Volume 7, Issue 1

Survival determinants can influence important strategic decisions. Analysis of longitudinal data on 418 online retailers in fourteen Bizrate product categories yields insights into key survival determinants. Survival tends to be easier for introductory versus growth stage entrants. Survival, however, is more difficult for e-tailers who sell touch and feel products and for pure e-tailers who do not have the support of either brick and mortar stores or catalog operations. While introductory stage entrants have higher survival rates versus growth stage entrants, there is no significant impact for rank order of entry or pioneer leadtime. These mixed results point to modest early mover advantages in online retailing. While some e-tailers like Amazon.com benefited from moving early, many others suffered from pioneer burnout.

Regression analysis with time series data is frequently used in marketing research. However, despite its popularity and ease of interpretation it cannot provide any information regarding the relations between marketing time series over different frequencies. This article proposes a new research tool, wavelet analysis, that when incorporated in regression analysis can provide some frequency domain insights about the effectiveness of marketing instruments over different cycles. In addition, by adopting appropriate regression-modeling techniques, wavelets can provide increased estimation and prediction accuracy of marketing causal effects.

This paper explores the link between quality, cost and concentration. Using concentration and cost data and product quality indicators for 2,244 products in over eighty industries in 1997 and 2002 in the US, a two-stage, ordered probit, random effects estimation explores the impact of concentration and cost on quality. The results demonstrate that overall market concentration and high fixed costs are both positively correlated with product quality across most industries. Generally, when either industry concentration or industry fixed costs increase, the likelihood of the product being higher quality increases and the likelihood of observing a lower quality decreases. Further, the authors confirm that prices are a good signal of product quality. A theoretical model is also derived.

This paper examines the effect of privacy concerns on purchase likelihood. In many consumer-seller relationships, consumers are required to provide personal information to marketers in order to get better service. The law requires that marketer provide a statement of their privacy policies to consumers. Secondary disclosure is a key aspect of privacy policies. Consumers are concerned about giving up privacy, but the impact of such concerns on purchase behavior is unclear. By integrating several marketer-controlled variables like price, product, and channels with a marketer’s secondary disclosure policy, the study measures the relative importance of the latter along with the effects of secondary disclosure and internet attitudes. The results show that secondary disclosure attitudes influence the weight of secondary disclosure policies in purchase situations. The results also indicate that price sensitivity is influenced by secondary disclosure policies. The clear implications are that privacy concerns do matter and that privacy policies and marketing strategies cannot be set in isolation of each other. The findings are discussed in relation to prior research on privacy and future research directions. Statistical techniques used in the paper include structural equation models, conjoint analysis, and simultaneous equation regression.

In the online world, publishers place ads from advertisers adjacent to internet search results for a given keyword. To sell such advertising, web publishers auction multiple ad slots using a generalized second-price auction. In this paper, we compare two auction policies that publishers can use to determine the rank and payments of bidding advertisers. The first policy, the highest bid policy, ranks ads based on the bids submitted while the second policy, the highest profit policy, ranks ads based on the expected profit generated to the publisher. Interestingly, we find that the highest profit policy may generate lower publisher profits per keyword even though it uses more information. Subsequently, we use data from a search engine and empirically establish that the correlation between valuations and click through rates are positive, an important assumption in our theoretical model. This finding provides significant support for the theoretical results.