Timothy B. Heath

Asymmetric Decoy Effects on Lower-Quality Versus Higher-Quality Brands: Meta-Analytic and Experimental Evidence

Prior research demonstrates that adding decoys to choice sets can increase choice shares of brands similar to decoys while reducing shares of brands dissimilar to decoys. Such effects have been dubbed attraction effects and violate the principles of independence of irrelevant alternatives (IIA) and regularity. We report a metaanalysis that, in addition to revealing heretofore unsupported range effects, demonstrates an effect of brand quality Decoys reduce shares of lower-quality competitors more than they reduce shares of higher-quality competitors. Moreover, whereas IIA is violated throughout, regularity is violated only when higher-quality brands are targeted. Decoys increase shares of higher-quality brands but typically do not increase shares of lower-quality brands. To assess the generalizability of the meta-analytic pattern, we tested decoy effects on two distinct populations in a large experiment The more traditional population replicated the meta-analytic pattern (standard asymmetry) while the more nontraditional population reversed it. These findings suggest that while the standard asymmetry is replicable, it may not generalize to all market segments.

Structural Equations Modeling

I would like to hear comments from more experienced experimental researchers about standard practices for recruiting and compensating participants in consumer and marketing experiments. What are the pros and cons of using student participants? (I know there was a debate about this in the literature a few years ago, but what is the current prevailing opinion?) Is there a difference between using undergraduate students (business majors or nonbusiness majors) and graduate students? When using student participants, is it better to compensate them with extra course credit or to pay them? And, is one time of the semester or quarter (i.e., beginning, middle, or end) preferable for using student participants? I am especially interested to know if anyone has conducted a systematic study of these last two issues. I have recently run experiments using student samples from the same population, but paying one sample and giving extra credit to the other, which definitely affected the rate at which students showed up for their assigned sessions. It may also have affected the variance in the quality of students that chose to participate. Also, in an experiment that I recently ran at the end of a semester (during the last week and a half of class meetings before the final exam week), I collected informal statements from participants in debriefing sessions that indicated that they were no busier or more distracted than they would have been in the middle of the semester. Also, what are the standard practices for recruiting and compensating nonstudent participants (e.g., ordinary folks off the street)? And, for experimental marketing and organizational research (on which I am presently embarking), what are the equivalent standards for industry-based samples (i.e., executives, managers, executive MBA students)? (This information is critical for budgeting grant proposals. I recently called the National Science Foundation and they could not offer much help on this point.) Also, does anyone have any great suggestions for increasing our success rate for getting such populations to participate in experimental research? I was discouraged by a recent conversation with George Day and David Montgomery, who said that even they are finding it increasingly difficult to recruit managerial research participants in the executive courses at the Wharton School and Stanford University. (So, where does that leave the rest of us?) Professor Prashant Malaviya University of Illinois at ChicagoIn the spring of 1990, the Hubble Space Telescope was put into orbit, the culmination of work by a multitude of astronomers, engineers, technicians, and researchers over a period of many years. Its proponents hail it as a key tool to understanding the universe, while its critics write it off as a monumental waste of resources that will never fulfill the expectations of those who designed it. Almost immediately after it went on-line, concern arose about the robustness of its inner workings, yet the demand for access to this device is immense.This special issue poses anonymous questions, then provides the answers and a discussion of the issues by the expert who responded. The answers are not anonymous--partly to give credit to the experts and partly to encourage future communication and debate on whatever lingering controversies may arise. After a number of questions, the special issue concludes with a discussion by the guest editor that summarizes the answers and provides straightforward answers to questions that were not addressed by the experts.

The differential processing of price in gains and losses: the effects of frame and need for cognition

Perhaps the most fundamental principle of decision theory is that more money is preferred to less: the principle of desired wealth. Based on this and other principles such as reference dependence and loss aversion, researchers have derived and demonstrated mental accounting (MA) rules for multiple outcome situations. Experiment 1 tested the invariance of the desired wealth principle and two mental accounting rules (mixed gain, e.g.

New Brands Versus Brand Extensions, Attitudes Versus Choice: Experimental Evidence for Theory and Practice

100 gain and a

Spokesperson Fame and Vividness Effects in the Context of Issue-Relevant Thinking: The Moderating Role of Competitive Setting

50 loss; mixed loss, e.g.

Asymmetric Competition in Choice and the Leveraging of Competitive Disadvantages

100 loss and a

The Asymmetric Effects of Extending Brands to Lower and Higher Quality

50 gain) across types of decision maker and frame. The desired wealth principle and the MA rule for mixed gains were found to vary depending upon (1) the thoughtfulness of the decision maker (need for cognition, NC), and (2) the frame used to describe gains and losses (e.g. a gain of

A comparative study of market share models using disaggregate data

x versus a gain of y%). The MA rule for mixed losses, however, was found to be immune to framing effects, even among people who are generally less thoughtful. The differential processing of gains and losses across frames (dollar versus percentage) and individuals (less versus more thoughtful) was tested further in Experiment 2 where evaluations of mixed losses were made at the level of the gestalt as well as the constituent (the gain and the loss being evaluated separately). Framing effects were evidenced only among subjects lower in NC and only when the constituent gain was evaluated. Evaluations of the overall mixed loss and the constituent loss were comparable across situation and individual, suggesting that losses motivate greater processing among people otherwise inclined toward cognitive miserliness.

The Reconciliation of Humanism and Positivism in the Practice of Consumer Research: A View from the Trenches

The current study compares better-fitting and worse-fitting new brand names and brand extensions on brand attitudes and choice shares across situations that differ in terms of the amount of product information available and consumer knowledge of the target product category (which had limited effects), 35[emsp4 ]mm cameras (choice-set competitors Nikon and Minolta). While brand extensions and better-fitting brands generally enjoyed more positive brand attitudes and larger choice shares, effects were moderated by product information. When information was limited to brand name and price, the better-fitting brand extension (Sony) commanded more share than did the better-fitting new brand (Optix) which in turn commanded more share than did either the worse-fitting extension (Nike) or the worse-fitting new brand (Topix). But when information on product features was added, target brands were chosen similarly across brand names where the better-fitting new brand Optix garnered slightly (non-significantly; 5%) more share than the better-fitting extension Sony. This weak preference was reversed, however, in the attitude data where Sony was rated significantly higher in liking than Optix. Two focal conclusions emerge. First, new brands can perform as well as or better than brand extensions when consumers process product information. In this study, brand-extension advantages were confined to situations of limited information processing and better fit. Second, since branding effects differed across attitudes and choice, researchers hoping to duplicate in the laboratory the types of branding effects likely to occur in the marketplace may want to expand their traditional focus on attitudes to include choice.

When signal swamps substance: the effects of multi-unit discount’s positive and negative cues on sales

Primarily on the basis of research from noncompetitive settings, it is generally believed that nonsubstantive advertising features are ineffective when consumers engage in issue-relevant thinking. Experiment 1 of the current study replicated prior research. Neither spokesperson fame nor the vividness of advertising copy influenced attitudes in noncompetitive settings. However, these same features proved effective in the context of balanced competition. When brands were homogenous (experiment 2) or characterized by large price-quality trade-offs (experiment 3), nonsubstantive features improved attitudes and choice probabilities despite issue-relevant thinking. When trade-offs were small, however, nonsubstantive features were again ineffective. The results suggest that balanced competition can neutralize the effects of substantive features, increase indecision, and promote perceptual contrast. These effects then empower nonsubstantive features to serve as heuristics and/or reduce the risk of postpreference regret.