Simon J. Blanchard

Biased Predecisional Processing of Leading and Nonleading Alternatives

When people obtain information about choice alternatives in a set one attribute at a time, they rapidly identify a leading alternative. Although previous research has established that people then distort incoming information, it is unclear whether distortion occurs through favoring of the leading alternative, disfavoring of the trailing alternative, or both. Prior examinations have not explored the predecisional treatment of the nonleading alternative (or alternatives) because they conceptualized distortion as a singular construct in binary choice and measured it using a relative item comparing the evaluation of both alternatives simultaneously. In this article, we introduce a measure of distortion at the level of the alternative, which allows for measuring whether predecisional distortion favors or disfavors every alternative being considered in choice sets of various sizes. We report that both proleader and antitrailer distortion occur and that the use of antitrailer processing differs between binary choices and multiple-options choices.

A model for clustering data from heterogeneous dissimilarities

Clustering algorithms partition a set of n objects into p groups (called clusters), such that objects assigned to the same groups are homogeneous according to some criteria. To derive these clusters, the data input required is often a single n × n dissimilarity matrix. Yet for many applications, more than one instance of the dissimilarity matrix is available and so to conform to model requirements, it is common practice to aggregate (e.g., sum up, average) the matrices. This aggregation practice results in clustering solutions that mask the true nature of the original data. In this paper we introduce a clustering model which, to handle the heterogeneity, uses all available dissimilarity matrices and identifies for groups of individuals clustering objects in a similar way. The model is a nonconvex problem and difficult to solve exactly, and we thus introduce a Variable Neighborhood Search heuristic to provide solutions efficiently. Computational experiments and an empirical application to perception of chocolate candy show that the heuristic algorithm is efficient and that the proposed model is suited for recovering heterogeneous data. Implications for clustering researchers are discussed.

A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification.

We introduce a new statistical procedure for the identification of unobserved categories that vary between individuals and in which objects may span multiple categories. This procedure can be used to analyze data from a proposed sorting task in which individuals may simultaneously assign objects to multiple piles. The results of a synthetic example and a consumer psychology study involving categories of restaurant brands illustrate how the application of the proposed methodology to the new sorting task can account for a variety of categorization phenomena including multiple category memberships and for heterogeneity through individual differences in the saliency of latent category structures.

Extracting Summary Piles from Sorting Task Data

In a sorting task, consumers receive a set of representational items (e.g., products, brands) and sort them into piles such that the items in each pile “go together.” The sorting task is flexible in accommodating different instructions and has been used for decades in exploratory marketing research in brand positioning and categorization. However, no general analytic procedures yet exist for analyzing sorting task data without performing arbitrary transformations to the data that influence the results and insights obtained. This manuscript introduces a flexible framework for analyzing sorting task data, as well as a new optimization approach to identify summary piles, which provide an easy way to explore associations consumers make among a set of items. Using two Monte Carlo simulations and an empirical application of single-serving snacks from a local retailer, the authors demonstrate that the resulting procedure is scalable, can provide additional insights beyond those offered by existing procedures, and requires mere minutes of computational time.

Exploring Intra‐Industry Competitive Heterogeneity: The Identification of Latent Competitive Groups

Purpose – The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain such performance results.Design/methodology/approach – To do so, a restricted latent structure finite mixture model is devised that can quantify the contribution of these three potential sources of heterogeneity in the formulation of latent competitive groups within an industry. The paper illustrate this modeling framework with respect to COMPUSTAT strategy and performance data collected for public banks in the USA.Findings – The paper shows how traditional conceptualizations via strategic or performance groups are inadequate to fully represent intra‐industry heterogeneity.Originality/value – This research paper proposes a new class of restricted finite mixture‐based models, which fit a variety of alternative forms/...

The Budget Contraction Effect: How Contracting Budgets Lead to Less Varied Choice

How do consumers adjust their spending when their budget changes? A common view is that the allocation of ones current budget should not depend on previous budget allocations. Contrary to this, the authors find that when the budget contracts to a particular level, consumers select less variety (as measured by the number of different items with some of the budget allocated to them) than when their budget expands to that same level. This budget contraction effect stems from a reduction in variety under the contracting budget, not from variety expansion under the expanding budget. Evidence from five experiments indicates that the effect is driven by a desire to avoid feelings of loss associated with spreading allocation cuts (relative to reference quantities from previous allocations) across many items.

Specialist Competitor Referrals: How Salespeople Can Use Competitor Referrals for Nonfocal Products to Increase Focal Product Sales

Intuition suggests that a salesperson should not refer consumers to a competitor for products that they both sell. However, myriad examples reveal salespeople doing just that. The authors study specialist competitor referrals, a sales strategy by which one increases consumers’ purchase likelihood of a focal product (e.g., a painting at an art gallery) by (1) referring consumers to a competitor (e.g., a frame warehouse store) that offers a nonfocal product (e.g., a frame) at a lower price, while (2) stating that the stores differ in their specializations (i.e., the stores concentrate their efforts on different goods). Using a study and survey with salespeople, experimental studies, an incentivized negotiation experiment, and a field study, the authors show that specialist competitor referrals can indeed benefit sellers. Specifically, they build on equity theory to show that specialist competitor referrals increase focal product sales by reducing consumers’ perceived overpayment risk for the focal product via increasing perceived equity in the exchange. The authors also show that competitor referrals for nonfocal products that do not justify the price difference on the nonfocal product are ineffective.