E-Sales Recruitment

  • Rajiv Khosla
  • Ernesto Damiani
  • William Grosky


The Internet has become a major driving force behind the development of computer based human resource management systems. This chapter describes e-business analysis, design and implementation of e-Sales Recruitment System (e-SRS) for a recruitment company. It illustrates the application of activity-centered e-business analysis component, problem solving ontology component and transformation agent component of the human-centered e-business system development framework. We begin this chapter with a brief description of human resource management e-business systems. It is followed by a brief discussion of motivation for using information technology in the area of sales recruitment, then followed by a detailed e-business analysis of the sales recruitment activity using the activity-centered e-business analysis component of the human-centered e-business system development framework. Finally, the e-business design of the e-SRS is described based on two alternative approaches. The first approach involves integration of a psychology based selling behavior model of artificial intelligence techniques like rule based expert systems. The selling behavior profiling and benchmarking results are outlined based on the artificial intelligence approach. The alternative approach is an adaptive approach, which involves integration of the selling behavior model with soft computing methods like fuzzy k-means clustering. In this incremental learning approach, the behavioral patterns are mined into meaningful selling behavior category clusters.


Human Resource Management Behavioral Category Behavior Categorization Recruitment Activity Behavior Profile 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bezdek, J.C. ‘Pattern Recognition with Fuzzy Objective Function Algorithms,’ Advanced Applications in Pattern Recognition, Plenum Press 1981, USAMATHCrossRefGoogle Scholar
  2. O’Brien, J., An Internetworked e-Business Enterprise, McGraw Hill Publishers, 11th Edition, USA, 2002.Google Scholar
  3. Khosla, R., and Dillon, T., ‘An Intelligent Assistant for Improving Sales/Customer Service Performance’ — in IEEE Workshop on Customer Service and Support, San Jose, California, U.S.A, July 1992Google Scholar
  4. Khosla, R. and Dillon, T.,‘A Knowledge Based Approach for Recruiting Salespersons’, Sixth Artificial Intelligence Technology Transfer Conference in Industry and Business, Monterrey, Mexico, Sept. 1993, pp.83–9Google Scholar
  5. Khosla, R., Dillon, T., and Parhar, A., ‘Synthesis of Knowledge Based Methodology and Psychology for Recruitment and Training of Salespersons’, in Lecture Notes in Computer Science (LNCS), Springer-Verlag, 18th German Annual Conference on Artificial Intelligence, Saarbr”ucken, Germany, September 1994Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Rajiv Khosla
    • 1
  • Ernesto Damiani
    • 2
  • William Grosky
    • 3
  1. 1.La Trobe UniversityAustralia
  2. 2.Universita di MilanoItaly
  3. 3.University of MichiganUSA

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