A nonspatial methodology for the analysis of two-way proximity data incorporating the distance-density hypothesis

This paper presents a nonspatial operationalization of the Krumhansl (1978, 1982) distancedensity model of similarity. This model assumes that the similarity between two objects i and j is a function of both the interpoint distance between i and j and the density of other stimulus points in the regions surrounding i and j . We review this conceptual model and associated empirical evidence for such a specification. A nonspatial, tree-fitting methodology is described which is sufficiently flexible to fit a number of competing hypotheses of similarity formation. A sequential, unconstrained minimization algorithm is technically presented together with various program options. Three applications are provided which demonstrate the flexibility of the methodology. Finally, extensions to spatial models, three-way analyses, and hybrid models are discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45746/1/11336_2005_Article_BF02295285.pd

Advertising, earnings prediction and market value: An analysis of persistent UK advertisers

YesThis paper examines whether major media advertising expenditures help in predicting future earnings. We consider the role of media advertising in firms’ marketing efforts and posit that persistent advertisers are more likely to benefit from advertising activities in creating long‐lived intangible assets. Employing a sample of persistent UK advertisers over the period 1997–2013, we find that advertising expenditures are significantly positively associated with firms’ future earnings and market value. We also report size and sector‐based differences in the association between advertising and firms’ future earnings. Our additional analysis provides support for the arguments that despite the recent rise in digital advertising budgets, traditional advertising media are still effective in positively influencing firms’ performance. Overall, the results of this study are consistent with the view that advertising expenditures produce intangible assets, at least for firms in certain sectors. These findings have implications for marketers in providing evidence of the value generated by firms’ advertising budgets, for investors in validating the relevance of advertising information in influencing future earnings, and for accounting regulators in relation to the provision of useful insights for any future deliberations on financial reporting policies for advertising expenditures

Information processing pattern and propensity to buy: An investigation of online point-of-purchase behavior

The information processing literature provides a wealth of laboratory evidence on the effects that the choice task and individual characteristics have on the extent to which consumers engage in alternative-based versus attribute-based information processing. Less attention has been paid to studying how the processing pattern at the point of purchase is associated with a consumer's propensity to buy in shopping settings. To understand this relationship, we formulate a discrete choice model and perform formal model comparisons to distinguish among several possible dependence structures. We consider models involving an existing measure of information processing, PATTERN; a latent variable version of this measure; and several new refinements and generalizations. Analysis of a unique data set of 895 shoppers on a popular electronics website supports the latent variable specification and provides validation for several hypotheses and modeling components. We find a positive relationship between alternative-based processing and purchase, as well as a tendency of shoppers in the lower price category to engage in alternative-based processing. The results also support the case for joint modeling and estimation. These findings can be useful for future work in information processing and suggest that likely buyers can be identified while engaged in information processing prior to purchase commitment, an important first step in targeting decisions. © 2013 INFORMS

The Right Metrics for Marketing-Mix Decisions

This study addresses the following question: For a given managerial, firm, and industry setting, which individual metrics are effective for making marketing-mix decisions that improve perceived performance outcomes? We articulate the key managerial takeaways based on testing a multi-stage behavioral framework that links decision context, metrics selection, and performance outcomes. Our statistical model adjusts for potential endogeneity bias in estimating metric effectiveness due to selection effects and differs from past literature in that managers can strategically choose metrics based on their ex-ante expected effectiveness. The key findings of our analysis of 439 managers making 1,287 decisions are that customer-mindset marketing metrics such as awareness and willingness to recommend are the most effective metrics for managers to employ while financial metrics such as target volume and net present value are the least effective. However, relative to financial metrics, managers are more uncertain about the ex-ante effectiveness of customer-mindset marketing metrics, which attenuates their use. A second study on 142 managers helps provide detailed underlying rationale for these key results. The implications of metric effectiveness for dashboards and automated decision systems based on machine learning systems are discussed

Evaluation Set Size and Purchase: Evidence from a Product Search Engine

© 2016 The last decade has seen a dramatic increase in the popularity of product search engines, yet the analysis of consumer behavior at such sites remains a challenging research problem despite its timeliness and importance. In this article, we develop and estimate a copula model of evaluation set size and purchase behavior employing data from 3,182 hotel searches by customers at a large travel search engine. The model allows us to jointly study purchase behavior, evaluation sets, and their antecedents. Our results reveal that evaluation set size and purchase are negatively correlated and that factors typically presumed to be associated with purchase—i.e., when users sort search results by price or quality, request many rooms, disclose that there are many guests in their party, or arrive from other search engines and/or partner sites—actually relate to larger evaluation sets but lower purchase probability. In contrast, when users filter the search results, we observe smaller evaluation sets and higher purchase probability. The theoretical background and practical implications of our findings suggest that efforts to increase purchases need not necessarily be predicated on cultivating larger evaluation sets