A key question for Internet commerce is the nature of competition with traditional brick-and-mortar retailers. Although traditional retailers vastly outsell Internet retailers in most product categories, research on Internet retailing has largely neglected this fundamental dimension of competition. Is cross-channel competition significant, and, if so, how and where can Internet retailers win this battle? This paper attempts to answer these questions using a unique combination of data sets. We collect data on local market structures for traditional retailers, and then match these data to a data set on consumer demand via two direct channels: Internet and catalog. Our analyses show that Internet retailers face significant competition from brick-and-mortar retailers when selling mainstream products, but are virtually immune from competition when selling niche products. Furthermore, since the Internet channel sells proportionately more niche products than the catalog channel, the competition between the Internet channel and local stores is less intense than the competition between the catalog channel and local stores. The methods we introduce can be used to analyze cross-channel competition in other product categories, and suggest that managers need to take into account the types of products they sell when assessing competitive strategies.

Internet retailers have been making significant investments in Web technologies, such as zoom, alternative photos, and color swatch, that are capable of providing detailed product-oriented information and, thereby, mitigating the lack of “touch and feel,” which, in turn, is expected to lower product returns. However, a clear understanding of the relationship between these technologies and product returns is still lacking. Our study attempts to fill this gap by using several econometric models to explore the said relationship. Our unique and rich data set from a women's clothing company allows us to measure technology usage at the product level for each consumer. The results show that, in this context, zoom usage has a negative coefficient, suggesting that a higher use of the zoom technology is associated with fewer returns. Interestingly, we find that a higher use of alternative photos is associated with more returns and, perhaps more importantly, with lower net sales. Color swatch, on the other hand, does not seem to have any effect on returns. Thus, our findings show that different technologies have different effects on product returns. We provide explanations for these findings based on the extant literature. We also conduct a number of tests to ensure the robustness of the results.

Despite the widespread adoption of search and recommendation technologies on the Internet, empirical research that examines the effect of these technologies is scarce. How do online consumers use these technologies? Does consumers' technology usage have an effect on the sales to them or their purchasing patterns? This paper empirically measures consumers' usage of website technologies by analyzing server log data. We match technology usage data to sales data, controlling for consumers' historical purchasing behavior. Our unique data set allows us to reveal the relationship between technology usage and online sales. Our analyses show that consumers' information technology usage has a significant effect on the sales to them, but this effect varies for different technologies and across different products. In particular, the use of directed search has a positive effect on the sales of promoted products, whereas it has a negative effect on the sales of nonpromoted products. In contrast, the use of a recommendation system has a positive effect on the sales of both promoted and nonpromoted products. Surprisingly, the use of nondirected search has an insignificant effect on online sales.electronic commerce, Internet, technology usage, online sales, search, recommendation

Motivation Species tree estimation from genes sampled from throughout the whole genome is complicated due to the gene tree-species tree discordance. Incomplete lineage sorting (ILS) is one of the most frequent causes for this discordance, where alleles can coexist in populations for periods that may span several speciation events. Quartet-based summary methods for estimating species trees from a collection of gene trees are becoming popular due to their high accuracy and statistical guarantee under ILS. Generating quartets with appropriate weights, where weights correspond to the relative importance of quartets, and subsequently amalgamating the weighted quartets to infer a single coherent species tree can allow for a statistically consistent way of estimating species trees. However, handling weighted quartets is challenging. Results We propose wQFM, a highly accurate method for species tree estimation from multi-locus data, by extending the quartet FM (QFM) algorithm to a weighted setting. wQFM was assessed on a collection of simulated and real biological datasets, including the avian phylogenomic dataset which is one of the largest phylogenomic datasets to date. We compared wQFM with wQMC, which is the best alternate method for weighted quartet amalgamation, and with ASTRAL, which is one of the most accurate and widely used coalescent-based species tree estimation methods. Our results suggest that wQFM matches or improves upon the accuracy of wQMC and ASTRAL. Availability wQFM is available in open source form at https://github.com/Mahim1997/wQFM-2020 Supplementary information Supplementary data are available at Bioinformatics online.

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