Min Ding, Associate Professor of Marketing
Ding’s research seeks to improve the accuracy of methods used to assess customer preferences for new products. The success of current methods for predicting customer preferences is relatively low. One of the key reasons for this is that potential customers are usually asked questions about their product preferences in hypothetical settings that are unrepresentative of those in which they will make real purchases. In such settings, their decisions have no impact on what they will get in return for participating in a study, and thus they don't have to make serious trade-offs between product attributes, consider alternative uses for their limited funds, or risk that they will make an unsatisfying purchase. The more complex and expensive the product, the more important these issues become. Ding and various coauthors have designed several mechanisms to motivate potential customers to truthfully reveal their preference by offering them a chance to win one of the products that they assess, whereas the specific configuration and price depends on their responses in the preference measurement method. In product categories including digital picture frame, iPod packages, camcorder, and cruise trips, his work shows empirically that such mechanisms substantially improve purchase prediction compared with a standard preference measurement procedure.
Peter Ebbes, Assistant Professor of Marketing, and Zan Huang, Assistant Professor of Supply Chain and Information Systems
Complex social networks have so many connections and nodes that it is nearly impossible to understand their properties and use them intelligently without asking smart questions about them via successive graph sampling techniques. Companies can use networks intelligently to understand how knowledge about products can spread by word of mouth in a population, about recruiting scarce talent, or making business connections. Sampling techniques (e.g., breadth first search, random walk, and ego networks) can lead to identification of correlations between demographics and network characteristics (e.g., income and degree of connectedness), or identification of market segments based on correlations between influence and connectivity. Ebbes and Huang have been working on developing new network sampling techniques that managers can use to quickly understand large complex networks. They use a large network database from “Facebook,” but need a highly skilled programmer to develop a program with which to analyze the data and test their methods. They are also interested in other network databases, such as networks of physicians, or business-to-business networks.
Peter Ebbes, Assistant Professor of Marketing, John Leichty, Associate Professor of Marketing and Statistics, and Wayne DeSarbo, Smeal Chair Distinguished Professor of Marketing
Ebbes and Liechty used a statistical analysis method called "finite mixture modeling" to identify clusters of product attributes (e.g., price, cost, and quality) that are proxies for market segments. These segments are derived inductively from examination of large data sets without making a priori assumptions about customer behavior. Liechty had a large data set from a previous project and used it for the current analysis. Additionally, Ebbes and DeSarbo are interested in working with managers who want to study how market segments evolve over time as a function of "marketing instruments" (e.g., changes in price, promotions, product mix, and shelf management). They can illustrate how their methods improve decision making when companies provide access to data on consumer behavior.
Duncan Fong, Professor of Marketing
Fong and his colleague Ujwal Kayande of The Australian National University are following up on research that they published on product incoherence in 2007. The earlier research theorized about the impact on consumer decision-making of inconsistent or contradictory product attributes (e.g., a high performance car that is also fuel efficient). Unless consumers can resolve the inconsistency or contradiction with a satisfactory explanation (e.g., it's a special category of hybrid cars), they will remain skeptical of advertising claims and choose not to buy the product. If the inconsistency is resolved, then demand should increase. Fong and Kayande plan to empirically test the theory by collecting a set of existing ads that make problematic claims, devising explanations that might resolve the problem, and presenting both to potential buyers. If their explanations resolve the problem, then demand for the product should increase. They also are developing a methodology to detect the relative effect of alternative ways to resolve the inconsistency, thus assisting marketers in improving the likelihood of success for their brands.
Rajdeep Grewal, Professor of Marketing
Grewal and two associates study the effect of service quality on shareholder value in the airline industry. These researchers believe that the level and strength of customer satisfaction with service quality significantly influences this relationship. They test their belief with customer satisfaction data, measuring level by mean satisfaction and strength by satisfaction variability. They measured shareholder value with a well-known index that is derived by dividing a company's market value by the replacement value of its assets. They also distinguished between satisfaction with service occurring early versus late in the service experience (denied boarding versus on-time arrival). Their results show that level and strength of satisfaction significantly influence shareholder value for service experienced early, and satisfaction alone influences shareholder value for service experienced at later stages.
Margaret Meloy, Associate Professor of Marketing, and William Ross, Professor of Marketing
Meloy and Ross are studying how a person's mood may affect ethical decision-making. People are exposed daily to many events that may induce a state of mind that we commonly call a good or bad mood. Meloy and Ross theorize that when people are in a bad mood, they will act in ways to improve their mood, which may include acting more ethically. Similarly, they predict that people who are in a good mood will act in ways to reinforce their good mood. For certain individuals who derive pleasure from manipulating others or beating the system, their choices may run counter to this latter prediction, leading to less ethical choices when in a good mood. Meloy and Ross are inducing student subjects to feel either happy or sad and asking them to make choices when the outcomes involve an ethical or unethical behavior. They are also collecting data on personality attributes and situational characteristics that may affect these choices.
Arvind Rangaswamy, Anchel Professor of Marketing
Rangaswamy's research focuses on how social structure affects the market adoption of new products. His guiding assumption is that consumers listen less to marketers than to others whom they trust more in making their adoption decisions. If so, then marketers need to modify their strategies to accommodate to this behavior. His research program includes four studies. The first study draws upon a database that showed the rate of diffusion of 35 product categories over 50 to 60 years. He develops a model that predicted the rate and pattern of diffusion of these 35 product categories. It is based on the degree of familiarity between potential product adopters and current consumers or at least to people who were thought to know about the products. Thus, family and close friends should be more influential than strangers, and this effect should be stronger in cultures where closeness is more influential. The model explains about 80 percent of the observed rate and pattern of diffusion. Rangaswamy’s second study focuses on software that was targeted for adoption by 600,000 auto dealers. This research assumes that software adoption is influenced by physical proximity; that is, close enough so that the auto dealers have a basis for developing trust (e.g., see each other's lot, go to the same restaurants. etc.). The results show that physical proximity matters. A third study concerns the patterns of calls between customers of a German telecommunications company. Data were available on the frequency of customer calls, the distance between callers and receivers, and the gender of the callers. It was possible from this data to identify the structure and process of diffusion and assess the success of a customer referral program. The fourth study concerns matching types of sampling techniques with different types of population distributions.