Shailaja Venkatsubramanyan

Streamlining grain supply chains of India: Cloud computing and distributed hubbing for wholesale-retail logistics

Spoilage rates in Indias grain supply chains have been estimated to be 25% to 30%. The price “mark-up” rates, i.e., service charges over crop costs, have been estimated to be over 240%, with a whopping 210% incurred by wholesalers, retailers and the intermediaries. The two rates in developed nations are approximately 3% and between 50% and 100%. This paper focuses on supply-chain portion from wholesalers through consumers and proposes service, information technology and logistics concepts to help reduce the two rates. In particular, cloud computing may help India bring the full benefit of IT to their small merchants through leap-frogging.

The Emerging Challenge of Knowledge Management Ecosystems: A Silicon Valley High Tech Company Signals the Future

A Silicon Valley case study serves as a leading indicator of knowledge management challenges emerging in high-tech knowledge economies. Though a leader in technology innovation, this multi-billion-dollar semiconductor company struggles with traditional knowledge management efforts in technical support owing to the sheer complexity and dynamism of their intellectual property. Add to this the back-end knowledge linkages to suppliers and customer-driven knowledge channels sprouting like weeds and a managed ecosystem perspective emerges. Implications of these emerging phenomena for knowledge management theory and practice are discussed.

Effects of Perceiver / Target Gender and Social Networking Presence on Web-based Impression Formation

As the Web has expanded in its use and utility it has fundamentally changed the way in which individuals gather and use information. This paper suggests that those changes give rise to tangible and significant effects in the impressions people form of others using Web-based information. This study explores the impacts of perceiver gender, target gender, and social networking presence on subjects’ perceptions of potential teammates otherwise unknown to them as revealed by ratings they assign based only on search engine results. Experiments reveal differences in how male and female perceivers view others’ social networking activity in general and suggest that how the perceiver gender matches, or differs, from the gender of the target affects how social networking presence plays into impression formation. Findings hold implications for professionals, academics and individuals concerned with the role that Web-based information plays in impression formation and how inherent gender-based biases may affect power and politics in the workplace and beyond.

Streamlining Knowledge Map Construction for an Online Auction House using Automatic Term Filtering

Organizations are building automated technical support software that can help both consumers and field support engineers with problem resolution. The goal of the automated technical support system is reducing operational cost and increasing customer satisfaction. This paper examines the set of challenges that knowledge engineers face in building automated technical support software. This paper uses a technical services engagement with a major online auction house with tens of millions of users to highlight the challenges and present an automated knowledge map generation technique. The objective of this automated technique was to improve the quality of expressions extracted from documents, which would reduce the burden on knowledge engineers to construct knowledge maps. The technique was run on large corpora of documents in the online auction house and found a significant increase in the quality of the knowledge map. Further experimentation showed that the technique works well for other domains as well.

An Economic Model for Comparing Search Services

Search services are now ubiquitously employed in searching for documents on the Internet and on enterprise intranets. This research develops an economic model for comparing search services based on a user’s information requirement in a decision making scenario. The model considers the noise effects of querying, search and filtering of results. Different search engines might return different results for the same query based on the characteristics of the search engine’s algorithms and the extent of captured data. Users are thus faced with the selection of a search service in order to minimize cost, reduce uncertainty, and maximize the benefits derived for their efforts. A methodology for comparing search services based on the model is presented. This comparison can also be used by search service providers to enhance alignment between their objectives and that of users as well as for pricing of their products and services. A preliminary experiment comparing three popular search services is used to illustrate the model.