Clueing in customers

Leonard L. Berry and Neeli Bendapudi When customers lack the expertise to judge a company's offerings, they naturally turn detective, scrutinizing people, facilities, and processes for evidence of quality. The Mayo Clinic understands this and carefully manages that evidence to convey a simple, consistent message: The needs of the patient come first. From the way it hires and trains employees to the way it designs its facilities and approaches its care, the Mayo Clinic provides patients and their families concrete evidence of its strengths and values, an approach that has allowed it to build what is arguably the most powerful brand in health care. Marketing professors Leonard Berry and Neeli Bendapudi conducted a five-month study of evidence management at the Mayo Clinic. They interviewed more than 1,000 patients and employees, observed hundreds of doctor visits, traveled in the Mayo helicopter, and stayed in the organization's many hospitals. Their experiences led them to identify best practices applicable to just about any company, in particular those that sell intangible or technically complex products. Essentially, the authors say, companies need to determine what story they want to tell, then ensure that their employees and facilities consistently show customers evidence of that story. At Mayo, the evidence falls into three categories: people, collaboration, and tangibles. The clinic systematically hires people who espouse its values, and its incentive and reward systems promote collaborative care focused on the patient's needs. The physical environment is explicitly designed for its intended effect on the patient experience. In almost every interaction, an organization's message comes through. "Patients first," the Mayo Clinic's message, is not the only story a medical organization could tell, but the way in which Mayo manages evidence to communicate this message is an example to be followed.     

Neighborhood price externalities of foreclosure rehabilitation: an examination of the Neighborhood Stabilization Program

The federally funded, HUD-administered Neighborhood Stabilization Program (NSP) was enacted in the wake of the financial recession to mitigate the underlying adverse neighborhood effects associated with foreclosed properties. We examined the neighborhood price impacts of NSP-funded foreclosure rehabilitation undertaken by Habitat for Humanity in Dallas County, Texas, using a difference-in-difference framework. Foreclosure rehabilitation projects in Dallas County produced an average 15% increase in neighborhood home prices that sold up to 30 months after the rehabilitated property sale and within 0.1 miles of the rehabilitated property. Foreclosure rehabilitation that involved significant exterior repairs was associated with the largest estimated effect sizes. Results suggest that NSP funding in Dallas County effectively targeted homes that had the potential to have the most severe neighborhood impacts and that rehabilitation was an effective means of reversing neighborhood price externalities associated with blighted foreclosed properties.     

Domestic entry, optimum-welfare and maximum-revenue tariffs

In this paper, we examine the ranking of the maximum-revenue tariff and the optimum-welfare tariff under a linear Cournot oligopoly model without and with free entry of domestic firms. We demonstrate that in a regulated entry oligopoly with asymmetric costs, when the marginal cost of the domestic firms exceeds a critical value, the maximum-revenue tariff is higher than the optimum-welfare tariff. We then show that under free entry of domestic firms with asymmetric costs, when the fixed cost gets larger and the domestic firms become fewer, the difference between the optimum-welfare tariff and the maximum-revenue tariff becomes larger.     

A conceptual framework of anonymity in Group Support Systems

As the development and use of automated systems for collaborative work grows, the need for a better understanding of these systems becomes more important. Our focus is on one type of system, a Group Support System (GSS) and, in particular, on one important aspect of a GSS—anonymity. A conceptual framework for the study of anonymity in a GSS is presented, which describes the general classes of variables and their relationships. These variables include the factors that influence anonymity in a GSS, types of anonymity, and the effects of anonymity on a message sender, receiver, group process, and outcome. Each of these variables is discussed with working propositions presented for important group process and outcome measures. The objectives of this article are to highlight the importance and complexity of anonymity, to act as a guide for empirical investigations of anonymity, and to influence future GSS development and use.