrating data analytics

Data analytics is an integral part of planning and decision making in business. Priorities have shifted to hiring skilled employees to support a company’s analytics requirements. The authors discuss the background of big data and data analytics, demand for trained professionals, and information on the development of a data analytics curriculum. Curriculum design models are explored and emphasis placed on university curriculum redesign. This is a perspective piece that also addresses interdisciplinary collaboration, accreditation, and related challenges.     

Different strokes for different folks: A method to accommodate decision -making heterogeneity

Consumer decision making is complex and no single perspective offers a complete theory of consumer decision making. While the research community acknowledges that there is heterogeneity, homogenous choice models dominate consumer decision research. This paper provides insights from one method that was designed to accommodate decision -making heterogeneity. Computer process tracing methods can be used to observe different consumer decisions in one product category to understand what and how people choose. More than two-hundred and fifty decisions were observed in this research. Consumers were asked to select one of nine air conditioner alternatives described with six salient attributes. The research findings clearly reveal consumer differences. Specifically, the attributes and decision types used differed resulting in different product choices. This paper reveals how methods that accommodate decision -making heterogeneity can be used by retailers to inform product ranging decisions for categories.     

Sequential sampling models of choice: Some recent advances

Choice models in marketing and economics are generally derived without specifying the underlying cognitive process of decision making. This approach has been successfully used to predict choice behavior. However, it has not much to say about such aspects of decision making as deliberation, attention, conflict, and cognitive limitations and how these influence choices. In contrast, sequential sampling models developed in cognitive psychology explain observed choices based on assumptions about cognitive processes that return the observed choice as the terminal state. We illustrate three advantages of this perspective. First, making explicit assumptions about underlying cognitive processes results in measures of deliberation, attention, conflict, and cognitive limitation. Second, the mathematical representations of underlying cognitive processes imply well documented departures from Luce’s Choice Axiom such as the similarity, compromise, and attraction effects. Third, the process perspective predicts response time and thus allows for inference based on observed choices and response times. Finally, we briefly discuss the relationship between these cognitive models and rules for statistically optimal decisions in sequential designs.     

Integrating Analytics Through the Big Data Information Chain: A Case From Supply Chain Management

The benefits of good decision making by a distributor often have broad leverage across a supply chain, and data science provides a comprehensive framework for making this possible. We present a case study of an ongoing partnership between the authors and corporate managers at a distributor of heating, ventilating, and air‐conditioning products. We describe in detail the “vertical integration” of our analytical tools through a long chain of data scientific activities, backward to raw data, and forward to visually appealing output, in an organization with legacy information technology infrastructure. The models are applied to a large‐scale data set, and spreadsheet‐based decision support tools that include useful visualization capabilities for the firm are illustrated. We also offer this case as a blueprint for building a collaborative research relationship between academia and industry.     

Finding the True Voice of the Customer in CPG Supply Chains: Shopper‐Centric Supply Chain Management

Demand and supply integration is the subject of increasing scholarly attention. The theoretical emphasis on combining market and supply chain data as the basis for strategic and operational decision making is particularly relevant in the context of Consumer Packaged Goods (CPG) supply chains, and offers the basis for advancing our understanding and knowledge in this field. Point‐of‐sale (POS) data are commonly used as the demand signal in CPG supply chains. Using empirical data, this research demonstrates that POS data can be influenced by nondemand factors. We present a number of issues raised by this finding.     

Choice and the Internet: From Clickstream to Research Stream

The authors discuss research progress and future opportunities for modeling consumer choice on the Internet using clickstream data (the electronic records of Internet usage recorded by company web servers and syndicated data services). The authors compare the nature of Internet choice (as captured by clickstream data) with supermarket choice (as captured by UPC scanner panel data), highlighting the differences relevant to choice modelers. Though the application of choice models to clickstream data is relatively new, the authors review existing early work and provide a two-by-two categorization of the applications studied to date (delineating search versus purchase on the one hand and within-site versus across-site choices on the other). The paper offers directions for further research in these areas and discusses additional opportunities afforded by clickstream information, including personalization, data mining, automation, and customer valuation. Notwithstanding the numerous challenges associated with clickstream data research, the authors conclude that the detailed nature of the information tracked about Internet usage and e-commerce transactions presents an enormous opportunity for empirical modelers to enhance the understanding and prediction of choice behavior.     

The interrelationships between brand and channel choice

We propose a framework for the joint study of the consumer’s decision of where to buy and what to buy. The framework is rooted in utility theory where the utility is for a particular channel/brand combination. The framework contains firm actions, the consumer search process, the choice process, and consumer learning. We develop research questions within each of these areas. We then discuss methodological issues pertaining to the use of experimentation and econometrics. Our framework suggests that brand and channel choices are closely intertwined, and therefore studying them jointly will reveal a deeper understanding of consumer decision making in the modern marketing environment.     

The dual role of informational social influence: Implications for marketing management

Evidence is presented from a laboratory study to support the seeking of both recommendations and attribute-value information from information sources during purchase decisions. Emphasis on recommendation information is associated with a broader view of consumer decision making including the subcontracting of brand choice to others and a hybrid decision making strategy under conditions of high perceived risk and high time cost in search. The shift in choice strategy is accompanied by a decrease in the extent of attribute-value information search, a finding which is the opposite of that expected on the basis of previous research and theory in the area. Managerial implications of these choice strategies and associated information use are discussed.     

Discounting and Its Impact on Durables Buying Decisions

The phenomenon of discounting is well established in the economics literature. However, this research has traditionally assumed that households have a single discount rate and make all intertemporal tradeoffs based on that rate. More current research in decision making and marketing has challenged the economics approach to intertemporal choice behavior. In this article, we propose two aspects of discounting research that are particularly relevant to consumer behavior toward durable goods. We propose first that consumers have individual discount rates for product categories. The second proposition is that, in a multiattribute modeling context, consumers have different discount rates for different attributes. We also discuss the strategic implications of these discount phenomena for marketing managers.     

Assumptions in Decision Making Scholarship: Implications for Business Ethics Research

While decision making scholarship in management has specifically addressed the objectivist assumptions within the rational choice model, a similar move within business ethics has only begun to occur. Business ethics scholarship remains primarily based on rational choice assumptions. In this article, we examine the managerial decision making literature in order to illustrate equivocality within the rational choice model. We identify four key assumptions in the decision making literature and illustrate how these assumptions affect decision making theory, research, and practice within the purview of business ethics. Given the breadth of disciplines and approaches within management decision making scholarship, a content analysis of management decision making scholarship produces a greater range of assumptions with finer granularity than similar scholarship within business ethics. By identifying the core assumptions within decision making scholarship, we start a conversation about why, how, and to what effect we make assumptions about decision making in business ethics theory, research, and practice. Examining the range of possible assumptions underlying current scholarship will hopefully clarify the conversation and provide a platform for future business ethics research.     

Using stated response choice data to enrich revealed preference discrete choice models

There is growing interest in exploring the view that both revealed preference (RP) and stated preference (SP) data have useful information and that their integration will enrich the overall explanatory power of RP choice models. These two types of data have been independently used in the estimation of a wide variety of discrete choice applications in marketing. In order to combine the two data sources, each with independent choice outcomes, allowance must be made for their different scaling properties. The approach uses a full information maximum likelihood estimation procedure of the hierarchical logit form to obtain suitable scaling parameters to make one or more data sets comparable. We illustrate the advantages of the dual data strategy by comparing the results with those obtained from models estimated independently with RP and SP data. Data collected as part of a study of high speed rail is used to estimate a set of illustrative mode choice models.     

Hybrid Choice Models: Progress and Challenges

We discuss the development of predictive choice models that go beyond the random utility model in its narrowest formulation. Such approaches incorporate several elements of cognitive process that have been identified as important to the choice process, including strong dependence on history and context, perception formation, and latent constraints. A flexible and practical hybrid choice model is presented that integrates many types of discrete choice modeling methods, draws on different types of data, and allows for flexible disturbances and explicit modeling of latent psychological explanatory variables, heterogeneity, and latent segmentation. Both progress and challenges related to the development of the hybrid choice model are presented.     

Multi‐dimensional search: choosing the right path

This article seeks to analyse processes of decision‐making where multiple costly observations can be made about individual decision outcomes. It presents a simple model of a decision in which information is collected sequentially. Most economic models of sequential collection of costly information have focused on price‐search. It is argued that this is a very specialized type of search. This has led such models to focus on the issue of when search should stop. The article attempts to show how the principles of sequential search can be applied to other types of problems, such as innovation, where the choice of path is also of central concern.