e-Retailers research

OPIM researcher Ravi Bapna joined with UConn Marketing's Raj Venkatesan and George Mason's Kumar Mehta on research examining: (i) the interplay and validity of search theories from economics in context of information transparency on the internet, (ii) service quality and customer retention from marketing and (iii) risk aversion, loyalty and search behavior. Initial research findings

suggest that retailers who provide higher service quality are able to consistently charge higher prices. Counter to traditional suggestions of competition leading to lowering of prices, they find that, in fact, high service quality retailers increase their prices in the face of growing competition.

An extremely challenging aspect of this study was learning how to successfully integrate and utilize data from multiple sources with different levels of structure and aggregation. This led to development of standardized procedures for: 1) automated identification and collection of relevant and related information from different sources; 2) automated cleansing and transformation of data; 3) collating of information at different levels of aggregation; and 4) identification and marking of potentially problematic data for follow up verification requiring human intervention. These procedures allow for larger scale and scope of data collection, without compromising on quality of data.

The required data captures different facets of the problem domain and is available from different sources. In order to examine the dynamic interaction of theories proposed by different disciplines including economics, psychology, and strategic management, related information from a variety of sources captured different aspects such as: 1) customer perceptions of retailer's service quality, 2) the reach of a retailer's website, and 3) the range of prices charged by different retailers for the same product. Data collected included over 30,000 price quotes from over 300 retailers for more than 2,800 products. This data had to be combined with service quality information for the retailers using web-based information intermediaries. To date, the research outcomes have provided impetus for a larger scale longitudinal study.

Examples of Interdisciplinary Research Questions from Price Dispersion Data

Economics:

  • Computational economic models that enable examination of the dynamics of the marketplace and identification of different types of consumers and retailers;

Statistics:

  • Modeling and application of appropriate hierarchical linear models;

Psychology:

  • Combining structured micro-level activity data with observations and cognitive data obtained from sources such as discussion groups and bulletin boards;

Computer Science:

  • Development of advanced “intelligent” data crawlers;
  • Pattern mining and machine learning utilizing gathered data.

List of articles

Venkatesan, R., K. Mehta, and R. Bapna, "Do Market Characteristics Impact The Relationship Between Retailer Characteristics And Online Prices?,” Under Review at Journal of Retailing

Venkatesan, R., K. Mehta, and R. Bapna, " When the Going Gets Tough the Good Get Going: Understanding the Confluence of Retailer Characteristics, Market Characteristics and Online Pricing Strategies", Under Review at Journal of Retailing